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• Volume 7, Issue 2
• Conducting health policy analysis in primary care research: turning clinical ideas into action
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• Alina Engelman 1 ,
• Ben Case 2 ,
• Lisa Meeks 2 and
• http://orcid.org/0000-0001-8521-5681 Michael D Fetters 3
• 1 Health Sciences , California State University, East Bay , Hayward , California , USA
• 2 Department of Family Medicine , University of Michigan , Ann Arbor , Michigan , USA
• 3 Family Medicine , University of Michigan , Ann Arbor , Michigan , USA
• Correspondence to Dr. Alina Engelman; alina.engelman{at}csueastbay.edu

Healthcare guidelines play a prominent role in the day-to-day practice of primary care providers, and health policy research leads to the formation of these guidelines. Health policy research is the multidisciplinary approach to public policy explaining the interaction between health institutions, special interests and theoretical constructs. In this article, we demonstrate how primary care providers can conduct high-impact health policy research using Eugene Bardach’s eightfold policy analysis framework in a primary care context. In a medical case, a woman with a history of total hysterectomy had scheduled a visit for a Papanicolaou (Pap) smear screening test as part of a well-woman health check-up with a family medicine resident. Conflicting recommendations on Pap smear screening after total hysterectomy sparked an investigation using the US Preventive Services Task Force criteria for conducting a health policy analysis. We illustrate broadly how clinical care dilemmas can be examined by using Bardach’s broadly applicable health policy framework in order to inform meaningful policy change. Bardach’s framework includes (1) defining the problem, (2) assembling evidence, (3) constructing alternatives, (4) selecting criteria, (5) projecting outcomes, (6) confronting trade-offs, (7) decision-making and (8) sharing the results of the process. The policy analysis demonstrated insufficient evidence to recommend Pap test screening after hysterectomy and the findings contributed to national recommendations. By following Bardach’s steps, primary care researchers have a feasible and powerful tool for conducting meaningful health policy research and analysis that can influence clinical practice.

• health policy research
• family medicine
• limited resources
• primary health care
• Cervical Cancer

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0

https://doi.org/10.1136/fmch-2018-000076

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Statement of significance

Primary care providers often serve at the front lines where patient care and policy intersect and are uniquely situated to conduct health policy research, illustrated by the example of a clinical encounter that prompted clinical policy analysis of the utility of Papanicolaou (Pap) smear screening after total hysterectomy. Bardach’s 1 policy framework can empower primary care providers to engage in health policy research while meeting competing demands of patient care and advocacy, even with limited resources and in the absence of extensive training and research experience.

Introduction

Health policy often dictates clinical care protocols and helps physicians and other providers make evidence-based decisions about patient care. However, if recommended actions are conflicting, clinically ineffective, cost-prohibitive or result in questionable health improvements, they warrant review. 2 Primary care providers are at the intersection of policy and practice and are naturally positioned to address gaps in healthcare policy and to conduct health policy research. Health policy guides many decisions that clinicians make about patient care in preventive, acute, chronic and end-of-life care. Well-crafted health policy has implications for ensuring timely and accurate guidance for healthcare providers to deliver effective medical care. Primary care providers seeking guidance on screening or intervention for patients may find recommendations that do not comport with their daily clinical experiences. This can prompt them to reassess prevailing policy in specific contexts or with unique populations. As health policies profoundly impact patient care and the overall health of populations, health policy analysis is a critical research tool for primary care providers.

Primary care providers operate as the point of first contact where policy intersects with clinical care, and are positioned optimally for recognising gaps, inconsistencies, or questionable guidelines or health policies ( table 1 ). For this reason, primary care physicians should feel empowered to conduct basic policy analysis, regardless of available resources. Our aim for this article is twofold: (1) to introduce the basics of health policy research explicated in Eugene Bardach’s eightfold policy analysis framework 1 and (2) to illustrate the feasibility of conducting health policy analysis by deconstructing the process step by step. Specifically, we use the example of a clinical encounter that prompted clinical policy analysis of the utility of Papanicolaou (Pap) smear screening after total hysterectomy that ultimately contributed to policy change. While hospital processes or population-based research studies can inform clinical decision-making, doctors facing individual patients often face additional considerations.

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Health policy analysis studies that have been led by primary care and public health researchers

Health policy context

Health policy is a course of action and inaction that affects the institutions, organisations, services, clinical practices and funding arrangements of the health system, and generally falls into two categories: (1) policies that define the functions and powers of agencies, or (2) policies aimed at the protection and promotion of health. 3 4 While government plays a key role in forming policies, private entities, such as insurance companies, as well as primary care physicians and other influential actors, also contribute to new and revised health policies. 4

Health policy analysis

A crucial aspect of health policy formation and change is health policy analysis, a multidisciplinary approach to public policy that aims to explain the interaction among institutions, interests and ideas in the policy process. 5 Often situated as a review of documents and guidelines, health policy analysis is a critical mechanism for ensuring best practices in light of new evidence and the promotion of good health. 6 For example, the growing evidence of disability-related health disparities, including lung cancer screening among the deaf, and case studies on the inclusion of medical residents with disabilities, can drive policy change. 7 8 The Alliance for Disability in Health Care Education, based on existing policy and research literature, crafted a policy document on core competencies on disabilities for healthcare education. 9 Primary care providers may be called on to create clinical policies within their own practices or within their institution, 10 to provide national guidance or highlight the lack of policies or need for guidance. 9–13 Although there is a robust research literature making the case for the importance of policy analysis, there is less guidance on how a practitioner may do so. This overview of health policy analysis procedures is aimed at empowering primary care providers and researchers to identify, improve and prioritise policies that can enhance health policies.

Example of health policy analysis methodology: a study triggered by a clinical encounter

We introduce the example of a clinical encounter that prompted a clinical policy analysis of the utility of Pap smear screening after total hysterectomy and ultimately contributed to policy change. Primary care providers are often the primary professional contact for women seeking cancer screening for breast, cervical or colon cancer, placing primary care providers in a unique position to address policies related to women’s health. A doctor encountered a woman in her 50s who had scheduled a well-woman health examination including a Pap smear in a family medicine clinic. Chart review indicated she had undergone a total hysterectomy for fibroids, a benign disease. After consulting with the preceptor and reading available guidelines, recommendations about cytological testing after hysterectomy were ambiguous. This prompted a health policy analysis on the utility of Pap smears after total hysterectomy for benign disease ( figure 1 ). 14

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Figure defining a healthcare policy problem prompted by the question of whether women who have undergone hysterectomy should undergo Papanicolaou screening for cancer.

The authors began their health policy analysis by examining (1) the recommendations and quality of guidance provided by multiple organisations vested in women’s healthcare and women’s cancers; (2) the existing literature on posthysterectomy Pap smear; and (3) recommendations in associated medical textbooks. They examined the risk of malignancy after total hysterectomy for benign disease and the extent Pap test screening after total hysterectomy for benign disease met the three US Preventive Services Task Force (USPSTF) criteria for an effective test. 15 These criteria include (1) the burden of suffering (does the test accurately identify the disease through screening and do those identified early have better health outcomes?), (2) the accuracy of the screening tool and (3) the effectiveness of early detection. 15 Having illustrated how the authors used health policy analysis according to the USPSTF criteria, in the following we introduce a broad, widely applicable health policy framework by Bardach. 1 Bardach’s framework affords primary care researchers a comprehensive, step-by-step approach for conducting policy analysis. 1

Steps for conducting health policy analysis: Bardach’s eightfold policy analysis framework

Eugene Bardach 1 established an eightfold policy analysis framework that is commonly applied in policy and administration research as well as in public health. Bardach’s eightfold policy analysis framework includes (1) defining the problem, (2) assembling evidence, (3) constructing alternatives, (4) selecting criteria, (5) projecting outcomes, (6) confronting trade-offs, (7) decision-making and (8) sharing the results of the process. In table 2 , we break down Bardach’s eightfold policy analysis framework using the same example to illustrate how Bardach’s more comprehensive approach can similarly illustrate the utility of Pap smears after total hysterectomy for benign disease. 14

Example of health policy analysis to inform improvements in guidelines for Pap smears after total hysterectomy for benign disease through Bardach’s steps 1–8

Step 1. Define the problem

First, in health policy analysis, primary care researchers must define the problem . Defining the problem is critical in policy analysis because it guides the research method and helps establish the structure used in communicating the results. In the exemplar article regarding the utility of Pap smears after total hysterectomy, the authors identify the problem as both a concern that applies to individual patients and as a public problem—one that affects multiple patients, increases healthcare costs, and includes the potential misappropriation of resources without evidence of need or efficacy. 14 The policy scope was narrowed by excluding a history of abnormal Pap testing and of subtotal hysterectomy where the cervix remains intact. Once the background is synthesised, a problem statement can be developed that will allow for improved public health policies. Given the critical importance of developing the problem statement, this message can consider primary care practice and the need for governmental interventions, as well as relevant public and private collaborations.

Step 2. Assemble the evidence

Step 2 involves assembling the evidence by investigating the background, trends, and systematic, institutional, interpersonal or financial barriers and facilitators to successful resolution. The research design and scope of the study will dictate the approach to analysis. In the exemplar study, the authors collected and reviewed existing guidance on Pap smear screening after a total hysterectomy, as well as data from from multiple organisations, the medical literature and textbooks. 14 In reviewing evidence, researchers should explicitly examine their own assumptions and propositions to facilitate a logical process as they compile evidence. Examining assumptions and evidence bases of existing guidelines can often be very informative as well. Once the background is synthesised, the problem statement can be revisited and sharpened to allow for well-defined focus of the policy analysis. As part of defining the problem or assembling evidence, primary care researchers can map a framework through a schematic figure ( figure 1 ). This process permits a comprehensive understanding of the problem to investigate and allows for a more focused literature review on policies, best practices and key barriers as the policy focus evolves.

Step 3. Construct policy alternatives

Step 3 of Bardach’s framework, constructing policy alternatives , guides primary care researchers to consider the advantages and disadvantages of each policy alternative and consider using alternative approaches to policy alone or in combination with other alternatives. Step 3, an explicit description of the assumptions and positions being made for each policy alternative, is critical for successful implementation of the policy. For example, if physicians address a decision choice that requires outside funding, they would be explicit in including funding agencies as part of the consideration of its implementation. Additionally, this discussion will aid the understanding of the evolution of policy and evaluation of the success of policy implementation. In the exemplar case, alternatives would include following existing cervical cancer screening guidelines (despite patients no longer having a cervix), an alternative interval or cessation of the practice.

The challenge to primary care researchers in this step is to adequately consider all the plausible alternative solutions and then pare down alternatives that best meet the needs of the population. This means the physician researcher needs to construct alternatives that are responsive to the highly complex environments in which they are made. To strengthen a policy analysis, physicians should also discuss their own assumptions about where they place the efficacy of the alternatives being constructed and consider the policy implementation.

Step 4. Select the criteria

Selecting the criteria , step 4 in Bardach’s framework, explores how alternatives can be measured and evaluated. This is an essential step to determine the effectiveness of current policy. Criteria can be established based on prior research and can include the feasibility of each alternative given local, epidemiological, political and socioeconomic conditions. Considerations can also include primary health outcomes, cost-effectiveness, feasibility of implementation, acceptability, political feasibility, sustainability and practicality.

Selecting the criteria includes establishing how the physician evaluates each alternative and prioritising each option. In determining how the alternatives are evaluated, physicians can draw from their unique perspectives and backgrounds. This includes considering prior research as well as clinical experiences with patients. These experiences, in turn, can inform considerations of the feasibility of each alternative, in addition to local, epidemiological, political and socioeconomic conditions. In the exemplar, the authors used the widely accepted USPSTF criteria to frame the evaluation. 14

In evaluating the alternatives, physicians can include any clinical or research knowledge on the cost-effectiveness of the alternative. They may establish a scoring system, with low to high, or less favourable to more favourable rankings. This may include categories about the impact on health, the feasibility, and the economic and budgetary impact of the alternative. 14 It is also important to think about the political implications, particularly if the alternative includes legislative or governmental action. In healthcare, this may involve recommendations or policies of disease specialists. Specialty organisation guidelines can be swayed by potential conflicts of interest based on the outcomes of the policy analysis. For example, policy favouring prostate-specific antigen screening for prostate cancer impacts the revenue of urologists. Policy guiding the frequency of mammograms for breast cancer screening impacts the revenue of radiologists. In the exemplar, multiple professional organisation perspectives were considered. When conducting the analysis, two organisations supported screening, two opposed screening and six lacked specific guidelines. 14 Physicians should incorporate considerations of the sustainability and practicality of the alternatives, drawing upon their experience-based expertise.

Step 5. Projecting the outcomes

Often considered the most challenging step, projecting the outcomes is an opportunity to consider how realistic or viable each alternative policy outcome is given resource constraints. In order to project the outcomes, primary care researchers must consider both the direction and the magnitude of the outcome. In the case of Pap smears after a total hysterectomy, it is important to state the positive or negative impact, and to quantify the magnitude of the impact using a point estimate or range. The physician should consider, estimate, project or provide a range for how many cases of cervical cancer are expected to be diagnosed in time for effective treatment as a result of each alternative policy. In the exemplar, the authors weigh the very low incidence of the disease, as well as the costs of false positives inherent in an extremely low prevalence condition. In a situation in which there are resource constraints, it is important to ensure proper balance between unrealistic expectations and cost considerations in light of projected effectiveness. The physician should consider whether simpler or less costly changes in policies and procedures can produce the same or better outcomes. In all projections, the costs of implementing a failed policy and which population would bear those costs need to be measured.

Step 6. Confronting trade-offs

In step 6, confronting trade-offs , the primary care researcher needs to consider the trade-offs between and within each policy alternative. The trade-offs need to be considered in terms of the criteria by which they can be evaluated, and the criteria themselves need to be weighted. In the exemplar, the researchers considered the trade-offs that could occur should women not receive a yearly Pap smear and the evidence or lack thereof for each, including the potential missed opportunity to check the ovaries for ovarian cancer or the potential for decreased breast cancer screening. A policy alternative that challenges an important criterion, such as survival rate, may need to be discarded even if it stacks up very well in regard to a criterion that may be perceived as less important, such as temporary patient discomfort. In essence, primary care researchers must, within and across each alternative, weigh the relative benefit and importance of each criterion, such as the cost savings of eliminating screenings against the relative risks of missed diagnoses.

Step 7. Decision-making

Decision-making is an opportunity for primary care researchers to go through the process of clarifying the costs and benefits in order to present a final decision to stakeholders. This will ensure that, when they explain the costs and benefits, their explanations are clear and the logic behind their choice is sound and easy to follow. In the exemplar study, using the criteria of the USPSTF, the authors concluded that there was insufficient evidence to support Pap smear testing after total hysterectomy for benign disease.

Step 8. Sharing the results of the process

Sharing the results of the process may take the form of a narrative, and primary care researchers need to clearly understand the reasons behind their decision. Most importantly, primary care researchers need to define the audience and ‘pitch’ the story to a target population, keeping in mind both the larger political environment and the story-telling medium. In the exemplar, the results of the analysis were shared with a member of the USPSTF, and the full paper was sent for consideration as part of the USPSTF cervical cancer screening guidelines. Shortly afterwards, another paper demonstrated very low yield of any abnormalities in vaginal cytological smears obtained after total hysterectomy. 16 In contrast to the first edition of the USPSTF guidelines, 15 the second edition of the USPSTF 17 provided a recommendation against Pap smear screening after total hysterectomy for benign disease. An additional analysis published by the authors demonstrated poor cost-effectiveness of testing, and the USPSTF continues to recommend against Pap test after total hysterectomy for benign disease. 18 19 When presenting findings and recommendations, the family physician should consider the audience and tailor the ‘pitch’ accordingly. If hospital administrators are the decision-makers, consider whether an oral presentation or a written policy document is warranted, or whether a combination of the two is best for communicating key points.

Using new media interventions, primary care researchers conducting health policy analysis can share their findings in an impactful way to inform, persuade and motivate their audience. 20 Media interventions are often used to communicate health research to other health professionals, patients and policy makers through social media, including Twitter. Abstracts shared via Twitter infograph can elicit interest in a full report or manuscript and has the potential to effectively share health-related research to policy makers and the public. 21 Depending on the specific topic, writing an op-ed or commentary for local or national professional journals, organisations or newspapers can help to stimulate public interest in the topic and catch the attention of policy makers. Rather than using one media strategy, primary care researchers doing health policy analysis should consider multiple strategies and messages tailored to specific target audiences. Strategies may shift depending on the audience, which can include the public, healthcare providers, grant funding agencies and policy makers.

Conducting a health policy analysis has the potential to change clinical practice at the national and international levels. While the initial step of defining the problem is critical, the lack of guidelines or conflicting guidelines among various organisations remains both a challenge and an invitation to conduct all steps of Bardach’s policy analysis. Yet the process requires a primary care researcher to spend considerable time engaging in all steps, including a review of the academic literature. Reviewing current health policies requires access to the literature and the analytical skills needed to interpret the evidence. After identifying a valid health policy problem, a physician should consider seeking assistance from an established health policy analyst or health economist with previous experience in the topic at hand.

An information sciences specialist could greatly enhance a policy analysis by grounding data in a rich evidence base, if available through academic affiliates. Policy analysis requires a critical eye for identifying guidelines that are not well supported by empirical evidence. For some clinical issues, there may be insufficient information to support changes in practice, even though a provider has identified an important policy question. Fortunately, the Bardach framework provides an approach to this limitation. Primary care researchers with limited infrastructure may need to think creatively about how to disseminate findings and analysis beyond publication in the academic literature, including the use of new media.

Additional resources

In addition to Bardach’s framework, readers may find helpful sources and approaches for policy analysis. 1 The Centers for Disease Control and Prevention policy analytical framework may be helpful prior to policy implementation. 22 Bodenheimer and Grumbach 23 are family physicians and policy analysts who provide a clinical perspective on policy analysis. Teitelbaum and Wilensky 4 offer a perspective on the intersection of health policy and law, including an overview of case law and the ethics of health policy. Dunn 24 provides a resource with a public policy analysis focus.

Conclusions

Primary care providers are often at the front lines where patient care and policy intersect and are uniquely situated to conduct health policy research. The scale of health policy analysis by primary care providers can range from setting clinical policies within their own practices, institutions and organisations, or on a nationwide scale. As illustrated by the featured policy analysis of Pap testing after total hysterectomy for benign disease, meaningful policy research can arise from a single clinical experience for a research-minded family physician. As some health policies and guidelines do not pose a financial conflict of interest, policy framing by primary care providers may provide invaluable balance with regard to recommendations for or against services. Primary care researchers need to recognise that they have a unique power to drive policy change. Due to primary care providers’ front-line expertise, their concerns are widely viewed as practical, can motivate policy research and help attract funding. Bardach’s policy framework can empower primary care providers to engage in health policy research while meeting competing demands of patient care and advocacy, even in the absence of extensive training and research experience.

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Contributors AE developed the conceptual framework, wrote and revised the manuscript. BC contributed to revisions, including references and formatting. LM contributed to manuscript writing and analysis. MDF contributed to manuscript edits.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; internally peer reviewed.

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Current debates in health care policy: A brief overview

Subscribe to the economic studies bulletin, matthew fiedler and matthew fiedler joseph a. pechman senior fellow - economic studies , center on health policy @mattafiedler christen linke young christen linke young deputy assistant to the president for health and veterans affairs - domestic policy council for health and veterans, former fellow - usc-brookings schaeffer initiative for health policy @clinkeyoung.

October 15, 2019

Issues in health care policy fall in two broad categories: those related to health care coverage and those related to the underlying cost of health care. Coverage policy addresses where Americans get health insurance, how it is paid for, and what it covers, while policies related to underlying costs seek to reduce overall health care spending by lowering either the price or utilization of health care.

A Closer Look

Health care is a major issue in American politics, with important debates related to health care coverage and the underlying cost of health care. The role of health care coverage is to insulate people from high health care spending burdens and facilitate access to health care. Policies related to coverage include those affecting how Americans get health insurance, how that insurance is paid for, and what insurance does and does not cover. Debates about how to reduce the number of people without health insurance, whether Americans should continue to get coverage through their jobs, if health insurance deductibles are too high, or how to change the premiums required under federal coverage programs all fall into this category.

Many coverage policies change how much families have to pay for health care, generally by changing what government programs pay on their behalf or by changing how health care spending burdens are shared between people with larger and smaller health care needs. But other proposals aim to reduce the underlying cost of health care, either by reducing how many health care services patients receive or by reducing the prices paid for those services. Policies like these have the potential to reduce overall health care spending throughout the system, but this is often easier said than done.

Policies related to health care coverage

More than 90% of Americans have health insurance. About half get coverage from an employer, and a third get coverage from a government program like Medicare or Medicaid. Another 5% buy coverage on the individual market, while 9% are uninsured. Different policymakers see different problems with the way people get coverage today and, correspondingly, propose different solutions.

Some policymakers believe that current federal programs that provide health care coverage are too generous and inappropriately burden taxpayers. These policymakers often support proposals that would narrow eligibility for or reduce the generosity of those programs, particularly Medicaid and programs that subsidize individual market coverage, even though fewer people would have coverage and some people’s coverage would become less generous. President Trump has supported proposals like these .

Other policymakers are primarily concerned with reducing the number of uninsured or reducing the burdens people face from premiums and cost-sharing. These policymakers often support proposals that would broaden eligibility for existing coverage programs or make those programs more generous, even though it would require additional federal spending. Many Democratic presidential candidates have supported approaches like these . Some proposals focus primarily on people who are currently uninsured or face particularly high health care spending burdens, while others support a program like Medicare for All that would commit a great deal more federal funds and insure all Americans through a single federal program.

Learn more about broad proposals related to health care coverage here . In addition to these broad proposals, some policymakers also support proposals that target specific problems with our existing health insurance system. One example is the fact that people with insurance can sometimes receive large “surprise” bills for health care services, discussed more here .  

Policies related to underlying health care costs

Health care spending is determined by two factors: how many health care services patients receive and the prices paid for each service. While there is broad agreement that some health care services are unnecessary and that the prices of some services are excessive, there is much less agreement about how to address these excesses.

Starting with the volume of services patients receive, the main challenge policymakers face is discouraging delivery of services that provide little health benefit without discouraging delivery of valuable services. One approach is to give health care providers financial incentives to eliminate unnecessary services by paying them based on the overall costs their patients incur rather than the number of services they personally deliver. Reforms like these can reduce utilization, seemingly without harming patients’ health, although total savings have been relatively modest so far.

Another approach is to require consumers to bear more of the cost of care themselves by increasing cost-sharing in hopes that they will become more cost-conscious and forgo low-value services. Research finds that this approach can also reduce service volume, but consumers often cut back on both high-value and low-value services rather than just low-value services. Increasing cost-sharing also reduces the effectiveness of health insurance in protecting against the costs of illness.

Policymakers may also be interested in lowering health care prices. A major cause of excessive prices is that health care provider markets—particularly hospital markets—are concentrated , with relatively few competitors in many parts of the country. In addition, many patients value a broad choice of providers. These and other features of health care markets allow many providers to demand prices from private insurers that substantially exceed providers’ costs of delivering health care services.

Policymakers have some options for addressing high prices. One is to make health care markets more competitive . This may include encouraging new entrants, blocking mergers, and aggressively policing anti-competitive behavior. Another approach is to take advantage of the fact that public insurance programs generally pay much lower prices than private insurers by introducing a “public option” or transitioning to a single payer system. Alternatively, policymakers could lower prices by regulating them directly. No matter how policymakers aim to reduce prices, they will need to balance the savings from lower prices against the risk of driving prices too low and jeopardizing access to or quality of care. Prescription drug prices raise somewhat different issues. In most cases, the main reason drugs are expensive is because the government grants a time-limited monopoly to inventors of new drugs via patents and related policies. That monopoly allows manufacturers to set high prices, with the goal of encouraging development of new drugs. Correspondingly, most approaches to lowering prices boil down to reducing the scope or duration of manufacturers’ monopoly or limiting the prices manufacturers can charge while the monopoly lasts. But, here too, there are tradeoffs: the benefits of lower prices on existing drugs must be weighed against the reduction in incentives to develop new drugs.

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October 23, 2018

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April 13, 2017

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US Health Policy—2020 and Beyond : Introducing a New JAMA Series

• 1 Medicine/Cardiology, Washington University School of Medicine in St Louis, St Louis, Missouri
• 2 JAMA , Chicago, Illinois
• Viewpoint The Implications of “Medicare for All” for US Hospitals Kevin A. Schulman, MD; Arnold Milstein, MD JAMA

Health care is always on the minds of the public, usually ranking among the top 3 concerns. Virtually all of the Democratic presidential candidates have discussed or will shortly detail health care proposals, whereas President Trump and the current administration recently expressed support for repealing the Affordable Care Act. With the presidential election just 18 months away, it is an opportune time to introduce a new health policy series in JAMA .

While various proposals to improve US health care will certainly differ in content, they will all by necessity share a common theme—a focus on reducing health care costs. In 2017, US health care spending reached $3.5 trillion, and such costs now consume approximately 18% of the gross domestic product (GDP). 1 Even though there has been a slight slowing in the annual growth of health care expenditures, 2 a recent projection suggested that by 2027, health care will consume 22% of the GDP, 3 outpacing the annual rate of inflation and increases in GDP over the next 5 years. This is an unsustainable trajectory.

At the same time, there are also crises of access and equity. Recent estimates suggest that nearly 14% of US residents are uninsured, and these numbers are markedly higher among people living in poverty compared with those who are wealthier, as well as among racial and ethnic minority populations compared with white populations. According to the 2017 National Healthcare Quality and Disparities Report, an estimated 40% of adults reported lacking a usual source of care, of which 15% indicated a financial or insurance reason for lacking regular access; these figures are also higher among impoverished persons and individuals of racial or ethnic minority. 4 Quality, though improving overall, remains inequitable as well: substantial differences across a range of quality domains persist for black and Hispanic individuals compared with white individuals.

The key question for policy makers is whether there are achievable health policies that will reduce the annual increase in health care expenditures yet at the same time increase access to care (fewer uninsured or underinsured), improve quality, and reduce inequities. Feasible policies likely must also maintain choice, which the majority of people repeatedly maintain is important to them.

To set the stage for a constructive policy debate, the first step requires defining the current starting point in coverage and spending ( Table ). For its population of 325 million in 2017, the United States spent $3.5 trillion on health care. Private health insurance covered approximately 197 million individuals and accounted for $1.2 trillion in health care spending. Medicare covered approximately 57 million individuals and accounted for approximately $706 billion in expenditures, and Medicaid covered approximately 72 million individuals and accounted for approximately $582 billion in health care spending. 2

These coverage numbers represent a significant shift over the past decade. Medicare has had relatively stable enrollment growth in its core populations of individuals aged 65 years or older and individuals younger than 65 years with end-stage renal disease, amyotrophic lateral sclerosis, or disabilities. However, the proportion of Medicare beneficiaries enrolled in private Medicare plans (ie, Medicare Advantage), which are administered by private insurance companies, has increased to approximately one-third in 2018. 5 Even more marked changes have taken place in Medicaid. Medicaid is a heterogeneous program and covers children, pregnant women, and adults living in poverty or with disabilities. Although children represent approximately 44% of Medicaid recipients (34 million of a total of 72 million), they account for only approximately 19% of the cost. 6 Medicaid has expanded substantially with the passage of the Affordable Care Act, with an increase in the number of individuals covered from approximately 50 million in 2010 to an estimated 76 million by 2020, as additional states have indicated that they will expand Medicaid. 7

Across these payers, how does the United States spend $3.5 trillion in health care dollars? Various estimates are available, but overall, hospitals account for approximately 33% of spending, 1 , 2 physician and clinical services approximately 20%, 1 , 2 and prescription drugs (including retail, ambulatory, and hospital costs) about 18%. 8 Skilled nursing facilities, nursing homes, dental care, home health care, other health and residential care services (such as mental health and substance abuse facilities and ambulance services), and durable and nondurable medical equipment also contribute to the $3.5 trillion, but virtually none of those services or products individually exceed 5% of total expenditures. 1 , 2 An additional important expense involves the cost of medical devices, and with a continued increase in the number of hip and knee replacements each year, and expanding use of devices like transcatheter aortic valves and mitral valve clips, it is likely that the cost of devices, like the costs of drugs, will increase substantially in the coming years.

Because of efforts to reduce costs and improve quality, the past 8 years have seen a number of new initiatives in payment reform. For example, the Centers for Medicare & Medicaid Services has been at the center of a major transition to value-based payment via many programs created or expanded under the Affordable Care Act. These include mandatory hospital-based programs like the Hospital Readmissions Reduction Program, voluntary programs like accountable care organizations and bundled payments, and ambulatory care payment programs like the Merit-based Incentive Payment System. 9 - 12 At the state level, there has been additional experimentation, including global budgeting in Maryland 13 and a rural hospital global payment model in Pennsylvania, 14 among others. Private insurers have also been involved, with major shifts toward value-based care, innovative delivery models, and new experiments in vertical and horizontal integration. Care delivery organizations have consolidated substantially as well. In part because of this complexity, it is difficult to estimate the percentage of the US insured population that receive care under a value-based or alternative payment model, although it is clear that the proportion continues to increase.

Even though the Affordable Care Act and the health care industry in general have been modestly successful at improving coverage, there has been less progress in improving quality or reducing health care costs. Most delivery system reform efforts have been iterative rather than transformative, although it may be too early to assess whether these efforts are at least setting the stage for more major and sustained effective subsequent changes. Nonetheless, even though current health statistics do not necessarily reflect the entire health of a nation, the recent decline in life expectancy, 15 the recent increase in cardiovascular disease deaths and prevalence of cardiovascular disease morbidity and mortality, 16 the ongoing epidemic of opioid-related deaths, 17 and the sustained high prevalence of obesity in the United States, with substantial differences by race, ethnicity, and extent of urbanization, 18 - 20 raise the issue of whether the United States is addressing the health of its population effectively and spending $3.5 trillion wisely.

Many potential solutions have been proposed or may be possible. Some may be market based and some may rely more on regulation; some may prioritize population health and wellness and others may focus on innovation in technology and cures. All will require difficult choices, compromise, and prioritization. Simply spending more on health care will not be an effective approach.

The new JAMA series on health policy will consist of scholarly and evidence-based Viewpoints that will focus on solutions aimed at controlling health care costs, expanding access to care, and improving quality and value, with an emphasis on needed modifications of current health care programs and policies, and analysis of various proposals introduced by governmental agencies and by presidential candidates. In the first article in this series, Schulman and Milstein 21 discuss the implications of proposals that advocate for a “Medicare for all” approach for US health insurance as it would relate to hospitals. The authors explore the potential ramifications of a universal application of Medicare payment rates to hospitals, which currently account for the largest share of US health care spending. As epitomized by this scholarly Viewpoint, the goal of this new series is to ensure robust, enlightened, and meaningful discussion and debate about how health care should be paid for and delivered in the United States—not just for today or in 2020, but importantly, well beyond.

Corresponding Author: Karen E. Joynt Maddox, MD, MPH, Medicine/Cardiology, Washington University School of Medicine in St Louis, 660 S Euclid, St Louis, MO 63110 ( [email protected] ).

Published Online: April 4, 2019. doi:10.1001/jama.2019.3451

Conflict of Interest Disclosures: Dr Joynt Maddox reported contract work for the US Department of Health and Human Services, Office of the Assistant Secretary of Planning and Evaluation. No other disclosures were reported.

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Joynt Maddox KE , Bauchner H , Fontanarosa PB. US Health Policy—2020 and Beyond : Introducing a New JAMA Series . JAMA. 2019;321(17):1670–1672. doi:10.1001/jama.2019.3451

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Key Data on Health and Health Care by Race and Ethnicity

Nambi Ndugga , Latoya Hill , and Samantha Artiga Published: June 11, 2024

Executive Summary

Introduction.

Racial and ethnic disparities in health and health care remain a persistent challenge in the United States. The COVID-19 pandemic’s uneven impact on people of color drew increased attention to inequities in health and health care, which have been documented for decades and reflect longstanding structural and systemic inequities rooted in historical and ongoing racism and discrimination. KFF’s 2023 Survey on Racism, Discrimination, and Health documents ongoing experiences with racism and discrimination, including in health care settings. While inequities in access to and use of health care contribute to disparities in health, inequities across broader social and economic factors that drive health, often referred to as social determinants of health , also play a major role. Using data to identify disparities and the factors that drive them is important for developing interventions and directing resources to address them, as well as for assessing progress toward achieving greater equity over time.

This analysis examines how people of color fare compared to White people across 64 measures of health, health care, and social determinants of health using the most recent data available from federal surveys and administrative sets as well as the 2023 KFF Survey on Racism, Discrimination, and Health , which provides unique nationally-representative measures of adults’ experiences with racism and discrimination, including in health care (see About the Data). Where possible, we present data for six groups: White, Asian, Hispanic, Black, American Indian or Alaska Native (AIAN), and Native Hawaiian or Pacific Islander (NHPI). People of Hispanic origin may be of any race, but we classify them as Hispanic for this analysis. We limit other groups to people who identify as non-Hispanic. When the same or similar measures are available in multiple datasets, we use the data that allow us to disaggregate for the largest number of racial and ethnic groups. Future analyses will reflect new federal standards that will utilize a combined race and ethnicity approach for collecting information and include a new category for people who identify as Middle Eastern or North African. Unless otherwise noted, differences described in the text are statistically significant at the p<0.05 level.

We include data for smaller population groups wherever available. Instances in which the unweighted sample size for a subgroup is less than 50 or the relative standard error is greater than 30% — which are outside of what we would typically include in analysis like this — are noted in the figures, and confidence intervals for those measures are included in the figure. Although these small sample sizes may impact the reliability, validity, and reproducibility of data, they are important to include because they point to potential underlying disparities that are hidden without disaggregated data. For some data measures throughout this brief we refer to “women” but recognize that other individuals also give birth, including some transgender men, nonbinary, and gender-nonconforming persons.

Key Takeaways

Black, Hispanic, and AIAN people fare worse than White people across the majority of examined measures of health and health care and social determinants of health (Figure 1). Black people fare better than White people for some cancer screening and incidence measures, although they have higher rates of cancer mortality. Despite worse measures of health coverage and access and social determinants of health, Hispanic people fare better than White people for some health measures, including life expectancy, some chronic diseases, and most measures of cancer incidence and mortality. These findings may, in part, reflect variation in outcomes among subgroups of Hispanic people , with better outcomes for some groups, particularly recent immigrants to the U.S. Examples of some key findings include:

• Nonelderly AIAN (19%) and Hispanic (18%) people were more than twice as likely as their White counterparts (7%) to be uninsured as of 2022.
• Among adults with any mental illness, Hispanic (40%), Black (38%), and Asian (36%) adults were less likely than White adults (56%) to receive mental health services as of 2022.
• Roughly, six in ten Hispanic (63%), AIAN (63%), and Black (58%) adults went without a flu vaccine in the 2022-2023 season, compared to less than half of White adults (49%).
• AIAN (67.9 years) and Black (72.8 years) people had a shorter life expectancy compared to White people (77.5 years) as of 2022, and AIAN, Hispanic, and Black people experienced larger declines in life expectancy than White people between 2019 and 2022; however, all racial and ethnic groups experienced a small increase in life expectancy between 2021 and 2022.
• Black (10.9 per 1,000) and AIAN (9.1 per 1,000) infants were at least two times as likely to die as White infants (4.5 per 1,000) as of 2022. Black and AIAN women also had the highest rates of pregnancy-related mortality.
• AIAN (24%) and Black (21%) children were more than three times as likely to have food insecurity as White children (6%), and Hispanic children (15%) were over twice as likely to have food insecurity than White children (6%) as of 2022.

Asian people in the aggregate fare the same or better compared to White people for most examined measures. However, they fare worse for some measures, including receipt of some routine care and screening services, and some social determinants of health, including home ownership, crowded housing, and experiences with racism. They also have higher shares of people who are noncitizens or who have limited English proficiency (LEP), which could contribute to barriers to accessing health coverage and care. Moreover, the aggregate data may mask underlying disparities among subgroups of the Asian population. Asian people also report experiences with discrimination in daily life, which is associated with adverse effects on mental health and well-being.

Data gaps largely prevent the ability to identify and understand health disparities for NHPI people. Data are insufficient or not disaggregated for NHPI people for a number of the examined measures. Among available data, NHPI people fare worse than White people for the majority of measures. There are no significant differences for some measures, but this largely reflects the smaller sample size for NHPI people in many datasets, which limits the power to detect statistically significant differences.

These data highlight the importance of continued efforts to address disparities in health and health care and show that it will be key for efforts to address factors both within and beyond the health care system. While these data provide insight into the status of disparities, ongoing data gaps and limitations hamper the ability to get a complete picture, particularly for smaller population groups and among subgroups of the broader racial and ethnic categories. As the share of people who identify as multiracial grows, it will be important to develop improved methods for understanding their experiences. How data are collected and reported by race and ethnicity is important for understanding disparities and efforts to address them. Recent changes to federal standards for collecting and reporting racial and ethnic data are intended to better represent the diversity of the population and will likely support greater disaggregation of data to identify and address disparities.

Racial Diversity Within the U.S. Today

Total population by race and ethnicity.

About four in ten people (42%) in the United States identify as people of color (Figure 2). This group includes 19% who are Hispanic, 12% who are Black, 6% who are Asian, 1% who are AIAN, less than 1% who are NHPI, and 5% who identify as another racial category, including individuals who identify as more than one race. The remaining 58% of the population are White. The share of the population who identify as people of color has been growing over time, with the largest growth occurring among those who identify as Hispanic or Asian. The racial diversity of the population is expected to continue to increase, with people of color projected to account for over half of the population by 2050. Recent changes to how data on race and ethnicity are collected and reported may also influence measures of the diversity of the population.

RACIAL DIVERSITY BY STATE

Certain areas of the country—particularly in the South, Southwest, and parts of the West—are more racially diverse than others (Figure 3). Overall, the share of the population who are people of color ranges from 10% or fewer in Maine, Vermont, and West Virginia to 50% or more of the population in California, District of Columbia, Georgia, Hawaii, Maryland, Nevada, New Mexico, and Texas. Most people of color live in the South and West. More than half (59%) of the Black population resides in the South, and nearly eight in ten Hispanic people live in the West (38%) or South (39%). About three quarters of the NHPI population (75%), almost half (49%) of the AIAN population, and 43% of the Asian population live in the Western region of the country.

TOTAL POPULATION BY AGE, RACE, AND ETHNICITY

People of color are younger compared to White people. Hispanic people are the youngest racial and ethnic group, with 31% ages 18 or younger and 56% below age 35 (Figure 4). Roughly half of Black (48%), AIAN (50%), and NHPI (51%) people are below age 35, compared to 42% of Asian people and 38% of White people.

Health Coverage, Access to and Use of Care

Racial disparities in health coverage, access, and use.

Overall, Hispanic and AIAN people fare worse compared to White people across most examined measures of health coverage, and access to and use of care (Figure 5). Black people fare worse than White people across half of these measures, and experiences for Asian people are mostly similar to or better than White people across these examined measures. NHPI people fare worse than White people across some measures, but several measures lacked sufficient data for a reliable estimate for NHPI people.

HEALTH COVERAGE

Despite gains in health coverage across racial and ethnic groups over time, nonelderly AIAN, Hispanic, NHPI, and Black people remain more likely to be uninsured compared to their White counterparts. After the Affordable Care Act (ACA), Medicaid, and Marketplace coverage expansions took effect in 2014, all racial and ethnic groups experienced large increases in coverage . Beginning in 2017, coverage gains began reversing and the number of uninsured people increased for three consecutive years. However, between 2019 and 2022, there were small gains in coverage across most racial and ethnic groups, with pandemic enrollment protections in Medicaid and enhanced ACA premium subsidies. Despite these gains over time, disparities in health coverage persist as of 2022. Nonelderly AIAN (19%) and Hispanic (18%) people have the highest uninsured rates (Figure 6). Uninsured rates for nonelderly NHPI (13%) and Black (10%) people are also higher than the rate for their White counterparts (7%). Nonelderly White (7%) and Asian (6%) people have the lowest uninsured rates.

ACCESS TO AND USE OF CARE

Most groups of nonelderly adults of color are more likely than nonelderly White adults to report not having a usual doctor or provider and going without care. Roughly one third (36%) of Hispanic adults, one quarter of AIAN (25%) and NHPI (24%) adults, and about one in five (21%) Asian adults report not having a personal health care provider compared to 17% of White adults (Figure 7). The share of Black adults who report not having a personal health care provider is the same as their White counterparts (17% for both). In addition, Hispanic (21%), NHPI (18%), AIAN (16%), and Black (14%) adults are more likely than White adults (11%) to report not seeing a doctor in the past 12 months because of cost, while Asian adults (8%) are less likely than White adults to say they went without a doctor visit due to cost. Hispanic (32%) and AIAN (31%) adults are more likely than White adults (28%) to say they went without a routine checkup in the past year, while Asian (26%), NHPI (24%), and Black (20%) adults are less likely to report going without a checkup. Hispanic and AIAN (both 45%) and Black (40%) adults are more likely than White adults (34%) to report going without a visit to a dentist or dental clinic in the past year.

In contrast to the patterns among adults, racial and ethnic differences in access to and use of care are more mixed for children. Nearly one in ten (9%) Hispanic children lack a usual source of care when sick compared to 5% of White children, but there are no significant differences for other groups for which data are available (Figure 8). Similar shares of Hispanic (7%), Asian (7%), and Black (4%) children went without a health care visit in the past year as White children (6%). However, higher shares of Asian (23%) and Black (21%) children went without a dental visit in the past year compared to White children (17%). Data are not available for NHPI children for these measures, and data for AIAN children should be interpreted with caution due to small sample sizes and large standard errors.

Among adults with any mental illness, Black, Hispanic, and Asian adults are less likely than White adults to report receiving mental health services. Roughly half (56%) of White adults with any mental illness report receiving mental health services in the past year. (Figure 9). In contrast, about four in ten (40%) Hispanic adults and just over a third of Black (38%) and Asian (36%) adults with any mental illness report receiving mental health care in the past year. Data are not available for AIAN and NHPI adults.

Experiences across racial and ethnic groups are mixed regarding receipt of recommended cancer screenings (Figure 10). Among women ages 50-74 (the age group recommended for screening prior to updates in 2024, which lowered the starting age to 40), Black people (24%) are less likely than White people (29%) to go without a recent mammogram. In contrast, AIAN (41%) and Hispanic (35%) people are more likely than White people (29%) to go without a mammogram. Among those recommended for colorectal cancer screening, Hispanic, Asian, AIAN, NHPI, and Black people are more likely than White people to not be up to date on their screening. Increases in cancer screenings, particularly for breast, colorectal, and prostate cancers, have been identified as one of the drivers of the decline in cancer mortality over the past few decades.

Racial and ethnic differences persist in flu and childhood vaccinations (Figure 11). Roughly six in ten Hispanic (63%), AIAN (63%), and Black (58%) adults went without a flu vaccine in the 2022-2023 season compared to about half (49%) of White adults. However, among children, White children (44%) are more likely than Asian (28%) and Hispanic (39%) children to go without the flu vaccine; data are not available to assess flu vaccinations among NHPI adults and children. In 2019-2020, AIAN (42%), Black (37%), and Hispanic (33%) children were more likely than White children (28%) to have not received all recommended childhood immunizations.

Health Status and Outcomes

Racial disparities in health status and outcomes.

Black and AIAN people fare worse than White people across the majority of examined measures of health status and outcomes (Figure 12). In contrast, Asian and Hispanic people fare better than White people for a majority of examined health measures. Nearly half of the examined measures did not have data available for NHPI people, limiting the ability to understand their experiences. Among available data, NHPI people fare worse than White people for more than half of the examined measures.   

LIFE EXPECTANCY

AIAN and Black people have a shorter life expectancy at birth compared to White people, and AIAN, Hispanic, and Black people experienced larger declines in life expectancy than White people between 2019 and 2021. Life expectancy at birth represents the average number of years a group of infants would live if they were to experience the age-specific death rates prevailing during a specified period. Life expectancy declined by 2.7 years between 2019 and 2021, largely reflecting an increase in excess deaths due to COVID-19, which disproportionately impacted Black, Hispanic, and AIAN people. AIAN people experienced the largest life expectancy decline of 6.6 years, followed by Hispanic (4.2 years) and Black people (4.0 years), and a smaller decline of 2.4 years for White people. Asian people had the smallest decline in life expectancy of 2.1 years between 2019 and 2021. Provisional data from 2022 show that overall life expectancy increased across all racial and ethnic groups between 2021 and 2022, but racial disparities persist (Figure 13). Life expectancy is lowest for AIAN people at 67.9 years, followed by Black people at 72.8 years, while White and Hispanic people have higher life expectancies of 77.5 and 80 years, respectively, and Asian people have the highest life expectancy at 84.5 years. Life expectancies are even lower for AIAN and Black males, at 64.6 and 69.1 years, respectively. Data are not available for NHPI people.

SELF-REPORTED HEALTH STATUS

Black, Hispanic, and AIAN adults are more likely to report fair or poor health status than their White counterparts, while Asian adults are less likely to indicate fair or poor health. Nearly three in ten (29%) AIAN adults and roughly two in ten Hispanic (23%) and Black (21%) adults report fair or poor health status compared to 16% of White adults (Figure 14). One in ten Asian adults report fair or poor health status.

BIRTH RISKS AND OUTCOMES

NHPI (62.8 per 100,000), Black (39.9 per 100,000), and AIAN (32 per 100,000) women have the highest rates of pregnancy-related mortality (deaths within one year of pregnancy) between 2017-2019, while Hispanic women (11.6 per 100,000) have the lowest rate (Figure 15). More recent data for maternal mortality, which measures deaths that occur during pregnancy or within 42 days of pregnancy, shows that Black women (49.5 per 100,000) have the highest maternal mortality rate across racial and ethnic groups in 2022 (Figure 16). However, maternal mortality rates decreased significantly across most racial and ethnic groups between 2021 and 2022. Experts suggest the decline may reflect a return to pre-pandemic levels following the large increase in maternal death rates due to COVID-19 related deaths. The Dobbs decision eliminating the constitutional right to abortion could widen the already large disparities in maternal health as people of color may face disproportionate challenges accessing abortions due to state restrictions.

Black, AIAN, and NHPI women have higher shares of preterm births, low birthweight births, or births for which they received late or no prenatal care compared to White women (Figure 17). Additionally, Asian women are more likely to have low birthweight births than White women. Notably, NHPI women (22%) are four times more likely than White women (5%) to begin receiving prenatal care in the third trimester or to receive no prenatal care at all.

Teen birth rates have declined over time, but the birth rates among Black, Hispanic, AIAN, and NHPI teens are over two times higher than the rate among White teens (Figure 18). In contrast, the birth rate for Asian teens is more than four times lower than the rate for White teens.

Infants born to women of color are at higher risk for mortality compared to those born to White women. Infant mortality rates have declined over time although provisional 2022 data suggest a slight increase relative to 2021. As of 2022, Black (10.9 per 1,000) and AIAN (9.1 per 1,000) infants are at least two times as likely to die as White infants (4.5 per 1,000) (Figure 19). NHPI infants (8.5 per 1,000) are nearly twice as likely to die as White infants (4.5 per 1,000). Asian infants have the lowest mortality rate at 3.5 per 1,000 live births.

HIV AND AIDS DIAGNOSIS INDICATORS

Black, Hispanic, NHPI, and AIAN people are more likely than White people to be diagnosed with HIV or AIDS, the most advanced stage of HIV infection. In 2021, the HIV diagnosis rate for Black people is roughly eight times higher than the rate for White people, and the rate for Hispanic people is about four times higher than the rate for White people (Figure 20). AIAN and NHPI people also have higher HIV diagnosis rates compared to White people. Similar patterns are present in AIDS diagnosis rates, the most advanced stage of HIV, reflecting barriers to treatment. Black people have a roughly nine times higher rate of AIDS diagnosis compared to White people, and Hispanic, AIAN, and NHPI people also have higher rates of AIDS diagnoses. Most groups have seen decreases in HIV and AIDS diagnosis rates since 2013, although the HIV diagnosis rate has remained stable for Hispanic people and increased for AIAN and NHPI people.

Among people ages 13 and older living with diagnosed HIV infection, viral suppression rates are lower among AIAN (64%), Hispanic (64%), NHPI (63%), and Black (62%) people compared with White (72%) and Asian (70%) people (Figure 21) . Viral suppression refers to having less than 200 copies of HIV per milliliter of blood. Increasing the viral suppression rate among people with HIV is one of the key strategies of the Ending the HIV Epidemic in the U.S. initiative. Viral suppression promotes optimal health outcomes for people with HIV and also offers a preventive benefit as when someone is virally suppressed, they cannot sexually transmit HIV.

CHRONIC DISEASE AND CANCER

The prevalence of chronic disease varies across racial and ethnic groups and by type of disease. Diabetes rates for AIAN (18%), Black (16%), and Hispanic (13%) adults are all higher than the rate for White adults (11%). AIAN people (11%) are more likely to have had a heart attack or heart disease than White people (8%), while rates for Black (6%), NHPI (6%), Hispanic (4%) and Asian (3%) people are lower than White people. Black (12%) and AIAN (13%) adults have higher rates of asthma compared to their White counterparts (10%), while rates for Hispanic (8%) and Asian (5%) adults are lower, and the rate for NHPI is the same (10%). Among children, Black children (16%) are nearly twice as likely to have asthma compared to White children (9%), while Asian children (6%) have a lower asthma rate (Figure 22). Differences are not significant for other racial and ethnic groups, and data are not available for NHPI children.

AIAN, NHPI, and Black people are roughly twice as likely as White people to die from diabetes, and Black people are more likely than White people to die from heart disease (Figure 23). Hispanic people (28.3 per 100,000) also have a higher diabetes death rate compared to White people (21.3 per 100,000). In contrast, Asian people (17.2 per 100,000) are less likely than White people (21.3 per 100,000) to die from diabetes, and AIAN, Hispanic, and Asian people have lower heart disease death rates than their White counterparts.

People of color generally have lower rates of new cancer cases compared to White people, but Black people have higher incidence rates for some cancer types (Figure 24). Black people have lower rates of cancer incidence compared to White people for cancer overall, and most of the leading types of cancer examined. However, they have higher rates of new colon, and rectum, and prostate cancer. AIAN people have a higher rate of colon and rectum cancer than White people. Other groups have lower cancer incidence rates than White people across all examined cancer types.

Although Black people do not have higher cancer incidence rates than White people overall and across most types of cancer, they are more likely to die from cancer. Black people have a higher cancer death rate than White people for cancer overall and for most of the leading cancer types (Figure 25). In contrast, Hispanic, Asian and Pacific Islander, and AIAN people have lower cancer mortality rates across most cancer types compared to White people. The higher mortality rate among Black people despite similar or lower rates of incidence compared to White people could reflect a combination of factors , including more limited access to care, later stage of diagnosis, more comorbidities, and lower receipt of guideline-concordant care, which are driven by broader social and economic inequities.

COVID-19 DEATHS

AIAN, Hispanic, NHPI, and Black people have higher rates of COVID-19 deaths compared to White people. As of March 2024, provisional age-adjusted data from the Centers for Disease Control and Prevention (CDC) show that between 2020 and 2023, AIAN people are roughly two times as likely as White people to die from COVID-19, and Hispanic, NHPI and Black people are about 1.5 times as likely to die from COVID-19 (Figure 26). Asian people have lower COVID-19 death rates during this period compared to all other race and ethnicity groups.

Obesity rates vary across race and ethnicity groups. As of 2022, Black (43%), AIAN (39%), and Hispanic (37%) adults all have higher obesity rates than White adults (32%), while Asian adults (13%) have a lower obesity rate (Figure 27).

Mental Health and Drug Overdose Deaths

Overall rates of mental illness are lower for people of color compared to White people but could be underdiagnosed among people of color. About one in five Hispanic and Black (21% and 20%, respectively) adults and 17% of Asian adults report having a mental illness compared to 25% of White adults (Figure 28). Among  adolescents , the share with symptoms of a past year major depressive episode were not significantly different across racial and ethnic groups, with roughly one in five White (21%) and Hispanic (20%) adolescents, 17% of Black, and about one in seven Asian (15%), and AIAN (14%) adolescents reporting symptoms. Data are not available for NHPI people. Research suggests that a lack of  culturally sensitive  screening  tools  that detect mental illness, coupled with  structural barriers could contribute to  underdiagnosis  of mental illness among people of color.

AIAN and White people have the highest rates of deaths by suicide as of 2022. People of color have been disproportionately affected by recent increases in deaths by suicide compared with their White counterparts. As of 2022, AIAN (27.1 per 100,000) and White (17.6 per 100,000) people have the highest rates of deaths by suicide compared to all other racial and ethnic groups (Figure 29). Rates of deaths by suicide are also over three times higher among AIAN adolescents (32.9 per 100,000) than White adolescents (10.6 per 100,000). In contrast, Black, Hispanic, and Asian adolescents have lower rates of suicide deaths compared to their White peers.

Drug overdose death rates increased among AIAN, Black, Hispanic, and Asian people between 2021 and 2022. As of 2022, AIAN people continue to have the highest rates of drug overdose deaths (65.2 per 100,000 in 2022) compared with all other racial and ethnic groups. Drug overdose death rates among Black people (47.5 per 100,000) exceed rates for White people (35.6 per 100,000), reflecting larger increases among Black people in recent years (Figure 30). Hispanic (22.7 per 100,000), NHPI (18.8 per 100,000), and Asian (5.3 per 100,000) people have lower rates of drug overdose deaths than White people (35.6 per 100,000). Data on drug overdose deaths among adolescents show that while White adolescents account for the largest share of drug overdose deaths, Black and Hispanic adolescents have experienced the fastest increase in these deaths in recent years.

Social Determinants of Health

Racial disparities in social and economic factors.

Social determinants of health are the conditions in which people are born, grow, live, work, and age. They include factors like socioeconomic status, education, immigration status, language, neighborhood and physical environment, employment, and social support networks, as well as access to health care. There has been extensive research and recognition that addressing social, economic, and environmental factors that influence health is important for advancing health equity. Research also shows how racism and discrimination drive inequities across these factors and impact health and well-being.  

Black, Hispanic, AIAN, and NHPI people fare worse compared to White people across most examined measures of social determinants of health (Figure 31). Experiences for Asian people are more mixed relative to White people across these examined measures. Reliable or disaggregated data for NHPI people are missing for a number of measures.

WORK STATUS, FAMILY INCOME, AND EDUCATION

Across racial and ethnic groups, most nonelderly people live in a family with a full-time worker, but Black, Hispanic, AIAN, and NHPI nonelderly people are more likely than White people to be in a family with income below poverty (Figure 32). While most people across racial and ethnic groups live in a family with a full-time worker, disparities persist. AIAN (68%), Black (73%), NHPI (77%), and Hispanic (81%) people are less likely than White people (83%) to have a full-time worker in the family. In contrast, Asian people (86%) are more likely than their White counterparts (83%) to have a full-time worker in the family. Despite the majority of people living in a family with a full-time worker, over one in five AIAN (25%) and Black (22%) people have family incomes below the federal poverty level, over twice the share as White people (10%), and rates of poverty were also higher among Hispanic (17%) and NHPI (16%) people.

Black, Hispanic, AIAN, and NHPI people have lower levels of educational attainment compared to their White counterparts. Among people ages 25 and older, over two thirds (69%) of White people have completed some post-secondary education, compared to less than half (45%) of Hispanic people, just over half of AIAN and NHPI people (both at 52%), and about six in ten Black people (58%) (Figure 33). Asian people are more likely than White people to have completed at least some post-secondary education, with 74% completing at least some college.

NET WORTH AND HOME OWNERSHIP

Black and Hispanic families have less wealth than White families. Wealth can be defined using net worth, a measure of the difference between a family’s assets and liabilities. The median net worth for White households is $285,000 compared to $44,900 for Black households and $61,600 for Hispanic households (Figure 34). Asian households have the highest median net worth of $536,000. Data are not available for AIAN and NHPI people.

People of color are less likely to own a home than White people (Figure 35). Nearly eight in ten (77%) White people own a home compared to 70% of Asian people, 62% of AIAN people, 55% of Hispanic people, and about half of Black (49%) and NHPI (48%) people.

FOOD SECURITY, HOUSING QUALITY, AND INTERNET ACCESS

Black and Hispanic adults and children are more likely to experience food insecurity compared to their White counterparts. Among adults, AIAN (18%), Black (14%), and Hispanic (12%) adults report low or very low food security compared to White adults (6%) (Figure 36). Among children, AIAN (24%), Black (21%) and Hispanic (15%) children are over twice as likely to be food insecure than White children (6%). Data are not available for NHPI adults and children.

People of color are more likely to live in crowded housing than their White counterparts (Figure 37). Among White people, 3% report living in a crowded housing arrangement, that is having more than one person per room, as defined by the American Community Survey. In contrast, almost three in ten (28%) NHPI people, roughly one in five (18%) Hispanic people, 16% AIAN people, and about one in ten Asian (12%) and Black (8%) people report living in crowded housing.

AIAN, NHPI, and Black people are less likely to have internet access than White people (Figure 38). Higher shares of AIAN (12%), and Black and NHPI people (both at 6%) say they have no internet access compared to their White counterparts (4%). In contrast, Asian people (2%) are less likely to report no internet access than White people (4%).

TRANSPORTATION

People of color are more likely to live in a household without access to a vehicle than White people (Figure 39) . About one in eight Black people (12%) and about one in ten AIAN (9%) and Asian (8%) people live in a household without a vehicle available followed by 7% of Hispanic and NHPI people. White people are the least likely to report not having access to a vehicle in the household (4%).

CITIZENSHIP AND ENGLISH PROFICIENCY

Asian, Hispanic, NHPI, and Black people include higher shares of noncitizen immigrants compared to White people. Asian and Hispanic people have the highest shares of noncitizen immigrants at 25% and 19%, respectively (Figure 40). Asian people are projected to become the largest immigrant group in the United States by 2055. Immigrants are more likely to be uninsured than citizens and face increased barriers to accessing health care.

Hispanic and Asian people are more likely to have LEP compared to White people. Almost one in three Asian (31%) and Hispanic (28%) people report speaking English less than very well compared to White people (1%)(Figure 41). Adults with LEP are more likely to report worse health status and increased barriers in accessing health care compared to English proficient adults.

EXPERIENCES WITH RACISM, DISCRIMINATION, AND UNFAIR TREATMENT

Racism is an underlying driver of health disparities, and repeated and ongoing exposure to perceived experiences of racism and discrimination can increase risks for poor health outcomes. Research has shown that exposure to racism and discrimination can lead to  negative  mental health  outcomes  and certain negative impacts on physical health, including depression, anxiety, and hypertension.

Black, AIAN, Hispanic, and Asian adults are more likely to report certain experiences with discrimination in daily life compared with their White counterparts, with the greatest frequency reported among Black and AIAN adults.  A 2023 KFF survey shows that at least half of AIAN (58%), Black (54%), and Hispanic (50%) adults and about four in ten (42%) Asian adults say they experienced at least one type of discrimination in daily life in the past year (Figure 42). These experiences include receiving poorer service than others at restaurants or stores; people acting as if they are afraid of them or as if they aren’t smart; being threatened or harassed; or being criticized for speaking a language other than English. Data are not available for NHPI adults.

About one in five (18%) Black adults and roughly one in eight AIAN (12%) adults, followed by roughly one in ten Hispanic (11%), and Asian (10%) adults who received health care in the past three years report being treated unfairly or with disrespect by a health care provider because of their racial or ethnic background.  These shares are higher than the 3% of White adults who report this (Figure 43). Overall, roughly three in ten (29%) AIAN adults and one in four (24%) Black adults say they were treated unfairly or with disrespect by a health care provider in the past three years for any reason compared with 14% of White adults.

About the Data

Data sources.

This chart pack is based on the KFF Survey on Racism, Discrimination, and Health and KFF analysis of a wide range of health datasets, including the 2022 American Community Survey, the 2022 Behavioral Risk Factor Surveillance System, the 2022 National Health Interview Survey, the 2022 National Survey on Drug Use and Health, and the 2022 Survey of Consumer Finances as well as from several online reports and databases including the Centers for Disease Control and Prevention (CDC) Morbidity and Mortality Weekly Report (MMWR) on vaccination coverage, the National Center for Health Statistics (NCHS) National Vital Statistics Reports, the CDC Influenza Vaccination Dashboard Flu Vaccination Coverage Webpage Report, the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) Atlas, the United States Cancer Statistics Incidence and Mortality Web-based Report, the 2022 CDC Natality Public Use File, CDC Web-based Injury Statistics Query and Reporting System (WISQARS) database, and the CDC WONDER online database.

Methodology

Unless otherwise noted, race/ethnicity was categorized by non-Hispanic White (White), non-Hispanic Black (Black), Hispanic, non-Hispanic American Indian and Alaska Native (AIAN), non-Hispanic Asian (Asian), and non-Hispanic Native Hawaiian or Pacific Islander (NHPI). Some datasets combine Asian and NHPI race categories limiting the ability to disaggregate data for these groups. Non-Hispanic White persons were the reference group for all significance testing. All noted differences were statistically significant differences at the p<0.05. We include data for smaller population groups wherever available. Instances in which the unweighted sample size for a subgroup is less than 50 or the relative standard error is greater than 30% are noted in the figures, and confidence intervals for those measures are included in the figure.

• Open access
• Published: 21 June 2024

COVID-19 healthcare success or failure? Crisis management explained by dynamic capabilities

• Ritva Rosenbäck 1 &
• Kristina M. Eriksson 1  

BMC Health Services Research volume  24 , Article number:  759 ( 2024 ) Cite this article

Metrics details

Introduction

This paper presents a structured review of the use of crisis management, specifically examining the frameworks of surge capacity, resilience, and dynamic capabilities in healthcare organizations. Thereafter, a novel deductive method based on the framework of dynamic capabilities is developed and applied to investigate crisis management in two hospital cases during the COVID-19 pandemic.

The COVID-19 pandemic distinguishes itself from many other disasters due to its global spread, uncertainty, and prolonged duration. While crisis management in healthcare has often been explained using the surge capacity framework, the need for adaptability in an unfamiliar setting and different information flow makes the dynamic capabilities framework more useful.

The dynamic capabilities framework’s microfoundations as categories is utilized in this paper for a deductive analysis of crisis management during the COVID-19 pandemic in a multiple case study involving two Swedish public hospitals. A novel method, incorporating both dynamic and static capabilities across multiple organizational levels, is developed and explored.

The case study results reveal the utilization of all dynamic capabilities with an increased emphasis at lower organizational levels and a higher prevalence of static capabilities at the regional level. In Case A, lower-level managers perceived the hospital manager as brave, supporting sensing, seizing, and transformation at the department level. However, due to information gaps, sensing did not reach regional crisis management, reducing their power. In Case B, with contingency plans not initiated, the hospital faced a lack of management and formed a department manager group for patient care. Seizing was robust at the department level, but regional levels struggled with decisions on crisis versus normal management. The novel method effectively visualizes differences between organizational levels and cases, shedding light on the extent of cooperation or lack thereof within the organization.

The researchers conclude that crisis management in a pandemic, benefits from distributed management, attributed to higher dynamic capabilities at lower organizational levels. A pandemic contingency plan should differ from a plan for accidents, supporting the development of routines for the new situation and continuous improvement. The Dynamic Capabilities framework proved successful for exploration in this context.

Peer Review reports

The COVID-19 pandemic is a disaster [ 1 ]. However, it differs from many other disasters by the worldwide spread, the uncertainty about the patient treatment, especially in the beginning, and the long duration. The healthcare crisis management challenges in a long duration pandemic are different from management in short duration disaster like an earthquake or a major accident. The management in shorter crises or disasters is described in the research of surge capacity (SuC) [ 2 , 3 ], but the COVID-19 pandemic revealed that successful management in a pandemic, needs to be different [ 4 ]. Further, pandemics differ from other long-duration disasters like war or severe air pollution, due to the uncertainty of the type of healthcare and knowledge needed. Merely, the infected patients appear at the hospital, thus the first to receive information about both the number of patients and their needs are the professionals at the hospitals [ 4 ]. Usually the flow of information comes from a rescue leader through the regional management that prioritizes and distributes the patients to the hospitals [ 5 ]. The hospital management needs to use the in-house knowledge and improve the mobility at the hospital [ 6 ]. Thus, the management’s need in a pandemic is less hierarchical and more learning and innovative [ 4 , 7 , 8 ].

SuC expresses the demand of unusually high capacity caused by crisis and disasters [ 2 , 3 ]. The concept of SuC seems to be the base for the worldwide used NATO standard for crisis management, with a hierarchic structure and strong rules of communication [ 5 ]. Resilience (R) is the most used management framework in healthcare organizations, defined as the capacity to absorb shocks while maintaining function, focusing on two categories i.e., robustness and rapidity [ 7 , 9 ]. The strategic “inside-out” Resource-based view, focus on how the resources on hand could be used to the market “inside-out” and have developed during time to the organization’s ability to renew competences to adjust to changes in the surroundings, and include understanding of the requirements from the market or environment (“outside-in”) [ 10 ]. The different flow of information and the constant need for learning and development in an unknown and continuously changing environment could make the hierarchic system of SuC too static and less successful. Therefore, in a disaster such as the COVID-19 pandemic other approaches to crisis management need to be considered. The Dynamic Capability (DC) framework was designed to explain how organizations achieve and sustain competitive advantages by adjusting resources and adapting to changing environments. Originating from a resource-based view, Dynamic Capabilities (DCs) emphasize an organization’s ability to adapt resources to new conditions. From this perspective the DC framework has been limited applied in healthcare management research before the COVID-19 pandemic [ 11 , 12 , 13 ]. However, the possibilities of DCs in the context of the public sector have gained research interest, e.g., Furnival et al. [ 12 ] suggest further research into using the microfoundations and Pablo et al. [ 13 ] ask for more research on how managers or organizations can enable DC in the public sector. The application of the DC framework in health care organizations are thus gaining research interest and to understand the applicability of DCs in health care, especially in relation to unpredictable and long duration disasters, further research into the field is called for. Contributions to the field, demonstrating results from in-dept studies with hospital management expertise at different management levels may be especially valuable for building knowledge toward meeting future long-duration disasters and crises with similar characteristics. This study adopts and develops the DC framework to investigate effective resource utilization and how the DC framework could be more usable, especially in long-duration pandemics. This prompts the research question: How can the DC framework explain the disaster management in healthcare organizations during the COVID-19 pandemic? The research presented develops the concept of the DC framework, which is applied to a multiple qualitative case study to understand the management changes during the COVID-19 pandemic.

The paper starts with an overview of applied crisis management theories, thereafter the results from a structured review of the use of SuC, R and DC in healthcare research, especially focusing of disasters and pandemics, is presented. The methodology of a qualitative multiple case study and the two cases are outlined and thereafter the findings are reported. The discussion and conclusion wrap up the paper.

Crisis management in literature

SuC expresses the demand of unusually high capacity caused by crisis and disasters [ 2 , 3 ]. SuC have been studied over the last decade, mostly in healthcare organizations, but can be generalized to other systems involving complex activities [ 9 ]. The management part of SuC is carefully stated with solid rules concerning how and to whom to communicate and incorporates a hierarchy of decisions [ 5 ].

R was originally used to describe ecological systems’ ability to resist disturbances [ 9 ]. The theories of R have been developed in crisis management science with the aim of improving performance of systems during crisis. R should include all resources that need to be safeguarded from expected or unexpected disturbances and can be described both as being robust during change, but also as the ability to absorb uncertainty [ 9 ]. Kruk, et al. [ 14 ] describe the need during the outbreak of a disease or other disasters resulting in a surge demand for healthcare. The conclusion is that a resilient health system needs to be aware, diverse, self-regulating, integrated, and adaptive [ 14 ]. During the COVID-19 pandemic, R could be described by three required preconditions; global solidarity, legal framework, and workforce policies [ 15 ], which are aligned with the research of Kruk et al. [ 14 ] and Therrien et al. [ 9 ]. McDaniels et al. [ 7 ] recommend using R instead of SuC in healthcare organizations, due to the described less static management.

DCs focuses both on the perspective of how the market (outside) influences the organization (inside) and the perspective that the organization needs to adapt to the chosen market [ 16 ], but also to the inside-out perspective which values the organization’s knowledge and resources in the choice of strategy and marketplace [ 10 , 17 ]. Teece, et al. [ 18 ], considered founders of the DC framework, describe the resource based view as static, when organizations in the short term are stuck with existing knowledge and structure. DCs are a special class of capabilities that describe change and innovation essential when organizations need to sustain performance in a changing environment [ 19 ]. The aspect of cyclicity and moving through the DC phases in several iterations may be necessary for organizations to be able to continuously develop [ 12 ] and reach a higher level of understanding of their specific organizations planning characteristics, such as shown by Eriksson, et al. [ 20 ]. Pablo et al. [ 13 ] describe this iteration to learn and transform as experimenting.

Further, the importance of taking a holistic view of the organization is stressed as a prerequisite when moving towards the capability of transformation [ 20 ]. Developed DCs are difficult for competitors to replicate and will give a competitive advantage and innovative response in a rapidly changing market when time to market is critical [ 18 ]. Both inside-out and outside-in strategy capabilities need to be dynamic and constantly renewed [ 21 ]. For moderately dynamic markets it is possible with traditional routines to build on predictable and analytical processes and build DC from existing knowledge. However, for high-velocity markets, with unpredictable outcomes, DCs need to develop to be simpler, more experimental, and iteratively relying on situation specific knowledge within simple rules and are often described vaguely as “routines to learn routines” [ 11 ]. Capabilities that are not supporting changes is by a few scholars called static capabilities (SC), e.g., Dawson [ 22 ] is using SC for exploring knowledge management and Mortensen et al. [ 23 ] are using it to explore barriers for futures literacy. The DCs have advanced in different areas and hereafter the development over the last ten years in healthcare disaster management are focused and described.

Crisis management in healthcare literature

The COVID-19 made the healthcare business volatile and has caused an exponential increase in frequency of use of concepts of crisis management i.e., SuC, R, and DCs. A structured search in Scopus, searching “all fields” with the keywords “Healthcare” and “Disaster” (doted lines) or “Pandemic” (full lines) and “Surge Capacity”, “Resilience” or “Dynamic Capability” between 2010 and 2022, delivers a result of the amount of research papers applying the concepts, see Fig.  1 . Research studies investigating the use of R is more than ten times higher than SuC and DCs (left scale) and shall therefore be read at the right scale in Fig.  1 . SuC and R seem to have been used in healthcare crisis management research at least from the beginning of 2010th decade both for pandemics and disasters. The interest of R seems to rise in use especially in combination with disasters and the interest of DC started later, but the use in research increased after 2014. At the start of the COVID-19 pandemic in 2020 the research into all three concepts increased largely and DCs is the concept with the highest increase in publications between 2019 and 2022 (> 400 times) after which it exceeded the use of SuC. SuC declined between 2021 and 2022. Thus, exploring the DC concept in healthcare was found interesting.

Use of the crisis management concepts SuC, R and DCs

The search in Scopus was limited from all fields to; article title, abstract and the keywords was reduced to “healthcare” and “Dynamic capabilities” resulting in 88 papers (reduced from 5134 results). Further papers in the areas of computer science, focusing on simulations and analytics, were omitted, resulting in 54 papers. The abstracts of those 54 papers were read and 24 papers of the highest relevance were kept. All 24 papers were read in full, and the eight most interesting papers were studied in more detail in this research. In addition, five research papers, found outside of the Scopus search through snowball technique, were included because of additional interesting and highly relevant research. Thus, in total 13 papers, outlined in Table  1 , about DC in healthcare crisis management were studied in detail and used in the research presented in this paper.

Dynamic capabilities framework in healthcare

The DCs framework is usually divided into sensing, seizing and transformation [ 24 ]. However, other scholars express it differently as i.e., detection, understanding and reconfiguration [ 25 ] or i.e., dynamic managerial capabilities and dynamic organizational capabilities, where the latter is divided as described above, but the former divides into managerial cognition, managerial human capital, and managerial social capital [ 26 ]. Moreover, Sheng [ 27 ] divides the capabilities into three groups for the inside-out view. First the “system capabilities” with the content of written regulations, guidelines, and instructions. Secondly the “socialization capabilities” can be explained as the organizations shared ideology and basic values and influences how the members of the organization treat each other in a crisis. The third is expressed as “coordinating capabilities” and influences the number of fruitful contacts in the organization. For the outside-in view Sheng [ 27 ] describes “organizational sensemaking”, as a continuous process of how the organization is seeking information of the environment and how this is formed to common goals for the organization. Moreover, in a framework for decision-making in crisis in major projects, sensing is explored as an important framework category [ 28 ] In the developed method in this paper Teece’s [ 24 ] the microfoundations are used as framework categories i.e., sensing, seizing and transformation.

Sensing includes the identification of all kinds of risks and opportunities, e.g., technical advancements, suppliers’ possibilities to deliver and regulations, preferably before they arrive [ 12 , 29 ]. Research concerning sensing often refers to analytical and forecasting [ 30 ], and the need for real time data [ 8 ]. The capability of sensing focuses on service users, stakeholders, and suppliers [ 12 ] or on specific important factors e.g., problems detection, lack of coherence of safe routines or risk for high demand or exhaustion [ 31 ]. Ohrling et al. [ 8 ] describe the importance of rapidly understanding the unexpected during the COVID-19 pandemic and finding resources to increase the ability to analyze the situation and add that knowledge and experience to the emergency management team. Further, the communication to spread an always changing target and new information to the emergency management team and to everyone, to create a common understanding [ 8 ] could also be included in the DC of sensing. To make the sensing appear, meetings need to be highly frequent both in the organization and between organizations. However, it could be important to limit information due to a high and intense flow from different resources that may lead to misunderstandings [ 8 ].

Seizing can be seen as the enablers to make dynamic capabilities work and can both be already existent in the organization or newly developed. The DC of seizing provides a link between environmental change and internal adaptability [ 13 ] or it could be routines and processes for change [ 29 ]. A beforehand made contingency plan can be a part of the seizing; thus, these are often built on SuC and are therefore rather static and work against DC [ 32 ]. Seizing could also include culture and management capabilities in the managers’ choice of the competing priorities [ 12 ]. Routines could be how planning, evaluating and decision making should be done, how ideas are received and accredited and how leadership and teamwork are functioning in the organization [ 31 ]. Decentralization and a culture of rapidly responding from the information towards actions and more practically, routines and processes that enable higher frequency meetings, faster coordination, added experts and teamwork can be seen as parts of the DC of seizing [ 8 ].

The transformation includes implementing new processes and policies, for example, decentralization, co-specialization, or governance, and measuring improvement activities and reviewing plans and strategies [ 12 , 29 ]. . Moreover, some researchers refer to learning to respond to changes [ 31 ]. The transformation during a pandemic needs to be continuous with adjustments and rearrangements, due to changing information and environment and the activities need to be tightly followed and continuously evaluated to build flexibility [ 8 ]. The sensing, seizing and transformation as described here is hereafter used in this research.

The synchronization of microfoundations is necessary to make the DC perform [ 33 ]. An organization without seizing will become cosmetic and bureaucratic, and therefore ineffective to take decisions and fulfill the customer needs due to shortage of inter-relationships between the microfoundations. Further, a shortage of transformation will ensure customers and stakeholders that the service will be provided, but it never happens. Without sensing, the organization will appear arrogant and unwilling to seek ideas and knowledge from the outside, thus just focusing on internal plans and strategies [ 12 ]. Whereas, a strong sensing capability could lead to high expectations of seizing and transformation, causing a capability gap, which could be recognized by a lack of top management [ 18 , 29 ]. Moreover, they also mean that a strong sense and a strong transformation at local organizational level implies local unit-focused initiatives, thus, may suboptimize the local unit and not benefit the whole organization. If sensing and seizing capabilities are high, it leads to high barriers between local units, but could also lead to barriers between local units and the top management [ 29 ]. At the daily level, especially in healthcare, the transforming capability is strongest, and the staff will try their best to help the patients. However, a focus on operational tasks may lead to organizations with difficulties in verifying their capacity for change and responding quickly to changes in the surroundings [ 34 ]. Furnival et al. [ 12 ] suggest that organizations in a disaster are different, thus sensing will be more important to be able to rebuild organizational confidence and capability of movement. However, in non-crisis organizations, seizing may be of higher importance, where commitment and culture should help ensure continuous development.

Methodology for the case study and case description

The methodology applied in the research presented is multiple case study. The case study methodology includes the collection of internal hospital documentation, documentation from externa public sources and qualitative data collection through interviews. The case studies are considered suitable when capturing different and elusive aspect and perspectives from real context [ 35 , 36 ]. Thus, the method was chosen to capture and develop an encompassing view of capabilities for disaster management during the COVID-19 pandemic. The selection of case hospitals was meticulous. Several hospitals were considered before finalizing the choices [ 36 , 37 ]. Key diversity factors included hospital size, infection pressure, pandemic timing, collaboration ability, and management stability. The first case, a medium-sized hospital, faced high and early infection pressure, could transfer patients, and had a stable organization. The second case was chosen for its contrasting attributes: larger size, lower and later infection pressure, responsibility to assist other hospitals, and a recent management change. To understand the selected cases, internal documents regarding mission and organization both before and during the Covid-19 pandemic were studied. Further, documents from external public sources were gathered and studied, e.g., newspaper articles and information from national press conferences during the pandemic to increase the knowledge about the pandemic situation and the cases.

The multiple and qualitative case study was built on semi-structured interviews with managers that were conducted about a year after the start of the COVID-19 pandemic. The choice of semi-structured interviews as data collection method were considered valuable for the multiple qualitative case study, to gain focused data and the managers personal view of the management [ 36 ]. Case studies produce context-dependent knowledge, and the data could be used to understand the complex issues of the aspects of the managers dynamic management during the pandemic [ 38 ]. The narratives from managers of different levels were used to identify their opinion of the organization’s management practice.

Case descriptions

The first investigated case (A) is a middle-sized hospital with about 1300 employees, located in a large Swedish region, with several hospitals. The case hospital is an emergency hospital, but without an infection department and with few intensive care unit (ICU) beds. The increase of the COVID-19 infection rate in the catchment area was rapid in the beginning of the pandemic and sometimes the percentage of hospitalized citizens was the highest in the country [ 39 ]. The hospital was about to implement a new NATO standard with instructions for starting a regional command center (RCC) at the regional headquarters and local command centers (LCC), with static rules for how to communicate and make decisions [ 5 ] and concluded the implementation during the beginning of the pandemic.

The second case hospital (B) was chosen to be different, as sought to be advantageous for designing a multiple case study [ 36 ]. Case hospital B is the central hospital in a less populated region (compared to Case hospital A). This region also includes two local hospitals. Case B has about 5000 employees and have an infection department and the most ICU beds in the region. Just before the COVID-19 pandemic the healthcare director was replaced and the region was reorganized and a regional organization was implemented with some of the department’s management centralized to the main hospital, for example the departments of infection and the departments of ICU. The contingency plan was not updated to the new organization.

The interview sessions started in March 2021, one year after the onset of the pandemic, and were completed within a month for Case A and another month for Case B. At Case A, a total of twelve interviews from three organizational levels i.e., hospital manager group (3), department manager group ( 5) and unit manager group (4), were conducted. The presentation of the interviewees is found in Table  2 including the time of the interview. At case B, with a total of eight interviews were performed the hospital management were merged to a regional healthcare management group with the responsibility of the departments, directly reporting to the director of healthcare and hospital managers were not existing. Important functions were found at the regional level and therefore the three levels of management studied became i.e., regional manager group (RM, 3), regional healthcare manager group (2) and department manager group (3) and in total eight interviews were conducted. The presentation of the interviewees is found in Table  2 including the time of the interview.

The interviews were semi-structured, which means the interviewees were allowed to talk freely, and the interviewer avoided affecting the interviewees [ 37 ]. The same researcher moderated all interviews and used a semi structured interview guide as been described in earlier research [ 6 ], with topics of the feeling of the size of the disaster, the contingency plan, how they built capacity for the COVID-19 patients, management during the pandemic and the information flow, as support. Another researcher actively observed the interviews and used the interview guide to follow the completeness of the collection of information and sometimes added a few questions for completion. All interviews were conducted via video conferencing with both sound and video recording. The interviewees were later provided with feedback in the form of a lecture and a written report, to make sure the information gathered was correctly understood [ 40 ]. The recordings were verbatim transcribed and NVIVO14 was used to structure the data. Thereafter, the data were exported to Excel and further analyzed.

Methodology analytical development

The DC and SC frameworks were applied and further developed in this study to explore and analyze the management during the pandemic in the cases. To align the data in relation to the DC and SC frameworks it was suitable to perform the analyses deductively. Therefore, the data were deductively analyzed by selecting excerpts, from the interviews, that aligned with the different DC framework categories (microfoundations)i.e., sensing, seizing and transformation, and SC framework categories (microfoundations), i.e., non-sensing, non-seizing, and non-transformation following other scholars’ definitions and proposals in their research. Moreover, to be able to receive deeper knowledge about the organization and the different management groups’ viewpoint of the organization’s performance at different organizational levels, the data was divided into organizational levels, i.e., department, hospital, regional and national level in case A. In case B one additional level of regional healthcare was used necessary by the special organization, where the regional healthcare organization worked besides the RCC stated in the contingency plan. The hospital level contained a spontaneously developed group of department managers during the first wave of the COVID-19 pandemic. However, during later waves a LCC was started as stated in the contingency plan and emerged with the department managergroup at the hospital level. Table  3 shows the 42 different categories in the deductive analyze.

The interviewee’s excerpts were analyzed several times both from the transcription of the interviews and later from the framework categories. This procedure was conducted to enhance the rigor of the research [ 41 ]. To be able to analyze and present the findings both qualitatively and quantitatively, the excerpts from each interviewee were only coded once to one framework categories.

The deductive analysis of the excerpts in the interviews to the framework categories of DCs and SCs at different organization levels was suitable and the researchers found the method satisfactory. The imposed quantitative analysis is done according to the visualizations in Table  4 .

Findings: multiple case study

A qualitative analysis was conducted to clarify special phenomenon in each of the cases. Moreover, the data was quantitatively analyzed according to the developed method described above. The framework categories and examples of excerpt of each case, group of managers, organizational level are structural gathered in Tables  5 , 6 , 7 , 8 , 9 and 10 and are referred to in the text to prove different phenomenon in the organization. The use of the group of managers instead of a single title for every excerpt gives an improved overview of the management opinions at different organizational levels. Moreover it ensures keeping the anonymity of the hospital and their employees. The excerpts about the national level were fewer and were therefore excluded from the table. However, DC at a national level mostly referred to the national organizations of ICU and infection physicians, who made large efforts to gather important medical information and treatment of the COVID-19 patients and to spread the knowledge to other physicians through webinars once a week as the chief medical officer at case A expressed:

“The Swedish Association of Infectious Disease Physicians has taken on a great deal of responsibility and has held regular webinars with knowledge updates with leading researchers and clinicians in this field.” (Chief medical officer, case A).

Some DC was about the prognoses from the National Board of Social Affairs and Health and the public health authority that was helpful especially towards the end of the pandemic for example:

“During the late spring (2020) and just before the summer, more scenarios are brought up that were sort of adapted based on different regions that you could then work with” (The chief of staff at regional level, case B).

Thus, the SC excerpts describe lack of information and the continuously changing information from Swedish authorities for example:

“Quite shaky at first. Slightly different message. Message not coming … We felt it was messy”. (The chief of staff at regional level, case B).

Figure  2 visualizes the excerpts of each of the units and department managers of case A and there were about double as many as the excerpts of the hospital management. At case B the healthcare management had a slightly smaller number of excerpts. This needs to be remembered during the semi-quantitative analysis. Moreover, Fig.  2 envisions that the managers find the organizations to be more dynamic than static. The hospital manager and unit managers in case A and the healthcare managers in case B have proportionally fewer SC excerpts.

Number of excerpts/group of managers

Figure  3 visualizes the number of excerpts per organization level to show their dynamically respectively statically behavior during the pandemic. The department level in case A received the highest number of dynamic excerpts followed by the hospital level, however, the hospital level has a higher proportion of SC. The highest proportion of static behavior, showing nearly the same number of excerpts as DC, are found at the regional level. The examples of criticism was that they lately understood the severeness of the COVID-19 pandemic (Table  6 , RegLev: NonSensa), pushed to work use the NATO standard even if it was not implemented (Table  7 , RegLev: NonSeiza) and kept the structure of crisis management even when the disasterwere prolonged. However, further into the COVID-19 pandemic RCC lost power towards the normal group of hospitals directors, which made the hospital managers more positive towards the regional level. (Table  7 , RegLev: Trans). Examples of criticism from the department managers towards the regional level appears later in the COVID-19 pandemic when the politicians changed focus and made the cooperation over the region work less effective (Table  6 , RegLev: NonSeizb). The politicians also caused dissatisfaction among the professionals by building an ICU at a fair hall outside the hospitals which was never used.

Number of excerpts/organization level

In case B the highest numbers of dynamic excerpts were at the hospital level and at the regional level, but the proportion of SC at the regional level was higher. The healthcare level had the highest proportion of SC with slightly the same number as the DC correlative to the situation at the regional level at case A. The healthcare level got criticised both from the department and the regional managers, for example one regional manager’s questioned the active decision at the healthcare level to have their own regional crisis management beside the RCC and that they did not start an LCC at the case hospital (Table  10 , RegHealthLev: NonSeiza; Table  8 , RegHealthLev: NonSeiza). Further, the regional management had a high proportion of SC especially from the healthcare level, because of the regional levels strong statement of a contingency plan that maybe was not appropriate in a pandemic (Table  9 , RegLev: NonSeiza). The regional healthcare level pushed for management more as usual as in line with the hospital managers at case A (Table  9 , RegHealthLev: Seiza). Thus, the department managers started an local manager group at the hospital for practical decisions and needs without any mandate and official agreement (Table  8 , RegHealthLev: NonSeizb).

Number of interviewees excerpts for each group of managers/organizational level

When looking closer of how different management groups assesses each organization level (Fig.  4 ) the cases differ even more. In Case A the managers consider their level with positive eyes as well as the level nearest above, for example when the department manager group praised the hospital manager for his braveness (Table  6 , HospLev: Seiza) or when the department manager group talked about their thoughts of getting the employees to act with the managers spirit (Table  6 , DepLev: Seiza). However, the most SC also appeared for the level directly above, for example, that the department level underestimated the COVID-19 changings (Table  5 , DepLev: NonSens) and the lack of tools for keeping employees at the working place in a stressing environment (Table  6 , HospLev: NonSeiza). However, the unit managers evaluates the second nearest hospital level dynamic and comment on the short distance to the hospital director, known by everyone (Table  5 , HospLev: Seiz).

All manager groupsof Case B seem to be self-critical and considered their own level as being somewhat static, for example the department managers reflection that the idea to start a new department was not the best choice (Table  8 , DepLev: NonTrans) or the regional managers reflection of their poor management when the healthcare LCC was not started in the beginning of the pandemic (Table  10 , RegLev: NonSeiz). The regional manager group seem to be self-confident about their own level (Table  10 , RegLev: Seiz), but the number of excerpts from the regional managers reflected that the healthcare level has higher proportion of SC than DC caused by the special crisis management group at healthcare regional level as described before. The healthcare manager group have a high number of dynamic excerpts towards the hospital level, who they found transformed by building additional beds at ICU (Table  9 , HospLev: Trans), but do not have many comments about the department level. The proportion of SC is high from the healthcare managers towards the regional level arguing that a pandemic need to be managed by normal healthcare management (Table  9 , RegLev: Nonseizb). Caused by interviewing the regional management of case B, the excerpts about the national level are present in higher numbers – both positive and negative.

At hospital A the sensing and transformation occurred more frequently at lower organization levels (Fig.  5 ) with a descending occurrence at higher levels. At the department level in case A they listened to the international network and because of their closeness to the production they saw the changing number of patients and clearly sensed the level of worry and stress on the organization (Table  6 , DepLev: Sensa). Further, they early on realized that a long duration pandemic made the situation different from other disasters (Table  6 , DepLev: Sensb). The proportion of transformation was high and for example they managed an increase in employment at the ICU from 160 to 320 (Table  6 , DepLev: Transb). Moreover, they changed working procedures, for example agreeing on an allowance to shout out into the corridor when you needed something to avoid taking the Personal Protection Equipment (PPE) off and on again (Table  6 , DepLev: Transa). Examples of non-transforming capabilities were overusing PPE, the infection spread between employees, the shortage of employees at the critical units and the shortage of training before work a shift at a new position (Table  5 , DepLev: NonTrans; HospLev: NonTrans).

Excerpt/organization level, case A

At the hospital level they sensed the employees’ anxiety and worries about the risk of infection for themselves and relatives and the knowledge shortage when moving to other tasks and transformed by arranging psychological help for the employees (Table  7 , HospLev: Transa). Moreover, they helped with recruitment, moved employees to the units needed, built education and hygiene rounds, and started and stopped planned surgery several times (Table  7 , HospLev: Transb). The meetings became digital and the number of employees in the coffee rooms at once was reduced and they reconstructed several departments. The non-transformation was rather high at the hospital level, possibly a sign that transformation was too late or not large enough (Table  7 , HospLev: NonTrans).

The seizing was found equally at department and hospital level. Hospital, department, and unit levels of case A increased the frequency of meetings to daily or even more. (Table  5 , DepLev: Seiz). The unit managers used the existing dynamic quality of the organization including single rooms at the wards (Table  6 , DepLev: Seizb), the united management of all wards and the knowledgeable management of ICU to take necessary decisions and execute them. The cooperation between the hospital departments increased and there was a focus on healthcare and all other questions were not prioritized (Table  7 , HospLev: Transc). The non-seizing was the most occurring static behaviour, and it increased in occurrence with higher organization level. Thus, the structure for moving employees to even out the pressure, for example agreements of compensation and individual education, were not in place and were not working properly (Table  6 , HospLev: NonSeizb). Moreover, department managers responsible for the reduced planned healthcare were not allowed to use their free time to develop their organization and they also commented that the focus of staffing at ICU was too high and that the decisions about the start of surgery in between the waves came to late (Table  6 , HospLev: NonTrans). The non-seizing towards the regional level was higher than the seizing. The criticism was that there was too little capacity at ICU in the region, neither agreements for cooperation between the public hospitals nor between the private and public hospitals were in place (Table  6 , RegLev: NonSeiza). Moreover, the contingency plans structured methods of communication, built for short term disasters, caused a lot of questions, especially during the first wave and no one listened to the managers respond about what happened at the hospital (Table  7 , RegLev: NonSeizb). The excerpts of seizing described the appreciation when the production group later became more powerful. Moreover, the ICU managers met over the region at a regional level and made decisions (Table  6 , RegLev: Trans).

The DC and SC excerpts pattern/organization level in case B were different compared to case A (Fig. 6 ). Instead of the highest number of excerpts about transformation near the production, the transformation in case B seems to have been high at department, hospital, and regional level and lower at the healthcare level. The number of excepts about sensing was surprisingly highest at the regional level, which possibly is due to the highly experienced and knowledgeable regional chief hygienist physician’s high and his trust and a good international network (Table  8 , RegLev: Sens). Moreover, the chief hygiene physician contributed to merely transforming the organization by decisions to decrease the infection between employees and at elderly homes (Table  10 , RegLev: Trans), which also caused the high number of transformation excerpts at the regional level. The number of excerpts for non-seizing is high both at the healthcare level and the regional level due to the earlier mentioned argumentation unclearness of documentation about where decisions were made (Table  10 , RegHealthLev: NonSeizb) and this meant a focus of seizing at hospital level. The non-transformation was rather high both at the healthcare level and at the regional level.

Excerpts/organization level Case B

The discussion is divided into discussion about the developed research method and discussion about the result of the multiple qualitative case study.

Research method

The research presented developed the use of DC in a qualitative deductive analysis of interviews and is novel especially in healthcare organizations. The data were analyzed with framework categories (microfoundations)of DC i.e., sensing, seizing and transformation following other scholars’ approach, e.g., Teece et al. [ 18 ]. , . In addition to this proven application of DC the interviewees’ excerpts, which narrate a static behavior, were coded as framework categories (microfoundations) of SC i.e., non-sensing, non-seizing, and non-transformation to increase the visibility of malfunctions in the disaster management analysis [ 22 , 23 ]. The coding of both DC and SC contributes to a more encompassing analysis of the organizations’ development during the COVID-19 pandemic. The introduction of SC shows important insight also into occurrences that may reverse the movement towards transformation of the organization. Further, the coding was divided by management group and organization levels, which revealed a visualization of the dynamics in between the management levels which had not been found in earlier research. The excerpts were only coded once and therefore the qualitative analysis could partly be quantitative even if some excerpts might contain several items. The method was used to analyze multiple cases and successfully revealed differences between the organizations when using this developed technique of analysis.

Multiple case study

The professionals working in production in both cases clearly sensed the situation when the COVID-19 patients arrived and the organization rapidly transformed to save lives, in line with research by Teece [ 34 ] and Ohrling et al. [ 8 ]. The early sensing at department level in case A, due to an international network made the organization transform even before the first patient arrived. These occurrences of sensing made the response to changes in demand possible even with high focus on operational tasks, despite such situations can be proved to be non-resilient [ 12 , 34 ].

All DCs were, according to the managers narratives, present at the department level in case A and the occurrence of high seizing impeded the suboptimizing of the local unit over the whole organization that could occur with strong sensing and transformation [ 18 , 29 ]. Moreover, seizing, which Furnival et al. [ 12 ] mean is advantageous when working with continuous development, might be a sign of a higher need of continuous development in a long-lasting pandemic than in a short crisis.

The highest occurrence of sense in case A was found at the department level and led to high expectations of seizing and transformation, which according to our research was not delivered from the regional or national level, which is aligned with situations described by other scholars [ 8 , 18 , 29 ]. In fact, the quota between the number of interviewees’ excerpts/framework categories of seizing and non-seizing decreases with higher organization level in case A, which suggests that the top management was less dynamic. The low sense at the regional level made the mistrust high, possibly because that they appeared arrogant and unwilling to change, in line with the study of Furnival et al. [ 12 ]. When the RCC was overtaken by the hospital managers, this production group made the organization more dynamic, and the sense of the situation was more easily transferred to the regional level. Later, when the politicians started to interfere with the organizations, the cooperation between the hospitals decreased, which resulted in suboptimization of the local units in the organization, which decreased the overall organizational efficiency, as also expressed by Ljungquist [ 29 ].

However, the situation in case B, where the non-official department management group originated in the absence of a strong hospital manager or a working LCC, became different. The department management group sensed the situation and transformed accordingly, which according to Ljungquist [ 29 ] and Teece et al. [ 19 ] research could cause suboptimization of the regional cooperation as well as high barriers between the department managers and the regional healthcare management. However, because the sense was high at the regional level the barriers between the department manager’s group and regional management were not seen. The chief hygiene physician at regional level early sensed the situation, by his international network and reacted fast, transformed, and successfully reduced the infection rate also outside the hospital. His placement at the top of the organization, far away from the production, was of a less hindrance due to his and his team members’ high frequency and trustful contacts with the organization’s lower management levels. The lower occurrence of sensing in case B, except for the regional level, is probably the cause of the decreased confidence between the organization levels, which is in line with Furnival et al. [ 12 ]. Moreover, Ljungquist [ 29 ] and Teece et al. [ 18 ] discuss that a higher occurrence of seize could mean higher barriers and mistrust both between local units and the units and top management, which is also recognized in case B. The low sense at healthcare level made the mistrust even higher possibly due to the appearance of arrogancy and unwillingness to change [ 12 ].

When the knowledge increased, and the COVID-19 infections changed, the transformation continued in cycles, as Eriksson et al. [ 20 ] highlighted in their research, for example when the surgery started stopped and restarted several times in case A. Another cyclic change occurred in case B when the launching of a new infection department failed due to problems with staffing. This proved an important learning point for the next step of transformation when instead an old inpatient unit was transferred, which follows the experimentation work described by Pablo et al. [ 13 ].

The information flow during the COVID-19 pandemic was enormous especially in case A with the higher and earlier breakout and the recommendations often changed and made the information channels break down. Using integrated information from different sources from different management levels like Ohrling et al. [ 8 ] suggest could probably also in our case reduce the amount of information and reduce misunderstandings.

The contingency plans, which the regional crisis management at both cases insisted on following were designed to manage a short-term crisis and seemed to be built according to a static and hierarchical SuC. However, this and other studies reveal a need for more distributed management in a long-term disasters [ 4 , 7 , 8 ]. Reality often differs from beforehand plans and if the plans are followed too strictly the organization will be static and not able to follow the dynamic changes [ 32 ]. The regional level’s insisting on sticking to the contingency plan excluded them from supporting the pandemic. Moreover, in case B the contingency plan caused a lot of argumentations about the plan instead of looking at the reality and developing a sound cooperation between the levels in the extended work caused by the pandemic. However, the regional healthcare level in case B insisted on keeping normal management routines, but because of the low sensing at regional healthcare level in case B this did not function. Whereas in Case A this approach worked well. The focus on following the plan in Case B possibly made the management levels less sensitive to the situation [ 12 ]. The suggestion from Eisenhardt et al. [ 11 ] to have “routines to learn routines” could build a more successful disaster management in a next pandemic.

Concluding discussion multiple case study

Case A had at department and hospital level well developed and synchronized DCs and managed the high pressure of the COVID-19 pandemic successfully, as foreseen by other scholars [ 12 , 33 ]. The managers in case A described that they and their employees became more self-confident and took decisions independently, which is in line with the reasoning by Ohrling et al. [ 8 ] about decision space as a success factor during the COVID-19 pandemic. The cooperation and trust at department and hospital levels increased during the pandemic, which is in line with research by Ohrling et al. [ 8 ] and Pablo et al. [ 13 ]. Higher management levels lacked developed DCs, which grew mistrust between the hospitals in the region and the regional management.

However, in case B the seizing and non-seizing were the strongest capabilities, which could be the sign of a concentration and discussion of routines in the overall organization rather than supporting a transforming at department level to save lives. The seen self-criticism in case B could be a sign that the management was malfunctioning, and they were looking for what was wrong at their position. To conclude, Case B coped well with the pandemic, however, they might have had problems succeeding if encountering the higher infection rate, such as in case A.

Conclusion/relevance/contribution

The method, using a deductive analysis of analyzing with DC and SC, different management groups and organization levels, has successfully been used when explaining the crisis management in healthcare organizations during a long-term disaster as a pandemic. This novel way of analyzing data facilitated a structured and detailed explanation of organizational behavior and has not been found in earlier research.

The case hospitals studied showed major differences, when evaluated with the promising DC-method; In case A the hospital manager was considered by the lower-level managers to be brave and strong and supported the professions sensing, seizing and transformation at the department level. Due to the information developed at profession level, the sensing did not reach the regional disaster management, thus could not appropriately support the transformation and their power was reduced in favor of the normal management and cooperation between hospital managers in the region. However, in case B, where the contingency plans stated LCC were not started, the hospital suffered from lack of management and started their own department manager group to be able to take care of the incoming patients. The seizing was high in the organization with the department management developing their own routines, while the regional level and regional hospital level got stuck in discissions about the best choice of management between disaster management or normal management. However, both cases did use DC’s and the capabilities were synchronized enough to withstand the COVID-19 pandemic at the level needed.

The managerial contributions from thisresearch are in line with other scholars.Crisis management in a pandemic need to be more distributed and dynamic and this view need to be the starting point for top management to develop a contingency plan specialized for pandemics. The pandemic plan should manage to develop routines according to the demand from an ongoing pandemic, develop and use DC’s in the whole organization to support the profession to sense, seize and transform. Moreover, building professional networks could help reaching an early sensing, where two examples are, the one that made case A start early to build capacity and the one at case B that reduced the infection rate, which will give an opportunity to save lives. In a long-lasting pandemic, cyclic and continuous improvement seems to be needed.

Limitations and future research

A limitation of this paper,, is its potential to generalize the findings from two Swedish hospitals’ case studies to other healthcare facilities or different organizations. THowever, this limitation is somewhat outweighed by the successful intention of obtaining rich data coupled with an in-depth analysis based on interviews with different manager groups’ view of the management at different organizational levels, which contributes an encompassing view of the applicability of the DC framework in health care. Nevertheless, additional research is needed to enhance the promising method’s effectiveness and support its broader development. It is highly recommended to conduct further studies in this area, expanding its application to diverse types of organizations and environments. It would be interesting to supplement the data with further inquiries about the current application of lessons learned during the pandemic. Especially what was learnt about the possibilities for flexible organizations to make multiple transformation to follow the changing environment during av pandemic. Not just that they transformed but also why some managers was able to build trust and avoid power games and negative story telling in the organisation. To summarize it is important that the insights gained from the COVID-19 pandemic should be carefully refined to strengthen disaster management, thus improving our readiness for future pandemics.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Dynamic Capabilities

Static Capabilities

Surge capacity

Framework categories

Transformation

Non-Sensing

Non-Seizing

Non-Transformation

Local command centre

Regional command centre

Department level

Regional healthcare level

Hospital level

Regional level

Intensive care unit

Personal protective equipment

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Rosenbäck, R., Eriksson, K.M. COVID-19 healthcare success or failure? Crisis management explained by dynamic capabilities. BMC Health Serv Res 24 , 759 (2024). https://doi.org/10.1186/s12913-024-11201-x

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Who Pays For Rising Health Care Prices? Evidence from Hospital Mergers

Brot-Goldberg, Z, Cooper, Z, Craig, SV, Klarnet, L, Lurie, I, and Miller, C. “Who Pays For Rising Health Care Prices? Evidence from Hospital Mergers.” NBER Working Paper No. 32613.

We analyze the economic consequences of rising health care prices in the US. Using exposure to price increases caused by horizontal hospital mergers as an instrument, we show that rising prices raise the cost of labor by increasing employer-sponsored health insurance premiums. A 1% increase in health care prices lowers both payroll and employment at firms outside the health sector by approximately 0.4%. At the county level, a 1% increase in health care prices reduces per capita labor income by 0.27%, increases flows into unemployment by approximately 0.1 percentage points (1%), lowers federal income tax receipts by 0.4%, and increases unemployment insurance payments by 2.5%. The increases in unemployment we observe are concentrated among workers earning between $20,000 and $100,000 annually. Finally, we estimate that a 1% increase in health care prices leads to a 1 per 100,000 population (2.7%) increase in deaths from suicides and overdoses. This implies that approximately 1 in 140 of the individuals who become fully separated from the labor market after health care prices increase die from a suicide or drug overdose.

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• Published: 23 September 2014

Health policy – why research it and how: health political science

• Evelyne de Leeuw 1 ,
• Carole Clavier 2 &
• Eric Breton 3  

Health Research Policy and Systems volume  12 , Article number:  55 ( 2014 ) Cite this article

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The establishment of policy is key to the implementation of actions for health. We review the nature of policy and the definition and directions of health policy. In doing so, we explicitly cast a health political science gaze on setting parameters for researching policy change for health. A brief overview of core theories of the policy process for health promotion is presented, and illustrated with empirical evidence.

The key arguments are that (a) policy is not an intervention, but drives intervention development and implementation; (b) understanding policy processes and their pertinent theories is pivotal for the potential to influence policy change; (c) those theories and associated empirical work need to recognise the wicked, multi-level, and incremental nature of elements in the process; and, therefore, (d) the public health, health promotion, and education research toolbox should more explicitly embrace health political science insights.

The rigorous application of insights from and theories of the policy process will enhance our understanding of not just how, but also why health policy is structured and implemented the way it is.

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Background: policy is not an intervention

Systems perspectives on population health development entered research and practice agendas from the early 1980s. Two complementary traditions emerged; McLeroy et al. [ 1 ] consider health behaviour change as the resultant of the complex interaction between behavioural determinants and higher-level environmental and policy conditions. The Ottawa Charter for Health Promotion [ 2 ] emphasises the development of supportive environments, reorientation of health services, and building of health public policy to enable societies making healthier choices the easier choices. Neither tradition has managed to comprehensively shift research focus, nor has it generated evidence of effectiveness from individual behaviourist perspectives to deep insight in the workings of broader social determinants of health.

Yet, the capacity to develop and assess policy processes for health promotion has been appreciated and formalized across jurisdictions. For Europe, the CompHP Core Competencies Framework for Health Promotion Handbook ([ 3 ], p. 1) states that: " A competent workforce that has the necessary knowledge, skills and abilities in translating policy, theory and research into effective action is recognised as being critical to the future growth and development of global health promotion ". Paragraph 5.7 of the Australian Health Promotion Association’s Core Competencies for Health Promotion Practitioners [ 4 ] states that " an entry level health promotion practitioner is able to demonstrate knowledge of: health promotion strategies to promote health—health education, advocacy, lobbying, media campaigns, community development processes, policy development, legislation ". Interestingly, the most detailed listing of policy competencies is provided by the US National Commission for Health Education Credentialing under section ‘7.5 Influence Policy to Promote Health’ [ 5 ], as indicated below.

7.5.1 Use evaluation and research findings in policy analysis;

7.5.2 Identify the significance and implications of health policy for individuals, groups, and communities;

7.5.3 Advocate for health-related policies, regulations, laws, or rules;

7.5.4 Use evidence-based research to develop policies to promote health;

7.5.5 Employ policy and media advocacy techniques to influence decision-makers.

Yet, for many health educators and health promoters ‘policy’ is a critical yet elusive concept [ 6 ]. On the one hand, they recognise public policy as a critical element in shaping the opportunities for the profession and setting the parameters for its effectiveness [ 7 ]. On the other, they consider policy as an abstract construct best left to politicians, or as a distal determinant of health that can be changed following Cartesian heuristics. Those that have attempted the latter and have failed would claim that policy-making is not just abstract but obscure, without any appreciable logic.

Within the health promotion and health education realm the discourse around policy has been obfuscated further by lumping policy change together with ‘environmental’ perspectives on ‘(social) ecological’ approaches for promoting or improving health behaviour [ 8 ]. Most of the North American literature remains implicit and surprisingly limited in defining, describing, or operationalising what such policy change is or encompasses. For instance, Kahn-Marshall and Gallant [ 9 ] carried out a meta-analysis to assess whether there is demonstrable effect of environmental and policy change on workplace health. However, nowhere in the piece they operationalise what precisely constitutes ‘policy change’ (or for that matter, ‘environmental change’) – it appears to be some undefined notion of modification in organisational parameters.

In this paper, we contend that public health experts, health educators, and health promoters would benefit from considering public policy through the lens of political science rather than through the lens of intervention research. The key arguments are (a) that policy is not an intervention, but drives intervention development and implementation; (b) that understanding policy processes and their pertinent theories is pivotal for the potential to influence policy change; (c) that those theories and associated empirical work need to recognise the wicked, multi-level, and incremental nature of elements in the process; and, therefore, (d) that the health promotion and education research toolbox should more explicitly embrace health political science insights.

Health, policy

Although this is not the place to fully review the academic and practice-oriented discourse around the concepts of ‘health’ or ‘policy’, it seems important to delineate a few issues around the use and application of the expression ‘health policy’.

Policy is in itself a fuzzy concept for political science scholars, variably apprehended as " The actions of government and the intentions that determine those actions " [ 10 ], or rather " Anything a government chooses to do or not to do " ([ 11 ], p. 2). Some would simply see policy as ‘The Plan’ or ‘The Law’ [ 6 ]. Richards and Smith say that " ‘Policy’ is a general term used to describe a formal decision or plan of action adopted by an actor … to achieve a particular goal… ‘Public policy’ is a more specific term applied to a formal decision or a plan of action that has been taken by, or has involved, a state organisation " [ 12 ]. De Leeuw [ 13 ], and Breton and De Leeuw [ 14 ], follow a European tradition in political science that specifies public policy as " the expressed intent of government to allocate resources and capacities to resolve an expressly identified issue within a certain timeframe " . The latter clearly distinguishes between the policy issue, its resolution, and the tools or policy instruments that should be dedicated to attaining that resolution.

Health policy is possibly an even fuzzier term. It has been described unequivocally as " policy that aims to impact positively on population health " [ 15 ] and has been framed as equivalent to " healthy public policy " [ 16 ]. Milio [ 17 ], the first to coin the latter term, later developed a glossary in which she states that " Healthy public policies improve the conditions under which people live: secure, safe, adequate, and sustainable livelihoods, lifestyles, and environments, including housing, education, nutrition, information exchange, child care, transportation, and necessary community and personal social and health services. Policy adequacy may be measured by its impact on population health. " More recently, healthy public policies reincarnated as Health in All Policies [ 18 , 19 ]: " a collaborative approach to improving the health of all people by incorporating health considerations into decision-making across sectors and policy areas. " Variations on this theme have been compiled by Rudolph et al. [ 19 ].

HiAP conceptualisations (Appendix, Rudolph et al., 2013) [ 19 ]

" Health in All Policies is a collaborative approach that integrates and articulates health considerations into policy making across sectors, and at all levels, to improve the health of all communities and people. " – Association of State and Territorial Health Officers (ASTHO).

" Health in All Policies is a collaborative approach to improving the health of all people by incorporating health considerations into decision-making across sectors and policy areas. " –California Health in All Policies Task Force.

" Health in All Policies is the policy practice of including, integrating or internalizing health in other policies that shape or influence the [Social Determinants of Health (SDoH)] …Health in All Policies is a policy practice adopted by leaders and policy makers to integrate consideration of health, well-being and equity during the development, implementation and evaluation of policies. " – European Observatory on Health Systems and Policies.

" Health in All Policies is an innovative, systems change approach to the processes through which policies are created and implemented. " – National Association of County and City Health Officials (NACCHO).

" Health in All Policies aims to improve the health of the population through increasing the positive impacts of policy initiatives across all sectors of government and at the same time contributing to the achievement of other sectors’ core goals. " – South Australia.

‘Health policy’ , thus, is both Healthy Public Policy and Health in All Policy, and may include public health policy and health care policy. Public health policy can be conceived either as public sector (government) policy for population health (public health policy) or any policy (including corporate and other civil society approaches) concerned with the public’s health (public health policy).

‘Health care policy’ in principle focuses on health care as the organised enterprise of curing or caring for disease, disability, and infirmity, and includes efforts at regulating and organising health care professions, pharmaceuticals, financing of the healthcare system, and access to healthcare facilities. Health care in essence is disease care [ 20 ] and at its core focuses on individual outcomes rather than population issues. This is potentially confusing as in most nation-states the healthcare system includes the public health system, although efforts have been made to separate the two, for instance in Canada with the creation of the (short-lived) Health Promotion Directorate following the publication of the Lalonde Report [ 21 ], and in Kenya with a ministerial public health and sanitation portfolio [ 22 ].

When the literature refers to ‘health policy’, it usually convolutes several of the above demarcations. Most often, the phrase ‘health policy’ will be used to talk about health care policy, i.e., when actually disease or healthcare policy is meant. Admittedly, health care policy research is already a dominant and powerful driver of developments in health political science, both in terms of the number of studies and in terms of the theoretical developments it yields. However, in its scope and impact, healthcare policy research is less interested in the politics of population health. In analysing the impact and outcome of health policy, therefore, any scholar should conscientiously delineate what s/he (a) considers ‘policy’ to be, and (b) considers as the scope of ‘health’. In this paper, we use the phrase health policy in a broader way to designate all government action to improve population health, i.e., Healthy Public Policy and Health in All Policy.

The policy process

Studying health policy requires an understanding of its development process. This is particularly important if we want to have an impact on the direction of policy and its framed health objectives. The application of theories of the policy process would enable an appreciation of the range of stakeholders and determinants of policy choice. Mackenbach [ 23 ] recently called for the further development of a ‘political epidemiology’ identifying the causal effects of political variables (structures, processes, outputs) on population health. In fact, the political sciences have developed a powerful toolbox of theories of the policy process framing these political variables (notably the work of Sabatier [ 24 ] with recent updates by Nowlin [ 25 ] and Schlager and Weible [ 26 ]).

Some of the theories that have been tried and tested include the event-driven Multiple Streams Theory empirically developed by Kingdon [ 27 ]; the Punctuated Equilibrium framework by Baumgartner and Jones [ 28 ], in which long periods of policy stability are alternated by general shifts in policy perspectives and ambitions; the Advocacy Coalition Framework [ 29 , 30 ] that emphasises the importance of coalition formation of camps of proponents and opponents to new policy directions; the Policy Domains approach coming from different perspectives on network governance [ 31 , 32 ]; and Social Movement Theory [ 33 ] arguing that disenchanted people will join social movements in order to mobilise resources and political opportunity to change public policy to their advantage. The scope of political science theory relevant to studying public policy and public policy change is even broader [ 34 , 35 ], ranging from hybrid approaches that mix these perspectives [ 25 ] or address specific processes such as coalition structuring [ 36 ].

We were keen to explore to what extent this body of theories of the policy process has made in-roads into health promotion and health education research [ 37 ]. The outcome of our systematic review was no less than disappointing: we identified 8,337 health promotion and health education research articles since the ‘healthy public policy’ rhetoric became mainstream in 1986, of which only 21 explicitly and conscientiously applied a political science theory. A systematic review of the use of ‘commonly identified policy analysis theories’ to the study of social determinants of health and health equity public policy arrived at similar results, with seven articles making use of such theories out of a total of 6,200 articles [ 38 ].

The importance of rigorous application of theory to solving social problems has been proffered by Birckmayer and Weiss in their Theory-Based Evaluation approach [ 39 ], and is a key doctrine for health promotion and health education development and evaluation [ 40 ]. The selection of an appropriate theory would provide answers to questions that ask why things are (not) happening beyond a mere description that they are (not) happening. A recent example of a policy issue that was investigated without the appropriate application of theories of the policy process was authored by Gonzalez and Glantz [ 41 ]. The authors record an extensive case study of a policy failure in The Netherlands. The country is a signatory to the Framework Convention on Tobacco Control and passed comprehensive legislation regulating all aspects of its MPOWER strategy ( M onitor tobacco use and prevention policies; P rotect people from tobacco smoke; O ffer help to quit tobacco use; W arn about the dangers of tobacco; E nforce bans on tobacco advertising, promotion, and sponsorship; R aise taxes on tobacco). In its implementation, however, The Netherlands failed to comprehensively ban smoking from all public drinking holes. Gonzalez and Glantz reach the conclusion that the legislative approach was unsuccessful because of " …poor implementation efforts and the failure to anticipate and deal with opposition to the law. " This is hardly a profound, or useful, political insight: " It didn’t work because it didn’t work. "

In a theory-based policy evaluation approach the authors might have made their assumptions of the phenomenon under study explicit and subsequently selected an appropriate theoretical framework. They may have already had some ‘gut feeling’ that policy implementation was to blame for the issue and applied a political science theory that claimed to identify relations between (Mackenbach’s) policy implementation structures, processes, and outputs. This may have led to the selection of Mazmanian and Sabatier’s policy implementation framework [ 42 ] – see below. Alternatively, they might have seen implementation failure as the result of a breakdown of governance arrangements between different policy levels and sectors, and selected, for instance, Hill and Hupe’s multi-level governance perspectives [ 43 ] to explain what went wrong, where, between whom and what, and how.

Assuming they would have selected the Mazmanian and Sabatier model (Figure  1 ) [ 42 ], this would have led to the careful operationalization of variables and data to be collected – rather than drawing on a fairly randomly selected collection of informants and media expressions. The conclusions, then, would have allowed for specific propositions as regards to the identification and management of the policy problem, the ability of the Dutch governments and its agents and structures to take measures leading to implementation, and measured descriptions of facilitators and barriers beyond the control of government that impact on the implementation process. One would assume that a carefully crafted methodology in which qualitative and quantitative approaches would supplement each other would yield a much more pointed analysis and conclusions that would provide evidence-based courses of action for policy entrepreneurs and smoking-or-health activists.

Variables involved in the implementation process (adapted from Figure  2 .1 in [ 42 ] ).

A similar theoretical naïveté can be observed in a recent, albeit slightly more astute, analysis of the determinants of tobacco excise tax in the USA [ 44 ]. The analysis is more astute as the authors find that ‘political’ determinants determine tax levels. That is, the level of tax is not dependent on economic considerations, but purely on ‘political characteristics’ – these being operationalised as Democratic-Mixed-Republican control of the executive and legislative branches of State government, governor time in office, and popular attitudes toward tax levels. The conclusion is that tobacco taxes in Republican states tend to be lower, and that there are many factors (and political variables) beyond the scope of the study. Should the recommendation to the policy entrepreneur and tobacco-or-health activist therefore be to join the campaign team of the Democratic Party for the next election? The answer, as Breton and colleagues have demonstrated for the tobacco control policy development in Quebec [ 36 ], is more complicated. In their description of the evolution of advocacy coalitions (based on Sabatier and Jenkins-Smith [ 30 ] and Lemieux [ 45 ]), they show how policy elites manage and manipulate events and pool resources, and tobacco control proponents break up emerging unification of opponent coalitions. Similar policy research, with foundations in Golden, Ribisl, and Perreira data [ 44 ], would potentially highlight vastly more astute political action to solidify and secure not just tobacco control but more broadly all health policy.

The stages heuristic and beyond

There seem to be a few barriers to the application of theories of the policy process to the health sciences in general. One is that few health scientists are trained in political science, and where they are, they do not seem to enter the health education and health promotion fields. Conversely, few students of public policy and public administration have taken an interest in health policy with the broad population and social determinant scope we described above. Most political science research is concerned with health care systems inquiry much more than with public health policy. Second, there is a lack of good benchmark studies that would set a standard for research applying theories of the policy process to public health policy, and consequently the kinds of superficial and uninsightful papers as discussed above find their way through editorial and peer-reviewed processes too easily. Third, we attribute the dearth of published studies inspired by theories of the policy process to a serious lack of (competitive) funding [ 14 ]. The proportion of grants devoted to public health is a fraction of the total medical research pool, and within the public health field funding for political research is virtually absent. Fourth, as Albert et al. demonstrated [ 46 ], members of health grant review panels do not regard social science research methods – and within that realm political science approaches – as a legitimate paradigm to study health matters. Fifth, the policy discourse in the health field is highly value-laden, intermingling debates about identity, equality [ 47 – 49 ], and – in the case of health care policy specifically – the role of technology and expertise [ 50 ], which clouds the legitimate application of the available evidence.

However, the two research examples given above highlight an issue that many health promotion and health education policy researchers seem to be struggling with most. This issue touches on the very nature of theories of the policy process. Theories applied in behavioural research are typically linear, at best with a feedback loop: a number of inputs (say, ‘attitudes’ and ‘beliefs’) are transformed through a number of conditioners (say, ‘social norm’ and ‘self-efficacy’) to produce intermediary (‘intention’) and final (‘behavioural’) change. In more complex behavioural systems there may be iterative and more incremental steps, and sometimes the models may take the shape of a cycle.

This, then, is also how policy development is typically modelled. Such a policy cycle can variably exist of as little as three steps (problem – solution – evaluation), four stages (agenda setting – policy formation – policy implementation – policy review) with as many as 15 sub-processes, to retrospective policy analyses that yield dozens of policy development instances, phases, and events.

All of these represent the policy process as displaying a curved linearity in which one stage –sometimes under conditions – leads to the next stage, just like the behavioural theories introduced above. While this representation of the policy process still permeates the health sciences – but also policy advice to governments [ 35 ] – policy students have now come to the realisation that policy making is a messy (some would say ‘wicked’) affair that does not neatly stick to stages.

It is not just that one stage or step coincides with another (for instance, the specification of policy alternatives may interface with the selection of policy instruments/interventions). In fact, often a step that comes ‘later’ in the stages heuristic in fact precedes an earlier phase in the cycle. A ‘real life’ example would be policy implementation. Implementation, as we have seen above, is driven by a wide array of contextual factors, including shifting power relations. Even when the policy problem is debated (as a first ‘agenda setting’ exercise), actors in the system implicitly, or by default, know that some implementation strategies will be impossible to develop. Regardless of how well-planned and analytical earlier stages in the policy process are, only certain types of interventions can be favoured. In a comprehensive review of the literature on policy instruments and interventions, Bemelmans-Videc, Rist, and Vedung formulate the ‘least coercion rule’ [ 51 ]: policy-makers choose the intervention that is least intrusive into individual choice of populations (as evidenced for obesity policy by, for instance, Allender et al. [ 52 ]). Thus, despite following the policy planning process conscientiously, the outcome in implementation terms favours communicative over facilitative or regulatory interventions. Steps in the cycle are therefore in reality rarely sequential or with feedback loops between sequential stages: often the process jumps a few steps ahead, to return to a previous step, or it finds itself going both clockwise and counter-clockwise for only sections of the cycle.

We were recently commissioned by WHO to develop a tool that would guide the development and application of Health in All Policies [ 53 ]. Through discussions with key stakeholders around the world we identified ten issues that need to be analysed and mapped in order to enhance the feasibility of Health in All Policies development. We drafted a Health in All Policies cycle (Figure  2 ) for discussion with Health in All Policies experts, showing both the clockwise and counter-clockwise sequential options for considering these options. The feedback on the figure demonstrated that the intuitive response to the graph was to diligently follow each of the stages, assuming there was a progressive logic to them. At the same time our panel agreed that the reality is that " everything happens at the same time ".

Proposed policy process cycle for developing Health in All Policies.

This is the essence of the critique that has been voiced by political scientist on the ‘stages heuristic’ [ 24 , 25 ] – that there is no causality between the different stages and therefore stages heuristic models defy theoretical testing mechanisms. The stages heuristic is useful as a mnemonic and an analytical visualisation of elements of the policy process, but does not describe the complex interactions within, between, and beyond its different features. Hassenteufel [ 54 ] furthermore argued that the analytical linearity of the stages heuristic clouds the symbolic nature of policy making in society as a sense-making activity rather than a purely methodical enterprise.We found that the best visual metaphor for this reality of the policy process is that of juggling (Figure  3 ).

Health in All Policies juggling process.

The juggling metaphor appears to ring true to policy entrepreneurs and activists at the coal face of policy development and change. It recognises that, although keeping all balls in the air virtually simultaneously creates an apparently hugely chaotic scene, systematic and disciplined action is required at all times. Juggling is decidedly not the same as the idea of policy making as a garbage-can process (most profoundly professed by March & Olsen [ 55 ]) – the application of theories highlighted above would aim at structuring and making sense of the logic, diligence, and structure of managing a chaotic process. Theory-led discussions between academics and practitioners have been suggested to work towards this end [ 35 ]. Is the ability to keep all balls in the air also predictive of policy effectiveness?

Assessing policy outcomes

Policies are formulated to address problems. In their ideal types, resources are allocated to develop evidence-based interventions and policy instruments and one would assume that, steeped in a validated body of knowledge, the policy will achieve its stated outcomes. However, as we have seen above, not all implementation strategies or policy ambitions are necessarily grounded in evidence. They follow the ‘least coercion rule’ [ 51 ]; are grounded in value-based rather than evidence-based policy ontologies [ 56 ]; are only symbolic to project an image of government concern [ 57 ]; or address a tangible yet insignificant element of the complexity of the real problem [ 58 ].

It is the responsibility of the policy analyst to expose such flaws through the systematic assessment of the policy process and its assumptions. Walt et al. [ 59 ] describe the multiple meanings and challenges in undertaking ‘proper’ health policy analysis. Following our argument above they contend that a conscientious, structured, and rigorous application of theories of the policy process to policy analysis is important. At the same time, however, the aims of policy analysis may be diffuse and its starting point should be to delineate its purpose. Paraphrasing a policy analysis training manual by the United Nations Environment Programme [ 60 ], the causal and final chains of drivers and consequences of policies and their contexts are hard to map, and many policies fail to include specific performance criteria or direct intervention parameters. Setting the boundaries of a policy analysis therefore becomes a negotiated process between many stakeholders, for which Pawson and Tilley [ 61 ] suggest a ‘realist’ approach that recognises the uniqueness of each policy issue and context. In showing policy ‘effectiveness’, evaluators therefore focus on intermediate policy effects rather than end-point health impact.

Case study: environments for health policy research – Environments for Health (E4H) policy effectiveness

In 2001, the government of the Australian State of Victoria adopted its E4H policy framework [ 62 ]. It connects with legislation that requires local governments in the State to develop Municipal Public Health Plans (MPHPs). E4H provides evidence-based guidance for the development of local policy that addresses social and environmental determinants of health in the overlapping domains of the social, built, economic, and natural environments. E4H explicitly embraces a social model of health, and the policy package provides local government with a comprehensive evidence base, capacity building for local health bureaucrats and communities, and exemplars of policy action.

Five years after adoption, the Victorian Department of Health commissioned an evaluation into E4H policy effectiveness. The evaluation objectives were to assess the extent to which the E4H Framework had:

 Been incorporated by local governments in their policies and practices;

 Contributed to greater consistency and quality in the scope and approach of municipal public health planning across the state;

 Led to the integration of MPHPs with other council plans;

 Increased the level of understanding among appropriate local government staff of the impact of the social, economic, natural, and built environments on health and wellbeing;

 Created additional opportunities for health gain through strengthened intersectoral partnerships to address the social determinants of health; and

 Been supported effectively by the Department of Human Services and other stakeholders [ 63 ].

The evaluation objectives were the outcome of negotiations between a range of stakeholders, including the Department of Human Services, local governments, and research sector representatives. The consequence was that hybridization of a number of political theories was required in a realist evaluation framework [ 61 ], notably policy diffusion theory [ 64 ], implementation theory [ 42 ], and Multiple Streams theory [ 27 ]. The resulting methodology drew on a range of data collection strategies:

 Document analysis of Victorian Local Government Authorities’ MPHPs (62 plans);

 Seventy-three individual and group interviews with key stakeholders in municipal public health planning;

 Online survey of individuals involved in municipal public health planning (councillors, council staff, non-council organisations, and community members) (108 survey respondents);

 Five community forums to present preliminary evaluation findings and obtain input from additional stakeholder groups.

In summary [ 65 ], the evaluation found that E4H had substantially changed the way local governments think about health; improved the way local governments plan for health; and started sectoral integration. However, developing a MPHP was frequently seen as a – statutorily required – means in itself, and implementation was often lagging. The Department of Health consequently launched programmes for implementation knowledge co-creation, capacity-building, and networking at the local level, case models for – especially economic – E4H development, and political skills.

Conclusions

Determining the evidence of effectiveness of policy change for health is an art and a science that is still in its infancy. A systematic and theory-driven approach needs to be applied. In this paper we have demonstrated that insights from political science would allow for better and more profound insights into the reasons why and how policies fail or succeed. This is a perspective that transcends a current tradition merely describing failure or success of policy initiatives.

Our empirical material shows that policy research, assessment, and analysis needs to be a negotiated process between stakeholders that is seemingly chaotic, but in reality must be driven by the appropriate – and often hybrid – application of theories from the social sciences, notably political science.

A conscientious and transparent approach to determining what policy is and entails is a critical starting point for the further development of this field. It is recognised that such a determination is frequently impossible as even policymakers, policy entrepreneurs, and decision makers themselves are deliberately equivocal about what they pursue – the eminent economist John Maynard Keynes pointed at the need to keep options open as long as possible by writing " There is nothing a Government hates more than to be well-informed; for it makes the process of arriving at decisions much more complicated and difficult " [ 66 ]. It is the responsibility of public health policy analysts to expose any efforts at purposely obscuring the strictures of policy making. Good scholarly process, rigour in research, and theory-based evaluation, should enable us to do exactly that.

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de Leeuw, E., Clavier, C. & Breton, E. Health policy – why research it and how: health political science. Health Res Policy Sys 12 , 55 (2014). https://doi.org/10.1186/1478-4505-12-55

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The aim of non-interventional studies, a type of study in which patients receive the marketed drug of interest during routine medical practice and are not assigned to an intervention according to a protocol, is to uncover insights that may be inaccessible through controlled trials. Non-interventional or observational study designs can play a crucial role in assessing treatment effects (i.e., causality) beyond the confines of traditional randomized controlled trials (RCTs). Within these study designs, routine clinical care outcomes are observed among real-world populations, as opposed to research participants of RCTs selected according to narrow inclusion/exclusion criteria. Real-world data (RWD), derived from sources such as electronic health records, claims data, and registries, offers a less constrained environment that better reflects the complexity and diversity of clinical practice. Additionally, real-world studies typically have much larger sample sizes, facilitating subgroup analyses often infeasible in RCTs. Subgroups, in this case, describes an analysis unit of a subset of participants within a given study population. This nuanced understanding can inform health care decision-making by capturing real-world outcomes, patient variability, and long-term effects of interventions observed as part of regular clinical care.

Real-world evidence (RWE) complements RCTs by providing timely insights into effectiveness across diverse populations beyond traditional clinical trials. Regulatory initiatives, such as the U.S. Food and Drug Administration’s (FDA) Advancing Real-World Evidence Program, acknowledge the value of RWE, aiming to modernize evidence generation and incorporate patient perspectives. However, ensuring the credibility of RWE for causal inference requires clear design, fit-for-purpose RWD, communication, and rigorous statistical analysis. Promoting RWE’s capacity for causal inference is essential for advancing evidence-based health care. Regulators recognize that certain limitations accompany the use of RWD to determine or measure causality. Proposed approaches might involve established concepts like target trial emulation and/or other causal frameworks to address confounding and other types of bias and schemas to describe overall study designs. Integrating RWE’s strengths with traditional research methods like RCTs can present a more comprehensive understanding of health care interventions and their real-world impacts.

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Change and Innovation in Healthcare: Findings from Literature

Frida milella.

1 IRCCS Istituto Ortopedico Galeazzi, Milan, Italy

Eliana Alessandra Minelli

2 University Carlo Cattaneo - LIUC, Castellanza, Italy

Fernanda Strozzi

Davide croce.

3 School of Public Health, Faculty of Health Science, Witwatersrand University, Johannesburg, South Africa

4 Centre for Health Economics, Social and Health Care Management, University Carlo Cattaneo - LIUC, Castellanza, Italy

Change is an ongoing process in any organizations. Over years, healthcare organizations have been exposed to multiple external stimuli to change (eg, ageing population, increasing incidence of chronic diseases, ongoing Sars-Cov-2 pandemic) that pointed out the need to convert the current healthcare organizational model. Nowadays, the topic is extremely relevant, rendering organizational change an urgency. The work is structured on a double level of analysis. In the beginning, the paper collects the overall literature on the topic of organisational change in order to identify, on the basis of the citation network, the main existing theoretical approaches. Secondly, the analysis attempts to isolate the scientific production related to the healthcare context, by analysing the body of literature outside the identified citation network, divided by clusters of related studies.

Methodology

This review adopted a quantitative-based method that employs jointly systematic literature review and bibliographic network analysis. Specifically, the study applied a citation network analysis (CNA) and a co-occurrence keywords analysis. The CNA allowed detecting the most relevant papers published over time, identifying the research streams in literature.

The study showed four main findings. Firstly, consistent with past studies, works reviewed pointed out a convergence on the micro-level perspective for change’s analysis. Secondly, an organic viewpoint whereby individual, organization and change’s outcome contribute to any organizational change’s action has been found in its early stage. Thirdly, works reported change combined with innovation’s concept, although the structure of the relationship has not been outlined. Fourth, interestingly, contributions have been limited within the healthcare context.

Human dimension is the primary criticality to be managed to impede failure of the re-organizational path. Individuals are not passive recipients of change: individual change acceptance has been found a key input. Few papers discussed healthcare professionals’ behaviour, and those available focused on technology-led changes perspective. In this view, individual acceptance of change within the healthcare context resulted being undeveloped and offers rooms for further analyses.

Introduction

Healthcare organizations are in an ongoing state of change forcing to convert themselves incrementally or in radical ways. 7 , 65 Organizational change is defined as the ‘change that involves differences in how an organization functions, who its members and leaders are, what form it takes, and how it allocates resources’. 32

Organizational change constitutes a complex phenomenon that develops in any sector. Change in the specific field of healthcare “requires a vision and understanding of the core functions of the system and infrastructure supporting those core functions”. 29

Accordingly, the paper is built upon two sequentially levels of analysis. First, the paper collects the overall scientific production concerning organizational change topic basis on the citations network. This allows for outlining main ongoing theoretical developments and detecting emerging research strands. This preliminary step is critical to gaining an insight into the depth of scientific production in the healthcare context. Second, the work groups additional contributions extant in the literature but not included in the citation network. The analysis is accomplished by selecting papers based on the occurrence of author keywords within the original set of retrieved papers. Thereby, this stage of analysis draws further conclusions on the existing body of knowledge concerning to organizational change in the healthcare context.

Specifically, the paper addresses the following research questions:

 RQ1: What are the current streams of research on change management?

 RQ2: What is the state-of-the-art of change management in the healthcare field?

A quantitative-based method, called “Systematic Literature Network Analysis (SLNA)”, introduced by Colicchia & Strozzi (2012), that employs jointly systematic literature review and bibliographic network analysis is adopted to carry out the two-stage of analysis. The dynamic perspective, which the method provides, eases the detection even of literature gaps not considered to date in the existing body of research production, due to the heterogeneous contributions.

State of Art in Healthcare

Healthcare organizations, described as “professional bureaucracy”, 40 deserve a specific focus.

Consistent with Harney and Monks (2014), 28 hospitals’ organization is characterized by a particular model: the whole arrangement draws upon the power of its high-skilled employees who are in charge to fulfil operational tasks in a professional and specific way. 4 Andreasson et al (2018) 2 observe that, in such a setting, the individuals and teams’ autonomy 53 enables them to operate into an environment where their knowledge and professional skills guide decisions.

Thereby, medical professionals can manage their patients without considering their peers throughout their activities. 24 , 40 This control over their work is partly offset by the so-called collegial influence 13 – based on professional credibility 43 - further considering that physicians pursue professional norms, work standards and institutional scripts provided externally the organization’s structure. 2 Concerning the autonomy of physicians, clinical judgment must be unrestricted due to the complexity of their job and the challenges of measuring outcomes. 33 As a result of this, managers could not handle the medical problem-solving process since they lack knowledge and skillset developed by long periods of training, apprenticeship, and socialization. 33 Such uneven allocation of power – managers – and knowledge – professionals – could determine tension between them. 49

In such perspective, professional bureaucracy organizations fulfil the function of sustaining the necessities of the professionals, who lead “decision-making on a day-to-day basis”, 12 rather than vice versa. 53 More specifically, in hospital environments, administrators are not involved in physicians’ clinical decisions 33 that aim towards patients’ needs. 1 , 36

Enshrined within this approach, it is clear that managers have to negotiate, seeking to be consistent with the organization’s culture, avoiding imposing working programs, procedures and rules. 27 Accordingly, Andreasson et al (2018) 2 observe that independent professionals and strategic leaders have to jointly approve proposed changes.

Hence, professional bureaucracy has developed drawing upon a bottom-up decision-making arrangement. 2 Striving to yield standardized outputs, the inverted power structure, 13 on the one hand, is conceived as rigid, on the other, is resistant towards the change. 40 Therefore, Andreasson et al (2018) 2 consider professional organizations based on professional workers’ authority “rather than on top-down steering”.

Consistent with Mintzberg (1983), 40 managing such an organizational configuration implies facing three distinct managerial issues. Firstly, as aforementioned, discretion might lead the focus away from the patient’s and organizational needs. 33 Secondly, fitting stable environments, professional bureaucracies tend to render “processes as predictable and routine as possible”: 33 thereby there are barriers to innovate in such a context.

Finally, the problem of coordination occurs due to a considerable autonomy that impedes managers to pursue efficiency and effectiveness of care processes’ coordination. 33

To this respect, what should be considered is the role of the professional community in healthcare organizations. The healthcare organizations can be considered as change-resistant due to the greatly fragmented essence of these organizations (namely numerous professional tribes) and the professionals’ power to block change in this sector in so far as not involved in the change process. 19 , 44 Thus, organizations with a high content of professional autonomy require a definition of the problems and actions to implement organizational changes that are not defined exclusively by the highest levels of management.

Health professionals cannot be equated with passive recipients of change because the lack of involvement would lead to considering the suggested solutions “as being poor fit with the local practice at hand”. 18

Materials and Methods

The data used in the paper were collected from Scopus database that provides coverage around 60% larger than the one of Web of Science. 56

At the beginning, related to the topic, the set of chosen keywords does not include specific terms. The multifaceted nature of the investigated subject and the purpose to obtain a comprehensive state of the art suggests performing a search strategy based on two of the most comprehensive author’s keywords, “change management” or “organizational change”.

Based on PRISMA flow diagram, 41 the selection of papers concerned contributions in subject areas ranging from “Business, Management and Accounting” to “Engineering, Social Science and Health Professions” and the search performed in early January 2019, included only articles or conference proceedings published in the last 10 years (2009–2019), with an output of 1968 documents. The query was performed as displayed below in Figure 1 .

Flow chart of the search strategy.

SLNA method contains the analysis of bibliometric networks based on the paper retrieved, such as citations and keywords analysis, as one of its components (Strozzi et al, 2017). In the following, Citation Network Analysis (CNA) and co-occurrence keywords analysis have been detailed.

To build the network two software packages were used: Vos Viewer and Pajek.

Vos Viewer ( http://www.vosviewer.com/ ) is a software tool for creating and displaying bibliometric networks. Vos Viewer was adopted for the preliminary analysis, in terms of network visualization, for creating the input file for Pajek, and for implementing the analysis of the keywords. Pajek ( http://vlado.fmf.uni-lj.si/pub/networks/pajek/ ) is a software tool for network analyses and, in this work, is employed for displaying and discussing the results of a citation network.

Citation Network Analysis (CNA)

CNA is a method based on citations, which are the links between papers (nodes) in a citation network. The isolated nodes cannot be involved in the analysis, and the citation analysis can be performed only when components are connected. 51

The first step in performing network analysis is extracting the isolated nodes, uploaded in VOS Viewer software. The bibliometric network showed only 1284 documents out of 1968 that received at least one citation, displayed in the Pajek tool. Firstly, the bibliometric network was adjusted by changing the direction of knowledge flow (ie, inverting the direction of arrows from cited to citing papers, that is, from the oldest paper to the most recent one). Secondarily, the analysis revealed that only 840 out of 1284 documents were connected.

CNA connected components in this network were 4. The first component included 353 papers, whilst the remaining components were composed of 26, 10 and 4 papers, respectively. Given the small size of the last identified components (ie, 26, 10 and 4) compared with the first one (ie, 353 papers), only the component with 353 nodes was analysed.

Figure 2 shows the first biggest connected component. In order to gain the backbone of the research line related to a group of connected paper, by recognizing the most relevant ones published over time, 11 , 37 , 51 the so-called “main path component” 37 was extracted. The main path enables to detect the main trend in the development of the research line’s contents, by calling attention to the papers based on prior articles which take on the role of hubs to the next ones. 51

First biggest connected component.

The quantification of the transversal weight of the citation was executed. The method “Search Path Count” allows considering all the paths deriving from each source (ie, a paper that does not cite any other) to each sink (ie, a paper not receiving citations by others).

A cut-off value of 0.5 was set (the default value) to eliminate all arcs having a lower value in the original citation network and to obtain the most relevant connected component. Figure 3 depicts the main path for the biggest connected component.

Main path of the first biggest connected component.

To outline a framework as comprehensive as possible on the subject, only the use of citations to trace the coordinates can be limiting. Some papers are not included in the analysis because other ones did not cite them, despite their contents were significant or they may not be selected since they were published recently, therefore they did not still receive a sufficient number of citations. This suggests that the CNA should be combined with other tools such as the Global Citation Score analysis and keyword analysis. 51

In the following, the citation network analysis is designed to trace the active research streams on the topic of organizational change and to have a preliminary assessment of the extent to which these patterns are present even among the studies dealing with organizational change in the healthcare field. In this view, a first-order analysis based on the main path associated with the biggest connected component may be useful to detect general streams and gain an overall picture. The main path sheds light on the articles that refer to prior papers, which act as hubs concerning later works.

Keywords Analysis

Global Citation Network Score Analysis is a tool to detect seminal or recent breakthrough studies 51 that were not selected in the citation network but received a significant amount of citations in the whole Scopus Database. In that sense, these works are however relevant in the field.

Co-occurrence analysis assumes that the authors’ keywords of a paper may be considered a synthetic descriptor of the content but also a reference for detecting linkages among issues analysed. 51 Therefore, the co-occurrence around the same word or pair of words may point out a research subject or trend in a specific field. 14 The tool allows to also consider the papers not having received citations nor citing others, ie, the isolated nodes of connected components. 9 In this work only the author keywords networks 14 will be performed.

Figure 4 shows the co-occurrence network of authors’ keywords obtained from the original database (1968 papers). The network was built by accounting for a minimum threshold of keywords’ occurrence equals 9 (ie, keywords that appear together at least 9 times).

Co-occurrence network of authors’ keywords.

Co-occurrence keywords analysis detects a cluster of contributions previously excluded as not having received citations nor having cited other authors’ papers. Therefore, this stage contributes to a complete preliminary understanding of which literature strands are being developed on organizational change topic within the healthcare field.

The Main Path of the First Biggest Connected Component

The core subject investigated refers to the role of individuals in implementing change, by focusing on the “individual change acceptance”. 67 Several papers 3 , 23 , 25 , 26 , 34 , 35 , 45 , 52 previously published already started adopting “micro-level perspective on change”. 65

A first research stream dwells on the factors enabling individuals to be prepared for specific change initiatives. Normative-reeducative change strategies and work environment steering towards learning culture demonstrate to be facilitators. 65 Readiness for organizational change is accomplished when individual attitude perceives change action as a necessary step and likely to be successful. 65 Therefore, readiness for organizational change is viewed conceptually similar to Lewin’s notion of the unfreezing step. 3 , 16 The group is limited to 5 papers ( Table 1 ).

Summary of Results Obtained by Citation Network Analysis

A second literature flow deepens personal beliefs that individuals develop about change initiatives. Personal appraisals about individual ability to face change actions, ie, “change self-efficacy”, 30 is referred to being factors making individuals more likely willing to accommodate and accept the change. 65 Individual’s pessimistic viewpoint about management ability to be effective in change implementation, ie “cynicism about organizational change”, 55 may jeopardise organizational change accomplishment, 47 as well as the middle managers’ strategy commitment. 63 The group contains 4 papers ( Table 1 ).

The third flow of literature proposes the adoption of a multi-level approach to organizational change and places emphasis on the change outcomes. Merging the individual-focused micro perspective and the organizational-oriented macro perspective, with inflows from meso-level theory 68 may contribute to obtaining a comprehensive vision on organizational change. Change type and change method should be converging to attain the intended change outcome. 58 The group contains 4 papers ( Table 1 .

Consistent with past studies, this step of literature review through CNA shows that works emphasized the need to give emphasis on individual perceptions towards change. The research trajectory appeared to be unexplored in healthcare. Interestingly, a comprehensive framework involving micro-meso and macro perspective to evaluate change actions and the importance of change outcome was found to be emerging trends only in the general literature on organisational change.

The use of keyword analysis is intended to confirm or to extend this initial finding on existing research streams related to the topic of organisational change in healthcare.

Clusters from Keywords Analysis

The first cluster includes approaches to manage change organization within the production context, 91 by illustrating applications in terms of product development 85 and impact on supply chain management. 83 The cluster is composed of 26 papers.

The second cluster reports supportive tools for change management, by emphasizing the importance of formal and informal communication to promote employees’ commitment to change. 75 The cluster is mainly composed of 7 papers.

The third cluster enlarges supportive and boosting tools of organizational change, containing IT applications such as a monitoring system for organizational development activities, 96 team-based simulations improving readiness for change in university setting, 73 and as a means for gaining business-IT alignment. 77 The cluster is mainly composed of 6 papers.

The fourth cluster encompasses the key role of participation for learning within change, 107 even debating a mix of learning styles to sustain successfully organizational change initiative in the healthcare context. 92 The cluster is mainly composed of 5 papers.

The fifth cluster copes with the performance management issue, by soliciting a change in organizational values to enhance a successful performance management reform. 82 Performance issue in the healthcare context is viewed as an outcome after the organizational change process. 76 Change management’s research address the related performance management issue, but the papers reviewed do not offer structured models or approaches. This is consistent with the result debated in the citation network analysis. The cluster is mainly composed of 6 papers.

The sixth cluster focuses on sustainability change initiatives in Higher Education Institutions. 80 Corporate sustainability issue is even addressed to pinpoint the effects of applying sustainability change efforts. 74 The cluster is mainly composed of 8 papers.

The core of the seventh cluster appears to emphasize the dual nature of change, including organizational and technological aspects (eg, 81 , 84 ), and suggests the need for an in-depth analysis on who has the “role of enabler” in change initiatives. This step was already addressed in the citation network analysis, where Choi and Ruona (2011b) 66 quote Rogers (1983) 48 and Rogers (2003) 49 for “the importance of readiness for change through the innovation-decision process model”. The cluster is mainly composed of 9 papers.

Within the eighth cluster, a first subject investigates the factors affecting physicians’ behaviour in technology-driven changes, assuming that clinicians’ beliefs on technology-induced improvements of patients’ care play a critical role. 93 Scholars address the issue in light of the theory of planned behaviour, 93 or by proposing an ad hoc framework where an impact assessment of individual acceptance should be a step before introducing new IoT technology in workflow. Debate on the individual behaviours involved in healthcare organizational changes points out individuals factors such as “personality, social identity and emotional intelligence” 105 influence coping strategies’ choice to tackle change-related stress, as complementary perspective.

A second related subject focuses on the managerial approach to change, revealing that, on one hand, unclear supporting methods by seniors managers may weak middle managers’ change activities, 88 on the other hand, for hospital managers, fully physicians’ involvement in technology-driven changes should impact positively on physicians’ attitude. 93

The relationship between innovation and change in the healthcare context should be explored. Both external and internal factors trigger the need for change in healthcare organizations. For instance, the current epidemiological and demographic transition is provoking a shifting of care’s need towards users affected by chronic diseases. This is leading to a compulsory changing in the healthcare organizational framework. Likewise, the need to make health processes more efficient, for instance, forms another triggering factor, the inside one, for organizational change. Therefore, the organizational change issue should be investigated by bearing in mind these multiple boosts to changing. This supports the need to investigate deeply the concept of change and innovation in a healthcare setting, by seeking to outline the boundaries of organizational change and innovation. In particular, the analysis should start investigating the issue by emphasizing on the fact that micro-context should not be assumed simply as a backcloth to action. 15

The resistance to organizational change initiative arises when professional logic comes into contrast with the management one. 18 In this regard, the future research should investigate the effect of a “local ownership” 18 of the problems behind the change in order to be recognized as relevant critical issues in the organizations by the professionals. Thus, it becomes a priority to seek a new concept of leadership where the recipients of the change can themselves be those who manage the leaders with the possibility to hinder or sustain proactively their leadership. 18 Healthcare organizations are moving towards multifaceted systems. As the work by Augl (2012) 76 pointed out in cluster number 5 of keyword analysis, the health system might be regarded as a set of social systems where organizations may be considered as communication systems. In this regard, the author suggested a new approach to change management by modifying the current communication paths to contextual collaboration. 76 Integrated systems need three pillars as institutional integration (ie, laws), management integration (ie, operational tools) and professional integration (ie, team), which are not mutually exclusive. 6 The cluster includes 31 documents.

Tables 2 and ​ and3 3 display the 8 clusters obtained by VOS (Visualization of Similarities) clustering technique.

Clusters (1-4) Obtained by VOS (Visualization of Similarities) Clustering Technique

Clusters (5-8) Obtained by VOS (Visualization of Similarities) Clustering Technique

Two contexts emerge clearly from the analysis.

The manufacturing context and the healthcare context. The former analyses the issue of organisational change also concerning supply chain management; the latter pays attention to the attitude of the clinician towards change initiatives linked to the introduction of new technology. Of the remaining clusters, some of them relate the topic of change to the adoption of support systems (IT applications – cluster 3) or support strategies (formal and informal communication – cluster 2; participation – cluster 4) for the implementation of change; further clusters tackle the topic of change as a tool to improve performance management (cluster 5) or combine it with sustainable change initiatives and the concept of innovation.

The keyword analysis shows that the general literature streams obtained in the previous CNA analysis are not yet developed in the healthcare context, although interest in the individual’s attitude to change seems to be an emerging approach.

The Importance of Individuals in Organizational Change

With the analysis carried out so far, a growing interest in the most recent literature on the individual-change relationship emerges (ie, 66 ). The subject is developed by scholars from different perspectives. Some authors focus on the psychological mechanisms that induce the individual to change, deepening the individual perception of change both as a skill that the individual recognizes inadequately pursuing a specific change initiative (ie, 30 ), and as the personal belief on the management’s ability to properly implement a change initiative (ie, 66 ). Furthermore, the literature analysed warns that the individual-organizational change relationship is a broad and articulated subject, which cannot be confined to “change recipients” only, but which deserves adequate study also concerning to the “change agents” themselves (ie, 63 ).

The contributions discussed in this paper clearly define the need to deal with acceptance of change from the perspective of the individual. What the general literature on the subject seems to offer, however, is a reading that does not allow linking the individual’s attitude towards change to the specific organizational context in which the change itself will be implemented, especially in the case of complex organizations. Martínez-García and Hernández-Lemus (2013) 38 recognize for example that

health systems are paradigmatic examples of human organizations that merge a multitude of different professional and disciplinary characteristics in a critical performance environment.

The extensive analysis reported on the topic allows contextualizing the organizational change initiatives in the healthcare world, where the individual-change relationship is central and can offer additional ideas on the profile of change recipients.

The research line takes a position on change recipients, by paying attention to the effects that organizational change causes on persons or, in other words, on the psychological aspects of the organizational change. 68 A unified framework of organizational change perspectives (ie, micro, meso and macro), to connect jointly the individual change acceptance to economic and sociological perspectives, 68 is missing, except one work. 68

Change outcome and organizational performance in change initiative appear to be not adequately explored. The work (see 58 ) illustrates only conceptual models. Studies aimed at identifying and testing empirically specific performance measures in the organizational change context appear to be missing.

Moving to the “second-order analysis”, based on co-occurrence keywords analysis, the results confirm and extend the preliminary understanding provided by the citation network analysis. A summary of the results is provided in the table number 4 ( Table 4 ). Cluster 8 provides some insights on the state of art in the healthcare research field. Beyond case studies, the topic becomes relevant only relative to the spreading of digital services in the care system. Other studies (eg, 62 ), retrieved in the previous step, describe a potential stream of organizational change issues in the healthcare context. Notably, these works address change management only concerning the negative health impact for the individual, without paying attention to the individual behaviour change. Moreover, the papers available do not point out change management in the specific context of professionalized organizations. Therefore, studies aimed at investigating the nature of change that characterizes the healthcare professionalized organizations are needed.

Summary of Results Obtained by Co-Occurrence Keywords Analysis

In summary, the literature reviewed informed us that three potential streams were not yet fully explored. Change management in the context of healthcare organizations, performance evaluations and innovation-organizational change relationship was the most evident gaps found out.

Nevertheless, the present work debates individual-level perspective on the change as a prominent dimension to tackle in designing change initiatives, albeit individual and organizational issues related to change should not be viewed as detached. This stimulates to set aside a polarized perspective on organizational change.

The performed review traces a clear step in the production research on the subject. The findings suggest that literature is seeking to overcome a traditional duality approach between “managerial change agent (the good) and resisters to change (the bad)”, 5 , 22 , 56 by paying attention to the critical role of attitude towards organizational change. Especially in the healthcare context, the literature reviewed highlighted an evident imbalance of scientific production in favour of individual effects of changing. This would be consistent with the literature stream identified, which has been moved to an integrated perspective in the organization’s vision during a change management initiative.

Technology and organization appear to be a double face of the change, being strictly related, but there is not a common perspective in defining the role of enabler for those variables. In this respect, further research should address the above-mentioned issue in the organizational change context.

Likewise, a specific investigation on organizational change and the healthcare field is encouraged. Healthcare organizations ought to adopt change models fitting their specific needs of change. Overall literature stream traces a systemic perspective, whereby an individual, organizational and expected outcome of change should be milestones of any organizational change action.

Healthcare organizations receive multiple external and internal stimuli of change.

The increasing dominancy of chronic diseases is forcing to shift the care gravity’s centre on the patient, by modulating the processes of providing the services according to the user and his changing needs. 21 , 31 The availability of new health technologies is changing the way through which health organizations offer services and deliver values (eg, e-health). New technologies are speeding up the demographic changeover and are increasing the economic burden for the NHS. 10 Health organizations are transforming their organizational models, eg, collaborative networks; 8 integrated hospital-local care; 39 , 42 sharing services 17 for reducing administrative costs. 51

The converging outcome lies on strengthen the equity, the value and the sustainability of healthcare.

In this regard, starting from the micro-level analysis, professionals needs’ integration with the organizational design and the individual technology acceptance should be pursued. Exploratory studies may be useful.

Research on change management is gaining momentum and offering many stimuli. Therefore, the development of research lines to deepen the topic is important, especially in the healthcare field.

The authors report no conflicts of interest in this work.

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Closing Gaps in Data-Sharing Is Critical for Public Health

Updated federal strategy could also ease burdens on agencies, providers.

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Every day, public health officials use data from each other and from doctors, hospitals, and health systems to protect people from infectious and environmental threats. When these officials receive timely, accurate, and complete information from health care providers, they can more clearly detect disease, prevent its spread, and help people connect to care. To improve the quality of this information, the U.S. Centers for Disease Control and Prevention developed the Public Health Data Strategy (PHDS), which was updated in April, to facilitate data-sharing between these many stakeholders. As the director of the CDC’s Office of Public Health Data, Surveillance, and Technology, Dr. Jennifer Layden is responsible for leading, coordinating, and executing the strategy.  

This interview has been edited for clarity and length.

What is the Public Health Data Strategy?

It’s CDC’s two-year plan to provide accountability for the data, technology, policy, and administrative actions necessary to meet our public health data goals. We aim to address challenges in data exchange between health care organizations and public health authorities, moving us toward one interconnected system that protects and improves health.

And what are the main goals of this effort?

The PHDS has four main goals: strengthen the core of public health data; accelerate access to analytic and automated solutions that support public health investigations and advance health equity; visualize and share insights to inform public health action; and advance more open and interoperable public health data. The plan sets milestones that help public health partners, health care organizations and providers, and the public understand what’s being done and what progress is being made toward these goals.

What barriers does the strategy aim to address?

Electronic health care records (EHRs) and associated efforts at interoperability [the successful exchange of health information between different systems] have seen over $35 billion of investment over the last couple decades. This has led to robust and widespread use of EHRs , adoption of health IT standards , and improved data-sharing across health care. Public health, however, hasn’t seen the same investment. And this has contributed to gaps in the completeness of data and the timely exchange of information to support public health.

Can you share an example of these gaps?

At the beginning of the COVID pandemic, we had race and ethnicity data on less than 60% of cases. New investments in public health, largely tied to the COVID response, allowed for advanced connectivity with the use of electronic case reporting, or eCR [the automated electronic reporting of individual cases of illness], as well as electronic laboratory reporting [the automated sharing of lab reports]. This led to a rapid improvement in the completeness of race and ethnicity data, which improved the nation’s ability to identify disparities in COVID burden and severity.

As we work to transform public health systems, we need to leverage existing health IT standards and technical approaches to ensure better connections between public health and health care. This benefits us all through more streamlined data-sharing, reduced burden on health care facilities and providers, and faster detection of health threats and outbreaks. And ultimately, improved bi-directional data-sharing [where data is available to health care providers who generate the information and health departments that receive the data] will benefit patients and those who care for them .

What progress have you seen so far?

The PHDS was launched in 2023 with 15 milestones, such as increasing the number of critical access hospitals sending electronic case reports as well as increasing the number of jurisdictions inputting eCR data into disease surveillance systems. Twelve were met , and work continues on the remaining three. The milestones reached in 2023 have made it easier to share information, provided access to modern tools, and improved the real-time monitoring of health threats, all of which strengthened public health data systems. The latest version of the PHDS includes updated 2024 milestones as well as new ones for 2025 that will advance the nation’s public health data capabilities. Milestones for the next two years focus on improving the completeness and coverage of eCR, syndromic surveillance [which uses anonymized emergency room data to identify emerging threats quickly], and data on mortality and wastewater. [When wastewater contains viruses, bacteria, and other infectious diseases circulating in a community, it can provide early warning even if people don’t have symptoms or seek care.]

How will the strategy make it easier for public health agencies and health care to share data?

Collaboration is at the heart of the new milestones. The updated strategy focuses on accelerating the adoption of eCR to ensure timely detection of illnesses, expanding data-sharing initiatives to improve public health responses and decision-making, and driving innovations in analytics to address health disparities and promote health equity.

These new milestones aim to reduce burdens on public health agencies by reducing the need to manually input case data into disease surveillance systems and will mitigate the overhead for managing individual point-to-point connections with labs to support eCR. The strategy will also let public health agencies more effectively identify and address health disparities based on a wider range of health equity measures.

In addition, the Workforce Accelerator Initiative, launched by the CDC Foundation, will recruit, place and support more than 100 technical experts in public health agencies to achieve the strategy’s goals.

What other partners will be engaged to accomplish the strategy?

Successful implementation will require collaboration with public health agencies, public health partners, private industry, health care partners, and other federal agencies, as well as sustained resources. We will directly engage with public health agencies to understand their priority needs and work with public health partners to support their progress toward key milestones. We’ll also collaborate with private partners to encourage dialogue and promote data exchange pilots, as well as with providers and labs to gather feedback on how we can better support their progress.

The CDC is working with the Office of the National Coordinator for Health Information Technology (ONC) to create a common approach for data exchange among health care, public health agencies, and federal agencies. This effort involves a partnership with representatives from health care, health IT, states, and federal organizations that sets up an exchange system to make it easier for providers to send data to public health agencies and for public health agencies to receive it. The collaboration will provide data standards, common agreements, and exchange networks that will assist public health agencies in their data exchange needs. We’ll continue to collaborate with ONC, as well as the Centers for Medicare & Medicaid Services, to advance a shared understanding of activities that support our milestones and will reach out to other federal agencies to synergize our efforts.

What will success look like?

We have ambitious goals to strengthen the connections between public health and health care. And other federal initiatives, like the movement toward the Trusted Exchange Framework and Common Agreement (TEFCA), adoption of USCDI+ , and new data standards lay out a pathway to making this a reality.

In five years, we aim to have 75% of state and big city jurisdictions , along with CDC, connected to TEFCA. This can eliminate inefficient point-to-point interfaces and enable more reliable exchange of real-time information. We also want to have 90% of emergency room data connected and flowing to public health agencies and envision a future where eCR has replaced most manual reporting of cases of infectious diseases and other conditions.

And big picture, what would this accomplish?

Reaching these goals would mean having more complete data and faster reporting of threats that could put our nation at risk. This will lead to better detection of outbreaks, faster response times, and healthier communities—and ultimately result in an integrated public health ecosystem that produces and uses data to support healthier communities and keep people safe.

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ANTI-SEMITIC ATTITUDES OF THE MASS PUBLIC: ESTIMATES AND EXPLANATIONS BASED ON A SURVEY OF THE MOSCOW OBLAST

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JAMES L. GIBSON, RAYMOND M. DUCH, ANTI-SEMITIC ATTITUDES OF THE MASS PUBLIC: ESTIMATES AND EXPLANATIONS BASED ON A SURVEY OF THE MOSCOW OBLAST, Public Opinion Quarterly , Volume 56, Issue 1, SPRING 1992, Pages 1–28, https://doi.org/10.1086/269293

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In this article we examine anti-Semitism as expressed by a sample of residents of the Moscow Oblast (Soviet Union). Based on a survey conducted in 1920, we begin by describing anti-Jewish prejudice and support for official discrimination against Jews. We discover a surprisingly low level of expressed anti-Semitism among these Soviet respondents and virtually no support for state policies that discriminate against Jews. At the same time, many of the conventional hypotheses predicting anti-Semitism are supported in the Soviet case. Anti-Semitism is concentrated among those with lower levels of education, those whose personal financial condition is deteriorating, and those who oppose further democratization of the Soviet Union. We do not take these findings as evidence that anti-Semitism is a trivial problem in the Soviet Union but, rather, suggest that efforts to combat anti-Jewish movements would likely receive considerable support from ordinary Soviet people.

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