Source: http://psjyearbook.com/content/article29-2016
Timestamp: 2019-04-24 14:04:43+00:00

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As debates in industrialized countries over the last century indicate, health care and the role of government in its provision are complex and contentious issues. This article provides an orientation to the variety of topics guiding research and discourse in U.S. health policy, as well as how those topical areas influence and engage each other. This paper introduces five prominent themes in health policy research, namely 1) biomedical policy, 2) public health policy, 3) health economics, 4) health care policy, and 5) health informatics policy. It also provides specific examples from current scholarship. Broad themes that connect those lines of inquiry are highlighted with recommendations for future research.
Since its passage in 2010, the Affordable Care Act (ACA) has dominated mainstream discussions of health policy in the United States. News outlets regularly run stories about health insurance cost and coverage implications for residents (e.g., Denish 2013), constitutionality of insurance exchange subsidies (e.g., Ehrenfreund 2014), the individual mandate (e.g., Rau and Appleby 2012), implementation of Medicaid expansion and basic health plans (e.g., Richert 2013), and the law’s broader implications for spending and cost containment (e.g., Reuters 2014).
The ACA was developed in part around the triple aim framework for improving the health care system in the United States (McDonough 2014). The “triple aim” refers to improving the patient experience of care (access and quality), reducing per capita costs of health care, and improving the health of populations as a national health care system strategy (Institute for Healthcare Improvement, Berwick et al. 2008).
However, while the ACA is commonly referred to as health reform, the policy decisions codified in the legislation are predominantly focused on health insurance coverage and health care payment reform, rather than reforms aimed directly at improving health outcomes in the population. The major provisions of the law — expansion of Medicaid, establishment of health insurance exchanges, the mandate that individuals carry health insurance, insurance plan benefit requirements, and a range of tax changes and cost containment activities — revolve around access to health care through insurance coverage and efforts at minimizing costs (Affordable Care Act 2010). Pilot programs in care management in Medicare and Medicaid and the establishment of the Community-based Collaborative Care Network Program, which are intended to provide incentives to improve the quality of care for individuals, are generic and receive less administrative attention. Comparatively, provisions that relate to improving the health of populations have received less attention in public discourse about the law, as well as far less funding than coverage and cost containment activities. Population health provisions include nutritional labeling in chain restaurants, funds for community health centers and disaster preparedness, and the allocation of Community Transformation Grants. While the largest gains in health in the United States in the last hundred years have been made through public health and infrastructure improvements, prevention efforts continue to struggle for equal footing in policy actions (McGinnis, Williams-Russo, and Knickman 2002). Cuts to the ACA’s Prevention and Public Health Fund serve as one example of this neglect (Weisman 2014).
Health policy pertains to more than health insurance standards and the economics of insurance markets and clinical care. It is also more than a single piece of legislation, however influential it might be. Health policy is about the collective moves made to define societal health objectives and how to achieve them. Creating health policy includes the identification of goals, decisions about the environments in which health and disease develop, the allocation of resources among social systems and health objectives, and the dynamics of values and interests that dominate those decisions.
An overview of health policy that focuses solely on questions of health care coverage and payment arrangements would draw a shade over other robust and active topics within this subfield of policy studies. Thus, this essay first offers an orientation to health policy that includes and goes beyond trends in health insurance reform. Five lines of inquiry in health policy are identified: biomedical policy, public health policy, health economics, health care policy, and health informatics policy. Current examples of specific policy questions are summarized to further describe the work undertaken within each domain. Next, underlying themes connecting the domains of health policy research are proposed. These themes suggest that professional engagement in health policy — whether as an academic or as a practitioner — is embedded in broader social contexts and value negotiations. The essay concludes with a discussion of opportunities for future research and advancements in the subfield.
Meaningful engagement with the field of health policy requires an understanding of the variety of questions motivating research and discourse. It is interdisciplinary, shaped by a range of perspectives, methods, and values as they relate to informing collective and individual decisions about the management of health and disease in society. Topics in health policy research can be understood in terms of health science (biomedicine and public health), health economics, health care services, and health informatics. While peer-reviewed journals indeed serve as the home of much of the scholarship in these topical areas, applications of such research (often funded by foundations such as the Commonwealth Fund or the Kaiser Family Foundation) also contribute to research and practice. Additionally, informed commentary appearing in journals (e.g., Milbank Quarterly and New England Journal of Medicine), professional associations (e.g., the AcademyHealth Blog), and popular media (e.g., The New Yorker, The Upshot in The New York Times) plays an important role in health policy discourse.
Figure 1 describes the interconnected and overlapping relationships among the major domains of health policy research. It also identifies several underlying themes across the domains, which are discussed later in this essay.
Figure 1. Health policy research domains and connecting themes.
Biomedicine pertains to the application of the natural sciences, particularly biology, to the study of medicine (Venes 2005). The biomedical model contrasts with more traditional — often referred to as alternative or complementary — medical practices whose origins are not born of the application of the scientific method to conditions and interventions in controlled settings. The biomedical model and its disease-oriented approach to health care dominates the U.S. system, a system that has been referred to as a “sick care system” rather than a “health care system” (e.g., Fani, Marvasti and Stafford 2012; Levi 2013; Menino and Johnson 2012).
While biomedical science may appear to be isolated to scientific findings in the laboratory, it is shaped by questions of how such research is funded, conducted, and how findings are translated from the laboratory to clinical settings. These sorts of collective decisions represent one domain of health policy research: biomedical policy. Biomedical policy refers to how biomedical evidence is developed and institutionalized into standards of care.
One example of biomedical policy is the recent debate over Right to Try laws, which would enable terminally ill patients to access experimental treatments (including drugs, biologics, and devices) not yet approved by the Food and Drug Administration (FDA). Proponents of such laws, including the libertarian Goldwater Institute, argue that terminally ill patients who have exhausted standard courses of care face undue barriers to accessing novel treatments that have not yet cleared the lengthy FDA approval process (Servick 2014). The process proposed in Right to Try legislation requires only that a treatment have passed initial safety tests (i.e., Phase I of clinical trial), and that a patient get approval from the drug company and a recommendation from a physician. Such legislation has gained popularity among some legislators and patient advocates — as of June 2015, 21 states have Right to Try laws on the books — but the biomedical community, as well as federal legislators and administrators, have largely not been supportive (Dresser 2015).
The FDA already grants “compassionate use” exemptions for patients with life-threatening conditions and without comparable alternative treatments. Unlike the Right to Try process, compassionate use exemptions preserve the FDA as a gatekeeper on the use of experimental treatments. Right to Try critics contend that the FDA process is important for both patient safety and public health; circumventing that process undermines federal law and the drug approval process (Begg et al. 2014; Farber et al. 2015). In addition to the possibility of individual patient harms, removing the FDA from the decision making process could lead to off-label usage of unapproved treatments (Servick 2014). Furthermore, even with a change in process rules, patient access to novel treatments would not be guaranteed due to the lack of incentives for insurers and manufacturers to cover the cost of care (Dresser 2015; Farber et al. 2015; Servick 2014). Still, while scientific consensus suggests that Right to Try processes are a net loss, broad values of patient choice keep them a viable policy option (Dresser 2015).
The development, application, and alterations to preventive screening practices is another matter of biomedical policy. In 2009, the United States Preventive Services Task Force (USPSTF) released recommendations against regular mammograms in women younger than 50 (i.e., mammograms were not given an “A” or “B” rating, but instead a “C” rating, which advises clinicians to use individual context regarding risk and preferences to inform their recommendation; USPSTF 2015). There has been a backlash to these recommendations, in part because efforts to make them more transparent by publishing them online for public comment has increased visibility in the media and lay community (Moyer 2013). This has opened the door to criticism that the recommendations effectively ration care because of the reimbursement implications of Task Force ratings: The ACA requires Medicare and qualified health plans to cover preventive services with a grade of A or B, though it does not prohibit coverage of services with lower ratings. Ratings may be preempted by legislative action, as for the prostate-specific antigen (PSA) test in Medicare. Concerns over implications for individual treatment, as well as criticism of the composition and decision making process of the Task Force (see Carroll's 2011 reaction to that commentary) impose additional burden on providers, who must field questions about the recommendations, despite not being compensated for time spent doing so. Such costs may be one reason that Pollack et al. (2012) find that many physicians do not plan to change their practice regarding referral for a PSA test.
The 2009 USPSTF recommendations are based on evidence that mammography does not reduce mortality for most women (Elmore and Kramer 2014) and that its net benefit is largely tied to the patient’s baseline risk (Pace and Keating 2014). Bleyer and Welch (2012) find that increased use of mammography has resulted in broad over diagnosis of breast cancer, a concern when considering the potential harms associated with screening, such as getting a false positive result (Melnikow et al. 2013). Similarly, Qaseem et al. (2013) find that potential harms of the PSA test outweigh the benefits for most men. Yet there is no consensus on the foundational science underlying the recommendations themselves. For example, in 2013, Etzioni et al.’s model-based approach led the authors to conclude that prostate cancer screening benefits outweigh harms, but Melnikow et al. (2013) maintained that the recommendations drew on evidence from randomized controlled trials (RCTs), the gold standard for clinical research.
Evidence-based medicine is embedded in precisely this challenge of moving from RCTs and clinical evidence, to general guidelines for diverse practice settings, to discrete decisions for individual patient care. That care should be practiced according to the best evidence seems an undeniable value. However, care must be considered in the face of interpretation of that evidence. Wegwarth et al. (2012) find that even medical experts are not particularly good at interpreting evidence from clinical trials about the benefits of cancer screening. The need to provide a check on individual human interpretation through formalized evidence-based processes (such as clinical practice recommendations and guidelines) and the protection of those processes is at the heart of Begg et al.’s (2014) criticism of Right to Try laws, for example. However, evidence-based recommendations do not go unchecked themselves, as recent Congressional action (HR2029) to require the Department of Health and Human Services to use the USPSTF’s 2002 recommendations for mammography demonstrates (Young 2015). RCTs may remain the gold standard of clinical evidence, but biomedical policy deals in the interpretation, translation, and values about both collective and individual decision making regarding that evidence.
Public health is concerned with preventive measures intended to improve the health of communities (Venes 2005). It refers to all organized measures, whether public or private, to prevent disease, promote health, and prolong life among the population as a whole (WHO 2015). Public health practice traditionally includes health and disease surveillance, as well as health promotion through behavioral, community, and environmental interventions. In contrast to biomedicine and medical practice, the science and practice of public health is primarily focused on health phenomena and interventions at the population level.
The community focus of public health is exemplified by the rise of the social determinants of health as a key concept in understanding public health dynamics. Social determinants of health refers to the conditions in which people are born, grow, work, play, live, and age, and the set of institutional forces and systems within which those activities occur (WHO 2015; Healthy People 2020). The fundamental contribution of this concept in public health research and practice is that it emphasizes the social context within which patterns of health and disease develop. Social determinants, combined with genetics, individual risk factors, and specific health care interventions, provide a more complete picture of human health.
Rather than treatments, public health research seeks to identify mechanisms for the prevention of disease and preservation of health within populations. Public health policy revolves around questions of what those mechanisms look like and how surveillance and promotion of health occur. Because many determinants of health are social, public health policy is influenced by, and has implications for, environmental policy, city planning, transportation, health care, education, and occupational standards, to name a few.
One public health policy question that continues to be of particular importance involves requirements and exemptions for childhood vaccinations. Despite being considered one of the top ten public health achievements of the 20th century (MMWR 1999), immunizations — more precisely, their requirement among children — continue to be the subject of debate. Much of this debate stems from the now discredited and retracted Wakefield et al. (1998; retracted 2010) Lancet article claiming a link between the measles-mumps-rubella (MMR) vaccine and autism in children. Despite extensive research that fails to show any association between the MMR vaccine or its components and autism (e.g., DeStefano, Price, and Weintraub 2013; IOM 2011; Taylor et al. 1999), Wakefield and his work still have a tremendous following (Dominus 2011). Celebrities and political figures (e.g., Michele Bachmann, Dr. Rand Paul) continue to provide recognizable public faces for the movement to roll back vaccine requirements and expand exemptions in the name of personal choice. A 2012 Morbidity and Mortality Weekly Review report found that vaccine coverage of children was down and refusals up (CDC 2012). The same report noted that 2011 marked the highest number of measles cases in the United States since 1996.
Thus while the the safety and efficacy of the MMR vaccine is scientifically well-established, public health policy must continue to engage disparate attitudes and values on vaccination practices. The prevention of outbreaks relies on high rates of vaccination in the population to maintain herd immunity, making requirements vital to stifle the incidence and spread of infectious disease (Carroll 2012). One effort to increase vaccination rates involves increasing public trust in the science of vaccination and debunking myths (Ozawa and Stack 2013), but attention to the role of exemptions (i.e., the conditions under which they should be allowed) is also important. Those who call for an end to all but medical exemptions (e.g., Dell’Antonia 2015) have seen a recent victory in Vermont, which has removed all philosophical exemptions, the largest source of that state’s vaccine refusals (medical and religious exemptions remain intact) (Specter 2015). California has passed a stricter vaccination law that allows for medical exemptions only (Perkins 2015). Lillvis, Kirkland, and Frick (2014) find additional evidence that legislation loosening vaccine requirements is waning. But the frames of personal freedom and parental choice remain a challenge to meeting public health prevention objectives in child health and infectious disease control.
Another significant topic in public health policy is what can and should be done to address health disparities. Health disparities are health differences that are closely linked with social, economic, or environmental disadvantage. Health disparities systematically and adversely affect groups that have experienced greater obstacles to health due to historical discrimination or exclusion, such as those based on race, ethnicity, religion, gender, or sexual orientation (HealthyPeople 2020). As Beal (2013) points out, public health has gone through a robust phase of chronicling health disparities (see Kershaw et al. 2015 and Piccolo et al. 2015 for recent examples of neighborhood-level effects), and now turns its attention to what interventions work to address those disparities. Research on disparity reduction and health promotion among low-income workers (Baron et al. 2014) and best practices for health care organizations in reducing health disparities (Chin et al. 2012) represent important work in this vein.
In addition to the descriptive foundation provided by Unequal Treatment (2002), the seminal report by the Institute of Medicine (IOM), research in health disparities highlights the potential for improvement through health care workforce diversity and representativeness (e.g., Iglehart 2014) and integrating reduction of disparities as an objective into other public service domains, such as city planning (Lemon et al. 2014). Moving health equity objectives into seemingly health-unrelated policy domains such as transportation, planning, education, economic development, and energy is the driving force behind the movement for “health and equity in all policies” (see the American Public Health Association, the Prevention Institute, the Robert Wood Johnson Foundation, the CDC and WHO for more information). The motivation behind this work is distilled nicely by Braveman et al. (2011): Addressing health disparities and building health equity matters for social justice.
Like evidence-based medicine, evidence-based public health is embedded in a struggle to reconcile the field’s scientific findings with matters of practice and diverse values within the community.i The Community Preventive Services Task Force (CPSTF) is the counterpart to the USPSTF for community-based interventions. Like the USPSTF, the CPSTF publishes guidelines and recommendations for public health interventions based on systematic reviews of current evidence, referred to as the Guide to Community Preventive Services, or “The Community Guide” (CDC 2015b). Evidence-based public health practice is also supported through the ACA’s Prevention and Public Health Fund investments. This includes additional funds for CDC immunization grants, the Racial and Ethnic Approaches to Community Health (REACH) program, and preventive services block grants (USDHHS 2015). However, the impact of evidence-based policies depends on more than just the epidemiological effectiveness of the interventions (what Brownson, Chriqui, and Stamatakis 2009 refer to as the “content” of the policy). The processes involved in increasing the likelihood of policy adoption and the ongoing evaluation of policy outcomes in target populations are equally important considerations in evidence-based public health policies (Brownson, Chriqui, and Stamatakis 2009). Addressing public health policy questions (for example, vaccination rates and health disparities) requires tracking various kinds of evidence, improved communication of that evidence, and making incremental policy improvements driven by repeated evaluation, as called for by Brownson and his colleagues. However, as the global responses to SARS or the Ebola virus suggest, incremental policy improvements are not always feasible; often research, experimentation, and policy interventions must occur simultaneously.
Health economics is concerned with efficiency, effectiveness, value, and behavior in the production and consumption of health and health care (Fuchs 1987). It involves the logic of markets applied to health and health care, and considers how society finances and assesses the quality of health goods, especially health care.
It is likely intuitive to the reader why health economics is an important trend in the health policy discourse, reflecting both its relevance to broader economic issues and its salience in discussions about health care reform. As an economic factor, health care accounts for a large portion of both public and private spending (17% GDP in 2013, CDC 2015a). Additionally, the health care sector is comprised of a range of robust markets (e.g., direct patient care, pharmaceutical production and marketing, medical device development and utilization, health care insurance products). As a health reform issue (which, to reiterate, is mainly focused on changes to insurance and clinical practice), it touches on two parts of the triple aim: quality of and access to care, and cost containment. More fundamentally, health economics is about the tradeoffs involved in achieving societal health objectives: costs and benefits, values and risks associated with the management of health and disease in a broader market economy.
It is worth noting that health economics is a relatively new field, one that is said to begin with Arrow’s 1963 piece in The American Economic Review entitled, “Uncertainty and the welfare economics of medical care” (Savedoff 2004). Arrow posits that uncertainties in the incidence of disease and the efficacy of medical treatment result in an inefficient allocation of resources and market failures in the health care system. As a result, medical care is not efficiently allocated through traditional market structures. Thus the primary questions are about when markets are a good idea in medical care and when non-market institutions are necessary in the system.
Since Arrow’s seminal work, the complexity of managing the market for health care has only increased under new technologies, institutional arrangements, and payment structures. In addition to identifying an excess supply of specialty offerings and unmet demand for primary care and prevention offerings, Fuchs (1983) notes that one of the most important sources of market failure in health care, information asymmetry, persists. Reimbursement based on interventions (i.e., how many procedures, tests, and treatments a provider prescribes) situates physicians as decision makers in the clinical setting, and incentivizes the preservation of that asymmetry.
But that information asymmetry exists elsewhere in the world. In an effort to explain what accounts for disproportionately high health care spending and growth in the United States compared to similar countries, Anderson et al. (2003) argue that it is the prices — not health status or utilization patterns — that account for such differences. Prices are not only high, but they are often hidden from patients and clinical practitioners (Brill, 2013). Lack of transparency in hospital prices, for example, represents layers of information asymmetry (Brill argues deception) between patients, institutions, and the providers those institutions employ. These are only some of the issues that clearly point to the crucial role economics plays in the health care system.
Evaluation of health care markets is not just about efficiency (i.e., monetized effects). It also involves connecting market characteristics and activities to health measures that are difficult to monetize. One such example in health economics research involves the relationship between health care competition and quality. Assessing quality of health care is a significant area of research in its own right (see Manary et al. 2013 for a summary of current perspectives). For example, the United Kingdom’s National Health Service requires and pays doctors to make sure their patients are aware of their choices of hospital. Hospital quality data are available to patients, so hospitals have real incentives to compete on the basis of quality of care. Frakt (2015) points to a 2013 evaluation of the U.K. policy by Gaynor, Moreno-Serra, and Propper indicating that it has increased both competition and quality of care. Other studies (Cooper et al. 2011; Kessler and McClellan 1999) support those findings, indicating that greater hospital competition is associated with lower mortality rates. Similarly, Frakt notes, increased hospital competition is associated with better management practices, such as quality monitoring and performance incentives (Bloom et al. 2015). Overall, hospital competition is associated with improved quality, better management, and lower prices (Gaynor and Town 2012).
Questions about health care markets continue to concern society in its aim to achieve health objectives. As Glied and Miller (2015) point out, health economics was at the center of much of the discourse involved in the development and debate of the ACA. While health economics offers important insights into the market tradeoffs in cost, quality, and choice, it also provides opportunities to think about the sustainability and social welfare gains inherent in those tradeoffs.
Health care refers to services made available by medical professionals to promote, maintain, or preserve life and well-being; to relieve pain, treat injury, illness and disability, and provide comfort and hope (Venes 2005). Because the delivery of health care is achieved through a web of market arrangements — for-profit, non-profit, and public sector institutions — health economics is strongly tied to health care policy; how society pays for health care is foundational to what care is made available.
The causes, costs, and consequences of health care are also captured in health services research, the field of study focused on how people get care, how much it costs, and what happens to them as a result (AHRQ 2002). Health care policy is the means by which the patient experience of care is institutionalized: how it is paid for, how it is accessed, the interactions in its delivery, and the results of that care. This includes insurance coverage and other mechanisms for access (e.g., community health centers and free clinics). Health care policy and health economics are primarily concerned with the roles of clinical care and interventions (rather than primary prevention in the community) as mechanisms for achieving societal health goals.
Because the provision and use of health care is fundamentally tied to economics, much of health care policy research revolves around the role of reimbursement in what services are offered, and informing possible changes to those offerings. Ultimately, payment for services is how the health care market functions, regardless of the specific arrangement (e.g., fee for service or fixed price for a bundle of services).
In clinical settings, one question concerns the time a provider spends with a patient and how that time is valued. If tests are ordered, medications prescribed, or procedures carried out, the time is valued according to those interventions. However, time spent on communication and understanding is not so easily monetized, and is often left out of the reimbursement schedule for providers. Thus there is pressure to avoid spending time discussing anything not proximately related to a specific course of treatment. This form of reimbursement has implications for communication about complicated decisions (for example, cancer treatment), as well as for issues with long time horizons or some degree of social stigma. Talking about end of life plans and advance directives, for example, is both an uncomfortable conversation between clinicians and patients (Span 2015) and one for which clinicians are not compensated (Carroll 2015). Mechanic (2014) posits that in order to achieve real parity in mental health care, time spent on pre-referral conversations in primary care settings must be included in payment arrangements.
Another question of clinical service reimbursement relates to what services truly produce good outcomes for patients. As mentioned above, pervasive PSA testing does not reduce overall mortality (Qaseem et al. 2013), but many physicians have no plans to change their practice related to testing (Pollack et al. 2012). Chou et al. (2011) find a general overuse of imaging for low back pain, and suggest that the practice is a function of financial incentives tied to the supply of MRIs, improving patient satisfaction, and avoidance of potential legal action tied to a missed diagnosis or poor outcome (i.e., defensive medicine). The provision of services of questionable value is a key motivation behind the push for cost effectiveness research in health care policy (Carroll 2014). Better understanding about the costs and effectiveness of various interventions may alter reimbursement arrangements and thus reduce overuse or inappropriate use of services.
Given the uncertainty of demand for medical care (Arrow 1963) and its role in maintaining a productive workforce, policies about the access, cost, and quality of health care are instrumental to achieving societal health goals. Those policies cannot be divorced from the relationship between health care payers (i.e., insurers) and providers. The ACA’s focus on financial incentives in patterns of clinical practice is a reflection of this imperative.
Health informatics is the study of the “design, development, adoption and application of information technology-based innovation in health care services delivery, management, and planning” (Proctor 2009). Health informatics also includes the use and exchange of information for health surveillance (e.g., HealthMap for infectious monitoring, healthmap.org) and health promotion (e.g., the use of Twitter and Facebook in several campaigns for sexual health promotion, per Veale et al. 2015). Health informatics is both technical and social in that the generation and utilization of health data is both computationally intensive and culturally mediated.
Health informatics policy refers to how health and health care systems function in the new resource environment of prolific data generation and utilization. It concerns how systems collect data on health, disease, care delivery, and outcomes, and how that information is used. Compared to the other domains outlined above, health informatics represents a nascent line of inquiry in the health policy subfield. Both the Health Informatics Journal and the Journal of Telemedicine and Telecare were launched just 20 years ago. It was as recently as 2001 that Computers and Biomedical Research was renamed the Journal of Biomedical Informatics, heralding a shift from a focus on a technical niche of practice to one addressing broad questions about an everyday part of biomedicine.
Two pieces of legislation have played a significant role in the emergence of health informatics as a policy domain. The 1996 Health Insurance Portability and Accountability Act (HIPAA), specifically Title II, required the establishment of national standards for electronic health care transactions. A series of revisions since then (particularly those related to privacy) have increased the applicability of health informatics concerns in common clinical practice. In 2009, the Health Information Technology for Economic and Clinical Health (HITECH) Act (part of the American Recovery and Reinvestment Act) provided legislative security to the Office of the National Coordinator for Health Information Technology created by President G.W. Bush’s executive order in 2004. It also established standards for “meaningful use” of electronic health records to improve quality, safety, and engagement; to improve care coordination and population health; and to maintain privacy and security of health information (HealthIT.gov 2014).
The modernization of the health care system through electronic health records (EHR) is one important question in health informatics policy. In an ethnographic study, Dixon-Woods et al. (2013) find that meaningful use of EHRs, specifically using secondary data to improve patient care quality, results in process measure improvements in a clinical setting. But gains in high quality processes and potential reductions in errors do not supplant concerns over privacy and data use of patient health records (Campbell 2015). The rise of other sources of data collection and management (e.g., mobile devices, wearable trackers), particularly if blended with EHR data, raise questions about the preservation of biomedical privacy in the meaningful use era (Malin, El Emam, and O’Keefe 2013).
Health informatics policy is also grappling with the role of data generation and utilization in meeting public health objectives. Birkhead, Klompas, and Shah (2015) note the potential for EHR to contribute to public health surveillance and treatment improvement, suggesting that the use of such data provides an opportunity to bridge the gap between public health and clinical medicine. But ‘big data’ are not a panacea for public health. Google Flu Trends’ overestimate of flu levels in the 2012-2013 season indicated that traditional epidemiological surveillance remains valuable (Butler 2013). Still, all models are meant to be updated, so it is not difficult to imagine that traditional, clinic-based reporting can be paired with web-based monitoring and algorithms (such as Google Flu Trends, or Flu Near You) to improve both surveillance and predictive capabilities in population health informatics.
Another question of interest in health informatics policy is the development of personalized medicine.ii Also referred to as precision medicine, personalized medicine relies on measures of individual patient characteristics (rather than the average patient) to inform medical decision making and treatment (FDA 2015). This includes the collection and use of biological data, such as genetic information, as well as care experience data, such as patient input on quality metrics and weights. While the Centers for Medicare & Medicaid Services (CMS) has typically relied on expert-developed measures for high quality reporting, current work by Mukamel and colleagues provides an opportunity for patients and their families to create personalized composite quality measures on CMS’s Nursing Home Compare (NHC) report card, an application referred to as NHCPlus (Mukamel 2014). Enthusiasm for customized quality reporting and treatment options is tempered by ethical concerns and equity considerations. Genetic screening, for example, can be used to tailor prevention and treatment, but it also has the potential to be misused by employers to identify and discriminate against high risk employees. Personalized quality measures may increase patient-centered care for some, but it may not alleviate the cultural competency failures that minority populations often encounter.
The technological challenges of EHR, public health surveillance, and personalized medicine all demonstrate a larger truth, that data proliferation and data mining in health informatics are pervasive (Herland, Khoshgoftaar, and Wald 2014). Leveraging the power of such data to inform collective action holds a great deal of promise in the quest to achieve societal health objectives, but not without a price. Health informatics policy must continue to negotiate the tradeoffs between the potential benefits for collective goals and the protection of individual rights to privacy.
This paper has offered just one of many ways to organize the literature that highlights the interdisciplinarity of the field of health policy. Being an expert in health policy requires deep, critical engagement with a range of different lines of inquiry into questions about the role of human health in society. For example, participation in discourse around the ACA requires not only an understanding of health care markets and health insurance, but also the interactions, relationships, and interdependencies among those institutions, health care providers, and the foundations of clinical decision making.
Health policy as a subfield is diverse, but lines of inquiry are ultimately connected by the negotiation of values and information within the individual, organizational, community, and market dynamics of the societal landscape. One common theme is the role that societal change has had in shaping what health policy questions are asked and how. As the landscape of society itself has changed, so too have the information and values being negotiated within it. Another theme relates to how inquiry in health policy is carried out, i.e., the methods employed to conduct research. Changes to the societal landscape highlight complexity, and the diversity of methods in health policy research reflect the multiplicity of approaches used to confront that complexity.
Demographic change and diversification is one element of societal change shaping health policy’s central questions. Demographic and cultural diversity are driving increased attention to issues of equity and justice in health determinants and health outcomes.
Illegal immigrants do not, and have not, had access to Medicaid, even with the expansion of Medicaid in most states through the ACA. But California has made moves to provide some Medi-Cal (the state’s Medicaid program) services to immigrants who lack legal status (CDHCS 2014). This decision was informed by the economics of California’s health services for a sizable immigrant population; state costs associated with emergency care and infectious disease management indicated that coverage of basic health services for all residents, regardless of legal status, was an efficient and cost effective move (Medina 2013). In addition to being projected as a smart policy move for California’s budget, Medi-Cal coverage (though limited) should improve access to services for illegal residents. However, Bustamante et al. (2012) find that in the years before Medi-Cal expansion, health care access for Mexican immigrants was tied to legal status. It is not clear how much of a barrier documentation will continue to pose to illegal residents under the new policy.
Health disparities among immigrant populations also influence the selection of topics in public health policy research. In addition to questions about whether existing interventions are appropriate for immigrant populations (e.g., Rothschild et al. 2012), some public health scholars have suggested that more attention needs to be given to immigration itself as a social determinant of health. Using this lens is necessary to understand the structural explanations for health disparities in immigrant populations (Castenada et al. 2015; Viruell-Fuentes, Miranda, and Abdulrahim 2012).
Health disparities across racial and ethnic categories will continue to be the subject to health policy challenges, even as minority groups become the population majority by the middle of the 21st century.iii This shift may change the scope and conceptualization of social justice issues, though disparities in access and outcomes are likely to be investigated in the context of the burden of chronic diseases across populations. In 2012, half of the adult population reported having been told by a provider that they had a chronic condition, and one in four adults had two or more chronic conditions (Ward, Schiller, and Goodman 2014). While additional attention to chronic disease prevention will continue to demand public policy action and funding (as through PPHF investments in obesity, diabetes, tobacco cessation, and heart disease and stroke; USDHHS 2015), it will be increasingly necessary for such action to be taken with express consideration of racial and ethnic diversity (for example, through programs such as Racial and Ethnic Approaches to Community Health; USDHHS, 2015).
The forces of an aging population in the United States draw attention to health equity and access in old age and at the end of life. By 2030, 20% of Americans will be aged 65 and over, compared to 15% in 2014 (Colby and Ortman 2014). In part due to aging baby boomers, falls prevention and Alzheimer’s outreach are acknowledged as population health concerns, not subjects of specialty care (e.g., ACA funds through PPHF; USDHHS, 2015). Given the burden that chronic conditions will continue to impose, the U.S. system can ill-afford lack of innovation in chronic disease prevention and management in the elderly. Health care policy and health economics have taken up questions about the sustainability of elder care (e.g., Baicker et al. 2013; Quill and Abernathy 2013), in addition to considering the role of advance directives in achieving the kind of care individuals desire (discussed above). Services such as palliative care, chronic disease management, and Medicare more broadly are expensive, and represent resources that cannot be spent elsewhere in the system.
Socio-political and climatic volatility are shaping health policy’s questions about the global challenges of safety and sustainability. The terrorist attacks of September 11, 2001 heightened American awareness of non-state international violence. Important lessons about the health effects of such attacks were learned from World Trade Center victim surveillance (Thorpe et al. 2015). But the attacks also raised new questions about long-term health consequences, long-term economic burden, health care system response and threats of bioterrorism and what actions need to be taken to prepare for the weaponization of chemical or biological agents. Unfortunately, the 2013 Boston Marathon bombing provided another opportunity to assess the responsiveness of health care and public safety workers (Kellermann and Peleg 2013).
Natural disasters have also driven research on disaster preparedness. Hurricane Katrina raised awareness about the human element of extreme weather events, both in terms of the human contribution to climate change and in failures of preparation and response for such events (CMAJ 2005; Jacob et al. 2008). Seven years later, Hurricane Sandy spurred questions about how to keep health care systems running in times of crisis (e.g., Redlener and Reilly 2012) and improving preparation and response among health care and safety workers (e.g., Manuel 2013). Knowlton, Rotkin-Ellman, and Sheffield (2013) point out that with rising sea levels and other effects of climate change, another Sandy- or Katrina-like event is only a matter of time. Health policy must consider the consequences of not preparing for such events, not only in terms of the health care and public health systems, but across other policy domains. A focus on health and equity in policy design and implementation is relevant to questions about what actions society will take on climate change.
Conflicting values over the role of the state in matters of health is another manifestation of the way in which societal pressures are shaping health policy. While the struggle to balance individual freedom with collective responsibilities and needs is not a new phenomenon, the dynamics of the tension — the intensity of the debate in American politics, most notably in the rise of the Tea Party and its absorption into mainstream Republican positions — are new. Consider the debate over vaccine requirements for children discussed earlier. While the health science is clear on the benefit of vaccinations, policy decisions are subject to conflicting values over the personal benefits and costs of mandatory vaccinations.
Debate over funding research on gun ownership, integration of the findings into policy, and implications of the findings for clinical practice represent a similar question: what science is done and actions are taken in the name of public and private health and safety (Laine et al. 2013). While the CDC funding ban on gun research (1996-2013) was lifted by executive order in the aftermath of the Sandy Hook Elementary school shooting in December 2012, public funding for gun research remains low, in part because the restrictive grant language applied to the CDC was extended to other federal agencies (Frankel 2015). Gun deaths continue to be a public health and safety concern, with no changes to federal policy on the horizon (Gopnik 2015). The debate has spilled over into clinical settings, where physician ability to discuss gun ownership with their patients is under scrutiny (Kellermann & Rivara 2013).
Lastly, debate over the ACA itself — the Medicaid expansion, health insurance subsidies, health insurance marketplaces, and the individual mandate to carry insurance — has revolved around the tension between the need for collective action to address failures in the health care system and personal (and state) autonomy. Questions about insurance marketplaces and Medicaid expansion concern what the federal government can make the states do (Haeder & Weimer 2015); the individual mandate is about what government can make individuals do (Elhauge 2012); and inter-branch conflict (such as the House lawsuit concerning spending approval for subsidies) is about what government can make itself do (Dellinger 2015). Such debates about the role of the state in society demonstrate how the tension between individual and collective decisions applies pressure on American health policy.
As an interdisciplinary subfield, health policy research has always drawn on a range of methods, including clinical trials, case studies, surveys, and epidemiological observation. The methods for causal inference and inferential statistics that dominate in econometrics and biostatistics continue to be popular in health policy research. For example, the prevalence of regression discontinuity analyses and instrumental variable approaches in health economics (e.g., Bauhoff, Hotchkiss, and Smith 2011; Cawley and Meyerhoefer 2012), and longitudinal and time series analysis in public health and biomedicine (e.g., Badley et al. 2015; Levy et al. 2012; Power, Kuh, and Morton 2013).
However, health policy scholars are attempting to accommodate change and complexity in their methods by expanding beyond standard research designs and analytic procedures. RCTs have long been treated as the gold standard for establishing causation, but they are not infallible, and are often impractical in social science research, including many questions in health policy (Frakt 2013). Sanson-Fisher et al. (2014) suggest a number of alternatives to RCT for building rigorous public health evidence, including cluster RCT, stepped wedge, and interrupted time series methods. Recently, discrete choice experiments have been widely used, especially in health economics, to assess preferences for treatment alternatives (Lancsar et al. 2013), health status (Bansback et al. 2012), and incentives to change health behavior (Promberger 2012; see also de Bekker-Grob 2012; Coast 2012).
In addition, health policy models have expanded to include a broader set of variables and factors that contribute to health in human society. Barbazza and Tello (2014) point out that health measures are increasingly tied to other social outcomes and factors, such as education (e.g., Kemptner et al. 2011), social welfare, market performance, poverty, energy, and governance. In a couple of interesting examples, Smith et al. (2013) explore the effects of different kinds of energy production on human health, and Fink and Masiye (2015) find that farmers in Zambia randomized to a malaria prevention intervention have increased agricultural productivity.
Because decisions about health and disease management are made within complex social settings, quantitative data and quantitative analysis of qualitative data (statistical analysis of survey data, for example) often fail to offer satisfactory explanations of health policy phenomena (Brownson, Diez Roux, and Swartz 2014; Rosner 2015). Quantitative methods have a lot to offer, but the subfield has plenty of activity in qualitative research and mixed methods to address the complexity of the problems of interest. Health Affairs has published work using ethnographic analysis (e.g., Bhatia and Corburn 2011), interviews and content analysis (e.g., Etchegaray et al. 2014), and regularly features case studies (e.g., Bechelli et al. 2014; Laurance et al. 2014; Lerner, Robertson, and Goldstein 2015; Rosenbaum et al. 2012). The Milbank Quarterly has published studies using comparative (Sorenson and Drummond 2014) and content analysis (Van der Wees et al. 2014), ethical (Rhodes and Miller 2012) and critical interpretive analysis (Moat, Lavis, and Abelson 2013), and has expanded its op-ed section to include additional informed commentary from leading health policy scholars.
The rise of team science as an approach to investigating issues of health in society represents a move to leverage the range of tools and techniques mentioned above. Team science refers to purposeful collaboration among professionals from multiple disciplines to confront complex societal problems (e.g., the burden of chronic disease on social and economic institutions) by drawing on measures, methods, models, and analytic tools across disciplines (Hall et al. 2008). Team science involves consideration of various units of analysis, research designs, and data sources. A related approach, transdisciplinary action research, emphasizes the need for researchers to collaborate with practitioners to provide context for the development and assessment of policy interventions (Stokols 2006). Because teams form around a specific problem rather than from a common professional or disciplinary tradition, collaborative research requires attention to the design, management, and evaluation of the team itself (Stokols et al. 2008). New approaches in research often require initial investments such as these, which provide opportunities for scientific advancement in return. The complexity of matters of health in society suggests that it is worth making such investments.
The challenges of linking models of individual health, disease, and behavior to collective actions, and back into individual decisions are in part being confronted in health policy by integrating quantitative data with qualitative methods for interpretation and sense-making. Yet the struggle to conduct health policy research that does not fall prey to either the atomistic or the ecological fallacy remains as individual-level and population data alike are used to inform both collective and individual decisions. Methods in health policy research must continue to leverage the benefits of a diverse toolbox, and work to synthesize analytic findings with diverse values and meanings.
This article has introduced and organized the field of health policy in terms of the domains guiding inquiry about collective actions taken to achieve societal goals in human health. It has identified biomedical policy as a set of questions about translating lab evidence into clinical settings; public health policy as questions concerning the translation of community evidence back into community intervention and infrastructural action; health economics as questions about the financing of and expenditures on health goods and the functioning of health care markets; health care policy as questions concerning the institutionalization of patient care through professional and market mechanisms; and health informatics policy as a set of questions about the collection and use of health and health care delivery data, and the integration of health information technology into the health care system.
This article has also highlighted underlying themes connecting these different lines of inquiry, i.e., common societal issues that shape the way in which health policy is framed and conducted. The challenge of collective problem solving in complex decision spaces & those areas characterized by the interaction of human health with social, political, and economic institutions & is the backdrop for health policy research. Demographic change and diversity, changes in the global environment, and value conflicts about the role of the state in personal health and health science exemplify these challenges. Leveraging both quantitative and qualitative methods across the subfield further reflects efforts to better understand the complex role of health in society.
While recent work has made important moves in confronting the complexity of managing the dynamics of human health, opportunities for continued work in that space abound. One area is in theoretical contributions: the frameworks scholars use to carry out research to inform decisions about health and disease management in society. Different lines of inquiry in health policy have strong theoretical foundations in their underlying science; for example, germ theory in biomedicine, classical economic assumptions in health economics, stages of behavioral change in public health, and machine learning algorithms in health informatics. However, health policy needs more than theories that help establish scientific evidence (as germ theory explains disease transmission, for example). It also requires theories about how society makes collective decisions about the legitimacy and value of scientific evidence, and the moves society makes to achieve objectives informed by those values and that evidence.
Deepening understanding about collective action requires theoretical frames that integrate micro explanations with macro explanation & frameworks that embed individuals, organizations, markets, and communities within systems of institutional and social decision processes. The study of the role of psychological and cognitive factors in individual decision making constitutes behavioral economics, which is one space where these sorts of connections are being made. For example, insights about program defaults (reference points) and the framing of decisions and alternatives have been used to shed light on drivers of health insurance take-up (Baicker, Congdon, and Mullainathan 2012) and adjustments to provider payment incentives (Khullar et al. 2015). Defaults and “convenience” factors have also been shown to have an effect in choice patterns in school lunchrooms (Hanks et al. 2012).
Organizational theories may also be used to explore the connections between micro and macro behaviors. Frameworks built around organizational structure and behavior have been picked up by implementation literature in health care settings to explain organizational learning and change (Kerman et al. 2012) and the integration of evidence-based practice in human services (Aarons et al. 2011).
The theoretical element in both behavioral economics and organizational studies that adds value to existing frameworks in health policy is the opportunity to examine not just health structures (e.g., organizational and social networks and markets), but also the dynamics, interdependencies, and interactions among the structures and different actors operating within those structures. This is fundamental to a treatment of health and health care as what Ackoff refers to as complex social systems (1994, 176). These are systems that are affected by external conditions, that have a purpose(s), and whose parts have purposes of their own. This conceptualization accounts for both micro and macro patterns and their interactions. Processes, especially nonlinear processes such as feedback loops and learning behaviors, are fundamental to systems frameworks. The utility of systems science in public health (Luke and Stamatakis 2012; Riley et al. 2011) and health care services (Paina and Peters 2012) has been pointed out by a few researchers, but opportunities for both theoretical contributions and modeling innovations through the systems lens are plentiful.
Additionally, public policy and management scholars can make important contributions to a latent theme in health policy research: Governance. Governance refers to “government’s ability to make and enforce rules, and to deliver services” (Fukuyama 2013, 350). Consider the rise of chronic disease burden and its implications for health care system structure and management. The American system developed from an acute disease care tradition where private firms (i.e., hospitals and independent physicians) were the key executors of health care delivery. But the burden of chronic disease — in death, disability, and cost — is now the driving force in health care system demand (CDC 2015c). In the 20th century, the coordinated efforts of “power-deploying institutions” (Fukuyama 2013, 348) in government — through for example, government payment for health care, public health efforts and successes, regulations on insurers, providers, and technologies — became central to the health care system, even while frontline delivery remained largely in the hands of private providers. Yet health policy scholarship continues to be dominated by biological (e.g., medicine, epidemiology) and economic disciplines. Bureaucratic and democratic institutional perspectives (as studied in political science or public administration) provide important context for community, clinical, and market forces and phenomena in health.
One existing line of study related to governance research revolves around accountability. Within the health policy literature, accountability has focused mainly on quality of care and access to services. Questions about accountability are raised in terms of how quality scorecards used for payment decisions (Conrad et al. 2013) and improvements in patient care (Porter, Pabo, and Lee 2013) can lead to improved provider performance. The 2014 discovery that 35 veterans died waiting for care at Phoenix-area Veterans Health Administration facilities (Daly and Tang 2014) raised questions not only about the backlog of patients, but about the incentives in place to keep those wait times hidden from the public (Reed 2014) and how to reestablish trust in the management of VHA centers across the country (Kizer and Jha 2014).
As the VHA scandal suggests, monitoring the performance of providers is not the only accountability relationship of import in health policy. To meet societal health objectives, policymakers, administrators, and society itself must be held accountable for collective health objectives and outcomes. Data security is one area where this idea has gained traction. Crawford and Schultz (2014) have highlighted the general need for considerations of due process and legal protection in light of big data collection systems in both the public and private sectors. Specific actions to be considered with health data include the de-identification of patient records (McGraw 2013) and the encryption of insurer files (Alonso-Zaldivar 2015).
Discourse around health disparities broadens the scope of accountability beyond formal health care actors by considering how all collective actions have implications for health equity. The health and equity in all policies movement in the public health community speaks to the idea that health equity — equal opportunities for health across populations — is fundamentally a social justice issue (Braveman et al. 2011). Thus, in order serve citizens faithfully, health policy efforts must include specific mechanisms for accountability to the people (e.g., the HHS strategy for reducing health disparities, Carey 2011; The Health Equity and Accountability Act of 2014).
Ultimately health and disease are profoundly personal, but they also are deeply collective. Individual health behaviors and outcomes are not independent events. Society is not a population of dice rolling themselves repeatedly; it is more like an intricate pattern of dominoes set up across a series of variably sturdy card tables. Our decisions shape and are shaped by the decisions others make, and by local and global events. We take cues from our institutions and each other, both as individuals and as a society. Health policy is both a reflection of that negotiation and a venue for informed discourse to leverage values and information for collective action. In an era where we confront complex problems of health in society, health policy is ultimately a matter of how the institutions wielding the power of our collective action use that leverage.
The Affordable Care Act continues to be the primary driver of public discourse about health care in the United States. However, this essay has indicated that debates about health insurance coverage and health care payment reform — the foci of the ACA — are only part of the current health policy picture. Those scholars who wish to engage meaningfully with the subfield would do well to connect with the full range of inquiry outlined above, as well as with the deeper societal trends cutting across those domains. Investigation of health policy’s biggest questions, including those involving the ACA, demands contributions through the confrontation of the interdependencies among health science, economics, services, and informatics; through the challenge of complexity that characterizes health policy scholarship in the 21st century.
I thank two anonymous reviewers, Kristin Harlow and Anand Desai for their thoughtful and constructive comments on earlier drafts of this piece. Any remaining errors or weakness are my responsibility alone.
Lisa A. Frazier is a doctoral candidate at the John Glenn College of Public Affairs at the Ohio State University, where she is also an instructor. She holds a Master of Public Health degree from the Ohio State University. Her dissertation examines administrative burden in state Medicaid programs using a mix of Bayesian regression and simulation models.
i See Brownson et al. 2011 for an extensive description on evidence-based public health and its practice.
ii I am indebted to an anonymous reviewer for recommending this discussion of personalized medicine, particularly its focus on NHCPlus as an example of care personalized for patient preferences.
iii Non-Hispanic white will be the minority compared to all other racial/ethnic categories by 2044. Groups other than non-Hispanic white made up 38% of the population in 2014, but will comprise 56% of the population in 2060. See Colby and Ortman 2014.
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