Building Bridges Between Medical Care and Public Health

Medicine and public health have been likened to trains on parallel tracks, with windows facing opposite directions, looking out on the same landscape. As described by Shalala,¹ those individuals on the medical train see the individual trees—the subtle differences in size, color, age, and health; and those individuals aboard the public health train see the forest—populations of similar trees, growing together and weathering the same storms. Although the 2 have potentially complementary perspectives, efforts to improve care as well as personal and population health are hampered by lack of communication and coordination between medical and public health professionals and fragmented data systems. Differing perspectives and disconnected data have also hindered effectiveness of shared efforts between health professionals and other stakeholders, including community-based organizations and health plans. Although the call for greater synergy between medical care and public health is hardly new, emerging technologies and the urgent need for health reform create the opportunity and imperative for them to come together.

Progress toward improving health care quality and outcomes in the United States remains slow. Even within managed care plans, quality often remains unacceptably low, particularly for chronic disease and among certain minority and low-income enrollees.² Although the reasons are complex, it is increasingly clear that the current configuration of health care delivery, with its emphasis on brief encounters with clinicians or calls from disease managers, is insufficient. Patients are usually treated as isolated individuals, not as members of a community whose characteristics may affect their health.

Most health plans and clinicians view populations they serve in broad demographic terms and administrative categories, such as by age, race, diagnosis, or payer, rather than in terms of the communities in which the patients live. Although health plans are expected to manage the care and health of members, key decision makers typically have only limited data about the communities in which their members live. Available data are usually presented in tables and charts that may obscure clusters or hot spots of low-quality care concentrated within a few neighborhoods and which typically ignore local factors that may contribute to those hot spots. Similarly, although physicians are implored to “treat their communities,”³ even clinicians in comprehensive medical homes with electronic medical records generally lack tools to help them see how and where groups of their patients may cluster. Consequently, regardless of their cultural competence or patient centeredness, busy clinicians (and insurers with which they contract) often fail to recognize instances when characteristics of a local community, such as lack of grocery stores or safe places to exercise, may be affecting a significant subgroup of their patients.

Data available to public health professionals have also been far from optimal. Even though most health departments have detailed information about the demographic distributions of their populations, often only sparse information is available about health indicators for these populations and how these are distributed within jurisdictions. With the exception of information derived from vital records or from reportable infectious diseases, most information available to local public health professionals is at the county level or higher rather than at the neighborhood level, including data about risk factors for and the burden of chronic disease. Although public health officials can sometimes obtain data from safety net clinics on chronic conditions among their patients, typically access to data from users of the rest of the health care system is lacking.

In short, fragmented clinical and public health data and gaps in how they are shared among medical care and public health professionals means that each group typically lacks salient and actionable data to maximize health of the populations they serve. This leads to widely differing views of the problem, risks misinformed decision making, and limits opportunities to identify how and where community and public health efforts might supplement the care of individual patients or, conversely, how medical care can help address thorny public health problems.

The disconnect between medicine and public health stands in stark contrast with approaches now routinely used in basic science research, such as genomics and proteomics, where shared efforts and infrastructure across multiple disciplines and across public and private sectors are helping to accelerate growth and disseminate knowledge and tools to overcome complex challenges that previously seemed insurmountable. Similar breakthroughs are occurring in the social sciences. For example, new information technologies are facilitating integration of information about neighborhood environments, population characteristics, and their associated health outcomes in ways rapidly advancing understanding of how neighborhoods affect health. That work, in turn, is increasing medical care sector recognition of the importance of community-level factors in primary and secondary prevention.

In medical care and public health, the large-scale deployment of health information technology is not yet a reality. However, existing geographic information systems (GIS) and Web technologies widely used in other fields can help link inpatient and outpatient care, medical care and public health information, and communities which the health care system serves in the ways that support a population health approach synergistic with patient-centered care. There is also a strong foundation of knowledge about use of GIS tools within both the medical care and public health sectors on which to build. Studies of small area variation in hospital and physician markets and practices, for example, have advanced understanding of health care financing and quality of care. Mapping important public health outcomes, such as variations in life expectancy and the burden of chronic disease, have shown county-level variation in these outcomes. Visual displays of data with maps and related decision tools can serve as a universal language making complex data understandable and actionable for diverse stakeholders in the health care system, public health, and communities. Visual displays can also act as a “disruptive innovation,”⁴ facilitating new insights into how community factors can influence health outcomes and highlighting specific opportunities for shared action between medical, public health, and other local stakeholders as part of routine, comprehensive care.

Several efforts to analyze and map medical care data highlight geographically aggregated health problems that require both personalized and population-level action. For example, mapping hospitalizations for ambulatory care sensitive admissions, such as for asthma or cellulitis, suggest geographic small areas in which community-level intervention is needed.^{5 - 6} The National Health Plan Collaborative, a group of health insurers aiming to reduce disparities in care, has noted very small area variation in quality of care for racial and ethnic population groups.⁷ By geocoding the addresses of enrollees and linking them to quality of care data, plans can use GIS tools to map and highlight hot spots of poor quality, including information regarding racial and ethnic populations. The role of local factors that can inform interventions, such as linguistic isolation or access to primary care clinicians, has been explored.⁸

However, any given health plan (or clinician) has data on only a small proportion of the population; combining data from multiple payers or clinicians in an area is likely to paint a more accurate picture. In California's Right Care Initiative,⁹ leading health plans, clinician groups, public health officials, and regulators are using mapping and decision tools to identify low-quality hot spots in communities served by participating plans. Once identified, additional analyses of local factors and resources will be used to plan and coordinate share action among diverse stakeholders, including competing plans, in ways that might not be feasible otherwise.

Efforts to integrate medical care and public health must go well beyond mapping. Numerous suggestions for doing so have been well articulated by others, including the Trust for America's Health.¹⁰ Meanwhile, health reform legislation can facilitate some immediate steps. For example, all federally collected health-related data should be geo-enabled to facilitate mapping. In addition, health care claims, including those from Medicare and Medicaid programs, should be geocoded and aggregated at the smallest possible level that preserves individual confidentiality. The Department of Health and Human Services might provide guidance on the kinds of data that would be most useful to geocode (eg, hospital discharges or quality measures). Health departments, business coalitions, or others might request that such data be aggregated across all payers and clinicians, both public and private, in their jurisdictions. These steps could help foster and sustain local experimentation in using mapping and related decision tools to identify populations and locations with the greatest needs, to understand local factors that may contribute to poor outcomes, and to develop promising practices and incentive structures for getting the medical care system, public health system, and others working together.

Planned investments in health information technology will likely lead to further innovation in ways to link public health and medical care data, including through automated disease and immunization reporting. Related comparative effectiveness research, at the delivery system and community levels, can then examine whether models that integrate individual and population-level care do indeed achieve better outcomes at lower cost. However, this will likely take several years to implement. In the meantime, greater use of available GIS tools that can integrate and display diverse types of data from many sources can help begin realigning the tracks in ways that will give those individuals on the medical and public health trains a similar view.