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Social Determinants of Health, Electronic Health Records, and Health Outcomes

Marisa L. Wilson / Paula M. Procter

INTRODUCTION

In 2010, the Affordable Care Act (ACA) was enacted to bring about a decrease in the number of uninsured Americans, to usher in an improvement in the quality of care received, and to result in a reduction in the overall cost of healthcare through an increase in access to preventive services. The main goal of the ACA was to promote the health of the country in order to improve quality of life and contain cost. The ACA shifted the focus of the healthcare industry away from payment for the volume of services rendered toward accountability for quality of care and health outcomes achieved which does not happen with medical treatment and services, testing, or pharmaceuticals alone. The ACA placed increased attention on areas outside of traditional medical treatment and care services and onto those factors that affect overall health where we live, work, and play. These factors are the social determinants of health (SDOH) which are those complex, integrated, and overlapping social, environmental, and economic structures that are responsible for most health inequalities. Given that medical care is estimated to account for only 10% to 20% of the modifiable contributors to health outcomes, the SDOH factors have to be considered if the goals are to be achieved (Hood, Gennuso, Swain, & Catlin, 2016).

For the informaticist, considering the inclusion of SDOH data along with the pool of data currently collected during medical care and treatment interactions and usually stored in an electronic health record (EHR) or other specialty database brings up several concerns that must be addressed in order for health and quality to be achieved. The concerns are as follows:

1.   What are the SDOH factors?

2.   What must be considered in the SDOH data collection process?

3.   Can community-level SDOH data be used?

4.   How does the collection of SDOH data impact the people, processes, and technologies that interact in any setting?

5.   How does the informatician work with an interprofessional and multiorganizational team to design successful projects to address the SDOH?

The aim of this chapter is to introduce the reader to the best evidence and resources that can be used to answer these questions.

BACKGROUND

The topic of social determinant’s influence on health is no longer a topic of debate. What is known is that the impact of SDOH factors such as sanitation, food insecurity, housing instability, transportation shortages, environmental issues, interpersonal violence on a patient’s health and well-being, healthcare utilization usage, and overall cost of care has been well established (Brooske, Athens, & Kindig, 2010; Gottlieb, Quinones-Rivera, Manchanda, Wing, & Ackerman, 2017). Landmark documents such as the 2008 Closing the Gap report by the World Health Organization Commission on Social Determinants of Health provides evidence to demonstrate that income, education, social status, and social support are correlated with increased morbidity and premature mortality (British Medical Association, 2011). In the United States, currently, 90% of healthcare dollars are spent on medical treatments that occur within a healthcare setting such as a hospital or provider’s office. However, up to 70% of a person’s overall health is driven by social and environmental factors and the behaviors that influence them, which are external to the medical and health care environment (Schroeder, 2007). Analyses presented by multiple organizations ascribe correlation with health outcomes as follows (Goinvo, 2017):

1.   Seven percent by the environment (pollution, location, exposure to firearms, allergens)

2.   Eleven percent by medical care

3.   Twenty-two percent by genetics and biology

4.   Thirty-six percent by individual behavior (psychological, mood and affect, risk, physical activity, sleep, and diet)

5.   Twenty-four percent by social circumstances (social connectedness, social status, culture and tradition, race, ethnicity, sexual orientation, military service, gender identity, incarceration, discrimination, and work conditions) Goinvo, 2017.

These structural determinants and conditions not only impact health of people but also contribute to over onethird of the causes of death in the United States every year (Galea, Tracy, Hoggatt, DiMaggio, & Karpati, 2011).

There is increased recognition that improving health, reducing costs, and improving quality will require broader approaches than what is currently offered within healthcare settings. Truly, achieving health will require a recognition of and a response to the social, economic, and environmental factors that impact health. Informaticians will need to understand the current evidence related to screening, risk assessment, data transfer, and evaluation of those programs established to address the SDOH factors. Informaticians will need to consider how SDOH data interact with and are considered along with medical data captured within EHRs.

International Recognition of SDOH

Note that the concern with the SDOH factors is not only a US centric issue but is being addressed by other countries as well, and this chapter provides a view from the United Kingdom. Beginning in 2011, the World Health Organization (WHO) Commission on Social Determinant of Health, the Rio Political Declaration on Social Determinants, the United Nations (UN) General Assembly, and the World Health Assembly all expressed global political commitment for the implementation of approaches that incorporate SDOH to reduce health disparities by improving access and addressing underlying conditions (Donkin, Goldblatt, Allen, Nathanson, & Marmot, 2017).

The United Sates is a rather newcomer to this perspective which has been driven by the relatively poor health status of the US population compared to that of other countries. Despite all that is spent on medical care, the United States lags in infant and maternal mortality, injuries, homicide, adolescent pregnancy, heart disease, obesity, diabetes, chronic lung disease, and drug-related mortality as compared to other like countries (National Research Council [NRC] and Institute of Medicine [IOM], 2013). These comparatively poor outcomes have fostered an interest in understanding the reasons. The fact that the United States has a higher rate of morbidity and mortality even though it spends more on healthcare than the other countries in the Organization for Economic Co-operation and Development (OECD) has coined the American Paradox (Bradley & Taylor, 2013). It may be because several OECD countries not only focus on the provision of care but also incorporate SDOH information in their provision of care and into their EHRs. The United States spends more on healthcare than other OECD countries and significantly less on social services, while other OECD countries with better outcomes spend more on tracking and addressing social services which could address the SDOH factors and ultimately improve health (PetersonKaiser, 2017).

• FIGURE 11.1. Social Determinant of Health Visualization. (Created by GoInvo at https://www.goinvo.com/vision/determinants-of-health/. Licensed under a Creative Commons Attribution 3.0 license.)

Given that SDOH contributes to poor outcomes and higher cost, it is important to note that there are efforts to address them within the context of current payment reform in the United States. In 2018, Health and Human Services Secretary Azar indicated that the Center for Medicare and Medicaid (CMS) Innovation Center (CMMI) was seriously considering inclusion of the role of social determinants of health and that the thirty-one 2017 Accountable Health Communities are serving as exemplars of how to address SDOH in the context of health (see: https://www.hhs.gov/about/leadership/secretary/speeches/2018-speeches/the-root-of-the-problem-americas-social-determinantsof-health.html). The Accountable Health Communities models address the gap between clinical care and community services in the current health care delivery system. The selected model leadership and researchers are testing whether screening and identification of SDOH and then referral and navigation to community services will impact costs, improve outcomes, and reduce healthcare utilization (CMS, 2019). Medicaid Managed Care Organizations are increasingly using care management and coordination to target SDOH factors such as housing, behavioral health, substance abuse, and nutrition using screening, data collection, and referral to coordinated partnerships to address risk (Institute for Medicaid Innovation, 2019).

Despite some success, there are challenges. Financing, data collection, data sharing, standardization of screening tools, quality metrics, and integration of strategies between systems represent opportunities for nurse leadership to impact the health of the nation.

CODE OF ETHICS

There is much work to be done to address SDOH by the Nurse Informatician. Competencies and professional performance expectations directs this nurse to address the data, the causes, and the solutions. In addition, consider that the Nurse Informatician, the Informatics Nurse Specialist, or the Nurse Clinical Analyst is still a nurse and, as such, must consider application of the American Nurses Association (ANA) Code of Ethics with Interpretive Statements in their professional practice. Specifically, provision 8 of the ANA Code of Ethics directs the nurse to collaborate with other health professionals and the public to protect human rights, promote health diplomacy, and reduce health disparities (American Nurses Association, 2015). The ANA Code of Ethics calls for all nurses to be creative and innovative in creating approaches that are ethical, respectful of human rights, and equitable in reducing health disparities (American Nurses Association, 2015). The Code of Ethics also challenges the nurse to identify conditions and circumstances that contribute to illness, injury, and disease, to foster health lifestyles, and to participate in institutional and legislative efforts to protect and promote health (American Nurses Association, 2015).

The ANA Code of Ethics applies to the nurse who works in informatics and challenges that nurse to work to incorporate SDOH data within that pool of data that originates from the traditional EHR. The Code of Ethics supports the efforts of that informatics nurse who studies SDOH data, calculates risk to patients and populations, and works with teams to implement and evaluate responses to reduce disparities and poor outcomes.

SDOH: CONTRIBUTORS TO HEALTH

The World Health Organization (WHO) defines social determinants of health as the conditions in which people are born, work, grow, live, and age and include the wider set of forces and systems shaping the conditions of daily life (World Health Organization, 2019). The WHO definition leaves wide open the potential list of SDOH data elements (Table 11.1). The Institute of Medicine (IOM) National Academies of Health and Medicine Committee on Recommended Social and Behavioral Domains and Measures for EHRs in a twophase project, took a broad list of 31 SDOH domains identified for possible consideration and applied criteria of strength of association with health outcomes, clinical and population health relevance, and research usefulness to cull that list down to 17 domains with 31 measures (The National Academies of Sciences, Engineering, Medicine, 2018). To further refine the domain list, the IOM reviewed that list against criteria of readiness with a standard measure, feasibility of collection; usefulness for inclusion in an EHR; and committee judgment (The National Academies of Sciences, Engineering, Medicine, 2018). The final list of identified specific domains and core measures that capture targeted SDOH were to be included in EHRs as part of Phase 3 of Meaningful Use. The IOM then explored and recommended specific data elements and methods for collection (The National Academies of Sciences, Engineering, Medicine, 2018) (Table 11.2).

The IOM made several recommendations to the Office of the National Coordinator for Health Information Technology related to the potential inclusion of these SDOH domains and data elements in EHRs. Two of the recommendations were as follows (The National Academies of Sciences, Engineering, Medicine, 2018):

1.   The certification process for EHRs should include the standard measures for four social and behavioral domains that are already collected (race/ethnicity, tobacco use, alcohol use, and residential address).

2.   The certification process for EHRs should add for inclusion the standard measures for the other eight recommended domains (educational attainment, financial resource strain, stress, depression, physical activity, social isolation, intimate partner violence, and neighborhood median income).

Standardization of the data elements within each selected domain would allow vendors to build product that could acquire, store, transmit, and download self-reported data pertinent to the SDOH factors. Standardization would allow for sharable, comparable data across phases of care as well as within locations and systems of care. This would allow advances in understanding the contributions of SDOH factor management to improved outcomes and quality and reduced costs.

SOURCES OF SDOH DATA

Given the importance of and focus on SDOH factors on individual and population health, many healthcare systems have begun to explore ways to integrate this data with patients’ clinical data. Medicaid and the Children’s Health Insurance Program (CHIP) payment reform projects are providing financial incentives for bringing the issue of SDOH data collection to a broader audience of providers beyond the community health centers and safety net providers who have traditionally worked to meet these needs among their high-risk populations (Cantor & Thorpe, 2018). EHR vendors have begun to develop tools within their EHRs for capturing and storing SDOH data. The vendors are developing tools that use this data for individual risk assessment and referral and for population health management. However, this work is not necessarily following a recommended strategy or being done using standard questions and answers or uniformly coded data elements, which ultimately thwarts attempts at transmission of data across systems. This work, although welcome for the recognition of the importance of SDOH to outcomes, will present challenges. In addition, SDOH data collection can occur external to an organization or internally during an interaction with care providers.

Community-Level Data—Community Indicators

One of the clearest determinants of health is geography. Where people live and the resulting consequences create barriers to care and exacerbate disparities (Graham, Ostrowski, & Sabina, 2015). Health and longevity are greatly influenced by one’s zip code which can be a stronger predictor of health than other factors such as race and genetic code. With this in mind, it is important to consider the utility of data generated external to a healthcare system. Community-level SDOH data are useful at a system level, can enhance performance of predictive models, and are of interest to researchers looking to determine the influence of community context in health outcomes. Community-level data use often requires the involvement of community members so that all are aware of the indicator development and how they will be used, particularly if the end product is a publicly available community comparison tool. Open source and publicly available data sets reflective of community-level SDOH can be used to create single data sets that are mapped at a census track level for analysis after processing and queries. Some sources of this data include that available from the U.S. Department of Agriculture, the Centers for Disease Control and Prevention, the American Community Survey of the U.S. Census Bureau, and other private organizations (Table 11.3).

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