Policy Points Health care systems and policymakers in the United States increasingly use language related to social determinants of health in their strategies to improve health and control costs, but the terms used are often misunderstood, conflated, and confused. Greater clarity on key terms and the concepts underlying them could advance policies and practices related to social determinants of health-including by defining appropriate roles and limits of the health care sector in this multisector field.

As a result of a large and compelling body of evidence documenting the impacts of social determinants, such as income and education, on health outcomes, health care systems are beginning to incorporate social and economic risk data into health care delivery decisions. But there is a risk that some of these efforts could worsen health and widen health inequities. We highlight 3 examples- including recent policy changes in Medicaid, social needs, informed risk prediction models, and advances in precision medicine-where the inclusion of social risk information threatens to reduce care quality or health care access for some groups of patients. A new dialog is needed about both the opportunities and potential consequences of bringing information about patients' social circumstances into a market-based health care system.

Policy Points The number of social prescribing practices, which aim to link patients with nonmedical services and supports to address patients' social needs, is increasing in both England and the United States. Traditional health care financing mechanisms were not designed to support social prescribing practices, and flexible payment approaches may not support their widespread adoption. Policymakers in both countries are shifting toward developing explicit financing streams for social prescribing programs. Consequently, we need an evaluation of them to assess their success in supporting both the acceptance of these programs and their impacts. Investment in community-based organizations and wider public services will likely be crucial to both the long-term effectiveness and the sustainability of social prescribing.

Background

Over five million people in the USA miss or delay medical care because of a lack of transportation. Transportation barriers are especially relevant to Medicare Advantage (MA) health plan enrollees, who are more likely to live with multiple chronic conditions and experience mobility challenges. Non-Emergency Medical Transportation (NEMT) helps to address transportation gaps by providing rides to and from routine medical care (for example, medical appointments, laboratory tests, and pharmacy visits) and has been added as a supplemental benefit to some MA health plans.

Objective

We aimed to characterize MA enrollees' experiences with supplemental NEMT benefits.

Design

Qualitative interviews focused on participants' experiences with existing NEMT benefits, transportation, and mobility.

Participants

Twenty-one MA enrollees who used their MA NEMT benefit in 2019 and who remained eligible for ongoing transportation benefits through 2021.

Approach

Using purposive sampling from a list of eligible participants, we recruited individuals who used their MA NEMT benefit in 2019 and who remained eligible for benefit-covered transportation services through 2021.

Key results

Participants considered NEMT an essential service, particularly because these services helped them decrease social isolation, reduce financial insecurity, and manage their own medical needs. Navigating logistical challenges associated with arranging NEMT services required participants to commit considerable time and energy and limited the effectiveness and reliability of NEMT.

Conclusion

Participants described NEMT as a valued service essential to their ability to access health care. They suggested ways to increase service flexibility and reliability that could inform future NEMT policy and practice. As health systems and payers learn how to best address social risks, particularly as the US population ages, our findings underscore the importance of NEMT services and highlight opportunities to advance comprehensive transportation solutions for MA participants.

Despite social care interventions gaining traction in the US healthcare sector in recent years, the scaling of healthcare practices to address social adversity and coordinate care across sectors has been modest. Against this backdrop, the coronavirus pandemic arrived, which re-emphasized the interdependence of the health and social care sectors and motivated health systems to scale tools for identifying and addressing social needs. A framework on integrating social care into health care delivery developed by the National Academies of Science, Engineering, and Medicine provides a useful organizing tool to understand the social care integration innovations spurred by COVID-19, including novel approaches to social risk screening and social care interventions. As the effects of the pandemic are likely to exacerbate socioeconomic barriers to health, it is an appropriate time to apply lessons learned during the recent months to re-evaluate efforts to strengthen, scale, and sustain the health care sector's social care activities.

Health care leaders in the US are actively exploring strategies to identify and address patients' social and economic hardships as part of high-quality clinical care. The result has been a proliferation of screening tools and interventions related to patients' social determinants of health, but little guidance on effective strategies to implement them. Some of these tools rely on patient- or household-level screening data collected from patients during medical encounters. Other tools rely on data available at the neighborhood-level that can be used to characterize the environment in which patients live or to approximate patients' social or economic risks. Four case examples were selected from different health care organizations to illustrate strengths and limitations of using patient- or neighborhood-level social and economic needs data to inform a range of interventions. This work can guide health care investments in this rapidly evolving arena.

Interest and incentives are increasing around strategies whereby the health care sector can better identify and address patients' social and economic needs in the context of primary care delivery. This interest is likely to accelerate during the economic recession following the OVID-19 pandemic. Yet effective and sustainable strategies for integrating social care practices (eg, patient-facing social risk screening and activities to address identified needs) have not been clearly established. Lessons learned from more than 2 decades of research on behavioral health integration could be applied to efforts to integrate social care into primary care. In this article, we synthesize learnings from primary care and behavioral health care integration, and translate them into organizing principles with the goal of advancing social care integration practices to improve the health of both patients and communities.

Abundant evidence demonstrates that enduring, endemic racism plays an important role in determining patient health. This commentary reviews a patient case about disease self-management and subsequent health outcomes that are shaped by social and economic circumstances. We analyze the case using a framework for social care developed in 2019 by the National Academies of Sciences, Engineering, and Medicine (NASEM). We then propose that the NASEM framework be adapted by adding the category abolition, which could make the other social care practices transformative for historically marginalized populations.

Health care organizations increasingly recognize the impact of social needs on health outcomes. As organizations develop and scale efforts to address social needs, little is known about the optimal role for clinicians in providing social care. In this study, the authors aimed to understand how health care organizations involve clinicians in formal social care efforts. In 2019, the authors conducted 33 semi-structured interviews with administrators at 29 health care organizations. Interviews focused on the development and implementation of formal social care programs within the health care organization and the role of clinicians within those programs. A few administrators described formal roles for primary care clinicians in organizational efforts to deliver social care. Administrators frequently described programs that were deliberately structured to shield clinicians (eg, clinicians were not expected to review social risk screening results or be involved in addressing social needs). The authors identified 4 ways that administrators felt clinicians could meaningfully engage in social care programs: (1) discuss social risks to strengthen relationships with patients; (2) adjust clinical care follow-up plans based on social risks; (3) modify prescriptions based on social risks; and (4) refer patients to other care team members who can directly assist with social risks. Administrators were hesitant to increase primary care clinicians' responsibilities by tasking them with social care activities. Defining appropriate and scalable roles for clinicians along with adequate support from other care team members may increase the effectiveness of social care programs.

Background

Multiple studies have documented bias in medical decision making, but no studies have examined whether this bias extends to medical coding practices. Medical coding is foundational to the US health care enterprise. We evaluate whether bias based on patient characteristics influences specific coding practices of professional medical coders.

Methods

This is an online experimental study of members of a national professional medical coding organization. Participants were randomly assigned a set of six clinical scenarios reflecting common medical conditions and asked to report encounter level of service codes for these clinical scenarios. Clinical scenarios differed by patient demographics (race, age, gender, ability) or social context (food insecurity, housing security) but were otherwise identical. We estimated Ordinary Least Squares regression models to evaluate differences in outcome average visit level of service by patient demographic characteristics described in the clinical scenarios; we adjusted for coders' age, gender, race, and years of coding experience.

Results

The final analytic sample included 586 respondents who coded at least one clinical scenario. Higher mean level of service was assigned to clinical scenarios describing seniors compared to middle-aged patients in two otherwise identical scenarios, one a patient with type II diabetes mellitus (Coef: 0.28, SE: 0.15) and the other with rheumatoid arthritis (Coef: 0.30, SE: 0.13). Charts describing women were assigned lower level of service than men in patients with asthma exacerbation (Coef: -0.25, SE: 0.13) and rheumatoid arthritis (Coef: -0.20, SE: 0.12). There were no other significant differences in mean complexity score by patient demographics or social needs.

Conclusion

We found limited evidence of bias in professional medical coding practice by patient age and gender, though findings were inconsistent across medical conditions. Low levels of observed bias may reflect medical coding workflow and training practices. Future research is needed to better understand bias in coding and to identify effective and generalizable bias prevention practices.