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Advancing the Learning Health System by Incorporating Social Determinants
Deepak Palakshappa, MD, MSHP; David P. Miller Jr, MD, MS; and Gary E. Rosenthal, MD
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Advancing the Learning Health System by Incorporating Social Determinants

Deepak Palakshappa, MD, MSHP; David P. Miller Jr, MD, MS; and Gary E. Rosenthal, MD
Without integrating data on the social determinants of health, the learning health system could fail to reach its mission of higher-quality, safer, and more efficient care.
Delivery of personalized care. In 2015, the National Institutes of Health launched the Precision Medicine Initiative (PMI) to understand the drivers of health and develop personalized treatments. A central element of the PMI is the All of Us Research Program, which plans to enroll 1 million adults to investigate how differences in genomics, environment, and lifestyle can contribute to the development of precision medicine treatments.22 Although not in the original LHS framework, the NAM published a 2015 workshop summary describing the inclusion of genomic data in the LHS.23 The LHS offers an opportunity to understand how integration of genomic data affects healthcare delivery.24 As an example, Geisinger Health is integrating individual genomic data within the EHR to inform patient care about disease risk.25 However, social risk factors often have a greater influence on disease occurrence than genetic risk factors. For the genomics-enabled LHS to provide precision medicine, it must account for these social factors that affect health and healthcare.26 Further, an LHS that combines data on individuals’ genomics and social risk factors can better elucidate causal pathways leading to health disparities and tailor the delivery of care.

Evaluate the effectiveness of care. A further goal of the LHS is to close the gap between evidence generation and clinical care, particularly the lack of generalizability of randomized controlled trials.2,3 One method to accomplish this is through pragmatic trials that evaluate the effectiveness of treatments in real-world settings. The LHS, with its EHR and robust data collection systems, is an ideal setting for pragmatic trials.27 However, without accounting for patients’ social factors, evaluating the effectiveness of treatments in the LHS could be incomplete. For example, one study evaluated the effect of neighborhood poverty on the impact of a primary care practice–based trial for depression. The authors found that the treatment effect weakened after the initial 4 months among participants living in high-poverty neighborhoods but not among participants in other neighborhoods.28 Thus, the failure to account for social risk factors could lead to effective treatments and novel healthcare delivery strategies showing minimal benefit as a result of unmeasured SDH. Similarly, collecting SDH data could shed light on ways in which social factors mediate the effect of treatments on health.29 Intermountain Healthcare is an LHS that is using an area-level deprivation index to evaluate heterogeneity in medication adherence among patients with hypertension.30


The LHS is a powerful framework for improving the cost and quality of healthcare. However, realizing the full potential of the LHS will require moving beyond the traditional boundaries of healthcare and developing innovative approaches to reduce social risk factors. Although the evidence base for the effectiveness of such approaches on improving health is currently limited, early results suggest that addressing patients’ unmet social needs can decrease costs and improve outcomes. We believe it is imperative that the LHS add to this evidence. An LHS that is able to rapidly evaluate and improve care could be an ideal setting in which to pilot interventions reducing social risk factors. This process should begin with the routine collection of SDH data. Making these data a part of the LHS should enhance patient engagement, allow for tailoring of care, and more accurately evaluate the care experience.

Author Affiliations: Department of Internal Medicine (DP, DPM, GER), Department of Pediatrics (DP), and Division of Public Health Sciences (DP, DPM), Wake Forest School of Medicine, Winston-Salem, NC.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DP, DPM, GER); drafting of the manuscript (DP, DPM, GER); and critical revision of the manuscript for important intellectual content (DP, DPM, GER).

Address Correspondence to: Deepak Palakshappa, MD, MSHP, Wake Forest School of Medicine, Medical Center Blvd, Winston-Salem, NC 27157. Email:

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