November 24, 2020

In the United States, community health centers (CHCs) provide essential care to 30 million people in many of the nation’s most marginalized and hard-to-reach communities. Due to the relationship between social risk factors and health, there is growing emphasis on using electronic health records (EHR) to help identify and address patients’ these factors, also known as social determinants of health (SDH), such as food, housing, transportation, and financial resource strain.

Providers across the OCHIN network have already used the OCHIN Epic EHR to conduct more than 565,000 SDH screenings for 360,000 unique patients to date.

Despite the increased emphasis on social risk screening in the health care sector, no clear standard has emerged on how to implement social risk screening and there is limited evidence on the acceptability and impact of conducting social risk screening in clinical settings. Recognizing the challenge, cost, and time involved with implementing patient-level social risk screening initiatives, some health care systems are exploring strategies for using publicly available community-level data to identify patients with social risks.

A new cross-sectional study led by Dr. Erika Cottrell, an OCHIN Investigator and Assistant Professor at Oregon Health & Science University (OHSU), explored the utility of community-level data for accurately identifying patient-level social risks by comparing the social deprivation index score for the census tract where a patient lives with patient-level social risk screening data. It was conducted in partnership with Drs. Laura Gottlieb and Mathew Pantell, researchers at the University of California, San Francisco.

The study, entitled “Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers,” was recently published in the Journal of the American Medical Association’s Open Network. It found that among 36,578 patients with SDH screening, 10,858 (29.7%) reported one or more social risks. Of those, 60% lived in the most vulnerable quartile of census tracts nationally, as measured by the social deprivation index percentile score. However, 40% of patients reporting one or more social risks did not live in the most vulnerable quartile of census tracts. Overall, the accuracy of the social deprivation index quartile for identifying patients with and without social risks was 48%.

In other words, these findings suggest that using community-level social risk data to guide patient-level activities may result in missing some patients who can benefit from social risk–targeted or social risk–informed care. However, the authors note that community-level data can still be valuable if targeted appropriately. For example, previous OCHIN research examined how community-level social risk data can be used to help health systems account for patient social complexity when assessing provider and clinic-level quality metrics. They also suggest that future research is needed to understand how patient-level and community-level data can be used in concert to most effectively and efficiently invest limited resources.

Additional collaborators on this study include: Michelle Hendricks, Katie Dambrun, Stuart Cowburn, and Rachel Gold (OCHIN); Matthew Pantell and Laura Gottlieb (University of California, San Francisco). This research was supported by the Patient-Centered Outcomes Research Institute (PCORI) Health Demonstration Study and ASCEND.

To learn more, read the full article in JAMA Network Open.