In the United States, community health centers (CHCs) provide essential care to 30 million people in many of the nation’s most hard-to-reach communities. Due to the relationship between non-clinical drivers of health outcomes and clinical health, there is growing emphasis on using electronic health records (EHR) to help identify and address these factors, also known as contextual factors influencing health outcomes, 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 non-clinical drivers of health outcomes screenings for 360,000 unique patients to date.

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

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 contextual factors influencing health outcomes. It was conducted in partnership with Drs. Laura Gottlieb and Mathew Pantell, researchers at the University of California, San Francisco.

The study was recently published in the Journal of the American Medical Association’s Open Network. It found that among 36,578 patients with non-clinical drivers of health screening, 10,858 (29.7%) reported one or more contextual factor influencing health outcomes. 40% of patients reporting one or more factors did not live in the most vulnerable quartile of census tracts. Overall, the accuracy of the quartile for identifying patients with and without contextual factors influencing health outcomes was 48%.

In other words, these findings suggest that using community-level data on non-clinical drivers of health outcomes to guide patient-level activities may result in missing some patients who can benefit from targeted or informed non-clinical care. However, the authors note that community-level data can still be valuable if targeted appropriately. For example, previous OCHIN research examined how data on community-level non-clinical drivers of health outcomes can be used to help health systems account for patient 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.