Faculty Lead(s)
Faculty
BIME Student(s)
Project Summary
Identifying patients’ social needs is a first critical step for health systems to address social drivers of health (SDoH). Yet, SDoH screening is underused since it relies on arduous clinician entry and patient-entered questionnaires in the electronic health record (EHR). While these methods identify a subset of patients with social needs, they primarily capture patients who are already engaged in healthcare and don’t scale for enterprise-wide population health management, especially when considering patient populations who may need the greatest level of outreach. Emerging CMS mandates for health systems to report SDoH data as a quality metric for federal level tracking will only amplify the need to address barriers to screening. Innovative strategies could bolster screening efforts by filling data gaps for a fuller picture of upstream social factors impacting the health of the population.
We investigate auto suggestion as a novel strategy that surfaces social needs information previously documented in clinical notes in the EHR. The free text of clinical notes contains rich detail about patients’ social needs, but this SDoH data is scattered across patient encounters, difficult to find, and thus remains underutilized. Natural language processing (NLP) makes it technically feasible to automatically extract SDoH from clinical notes with high accuracy. With this automated approach “SDoH autosuggest” in mind, our team envisions a future in which the EHR surfaces SDoH automatically extracted from clinical notes, and pre-fills those social needs as auto-suggestions for users to accept/reject in clinical tools, such as Epic flowsheets, MyChart, health equity dashboards. Although SDoH autosuggestion could help facilitate and scale up social needs screening, SDoH data is sensitive. This study addresses how patients would feel about having their EHR mined for data to identify social needs.
The purpose of this project is to illuminate the voice of patients regarding potential use of “”SDoH auto-suggest”” in health care through three specific aims:
Aim 1. Co–produce inclusive study materials with clinical and community champions
Aim 2. Interview patients about their acceptability of SDoH auto-suggestion
Aim 3. Interview clinicians about their acceptability of SDoH auto-suggestion
This project also includes the following faculty members: Brian Wood, MD, Gary Hseih, PhD, Jared Klein, MD, and Herbie Duber, MD from the University of Washington.