Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jan 31, 2023
Date Accepted: Jun 10, 2023
Comparative Analysis of Social Determinants of Health Documentation in Structured and Unstructured Clinical Data of Patients with Diabetes
ABSTRACT
Background:
Electronic health records (EHRs) have struggled to fully capture social determinants of health (SDOH) due to challenges such as nonexistent or inconsistent data capture tools across clinics, lack of time, and the burden of extra steps for the clinician. However, patient clinical notes (unstructured data) may be a better source of patient-related SDOH information.
Objective:
It is unclear how accurately EHR data reflect patients’ lived experience of SDOH. Manual process of retrieving SDOH information from clinical notes is timely and not feasible. We leveraged two high-throughput tools to identify SDOH mappings to structured and unstructured patient data, PatientExploreR and Electronic Medical Record Search Engine (EMERSE).
Methods:
We included adult patients (≥18 years) receiving primary care for their diabetes at UCSF from January 1, 2018, to December 31, 2019. We used expert raters to develop a corpus using SDOH in the Compendium as a knowledge base as targets for the natural language processing (NLP) text string mapping to find string stems, roots, and syntactic similarities in the clinical notes of patients with diabetes. We applied advanced built-in EMERSE NLP query parsers implemented with JavaCC.
Results:
We included 4,283 adult patients receiving primary care for diabetes at UCSF. Our study revealed that SDOH may be more significant in the lives of patients with diabetes than is evident from structured data recorded on EHRs. With the application of EMERSE NLP rules, we uncovered additional information on problems related to social connections/isolation, employment, financial insecurity, housing insecurity, food insecurity, education, and stress, within patient clinical notes.
Conclusions:
We discovered more patient information related to SDOH in unstructured data as compared to structured data. Application of this technique and further investment in similar, user-friendly tools and infrastructure to extract SDOH information from unstructured data, may help to identify the range of social conditions that influence patients’ disease experiences and inform clinical decision-making.
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