Abstract
Background: Social determinants of health (SDOH) such as language preference, health literacy, housing access, food insecurity, social isolation and supports, transportation, depression and addiction, can significantly impact health outcomes and exacerbate disparities surrounding care transitions in and out of hospital. There is growing momentum among healthcare systems to capture SDOH through point-of-care surveys for implementation efforts. However, the sporadic, unstructured nature to these surveys when patients are acutely unwell can lead to low response rates. To be more effective, these efforts can benefit from a systematic approach to capturing SDOH in electronic health records (EHRs).
Approach: A comprehensive list of social determinants of health relevant to care transitions was informed based on literature review across different countries then narrowed down to balance feasibility of capture among health records at point of care in two provinces (Ontario and Alberta). A random sample of 3075 charts were then manually reviewed among admitted patients enrolled in integrated care program supporting patients around an acute care admission for these SDOH. Additionally, A specialized keyword list was made to narrow down the search within EHRs. The keywords were selected based on their relevance to the SDOH as highlighted in the literature and based on similar social determinants of health research conducted across other countries.
Result: The hospital-level survey (N=3075 of ICP participants) demonstrated poor response and capture of several SDOH, particularly for income-related SDOH. However, the use of chart records (including admission, consultant and other point of care notes) demonstrated feasible and usable capture of key search terms for SDOH. Of the 50 patient charts reviewed so far, 45 individuals had SDOH captured in chart-level records, with the majority of these being related to language barriers and a minority being related to transportation access. Similarly, other SDOH also showed equally low response rates in the survey. These results imply that although it is possible to capture SDOH from electronic health records, existing approaches need significant improvement to become more efficient and scalable.
Implication: The use of health care records from clinicians documenting at point-of-care presents a unique opportunity for capturing SDOH in electronic health record systems. Shared learnings from this project will greatly widen institutions with EHRs feasibility and success of capturing SDOH for the evaluation, refinement and spread of integrated care models surrounding acute care admissions. Next steps include collaboration with decision support and analytic teams across Ontario and Calgary to develop machine learning algorithms with the use of natural language processing to perform more extensive data pulls and analytics. This process will also involve implementing the list of keywords that the algorithms will use to improve the accuracy and range of data extraction. These steps would capture a larger and more accurate number of social needs.