Integrating Data On Social Determinants Of Health Into Electronic Health Records

2018 ◽  
Vol 37 (4) ◽  
pp. 585-590 ◽  
Author(s):  
Michael N. Cantor ◽  
Lorna Thorpe
Keyword(s):  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Health Records ◽  
Electronic Health

scholarly journals A Road Map to Integrate Social Determinants of Health into Electronic Health Records

2017 ◽  
Vol 20 (6) ◽  
pp. 424-426 ◽  
Author(s):  
Ana Palacio ◽  
Maritza Suarez ◽  
Leonardo Tamariz ◽  
David Seo
Keyword(s):  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Health Records ◽  
Road Map ◽  
Electronic Health

scholarly journals Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records

2017 ◽  
Vol 25 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Cosmin A Bejan ◽  
John Angiolillo ◽  
Douglas Conway ◽  
Robertson Nash ◽  
Jana K Shirey-Rice ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Adverse Childhood Experiences ◽  
Determinants Of Health ◽  
Homeless Population ◽  
Health Records ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Electronic Health

Abstract Objective Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository. Materials and Methods We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE. Results word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC = 0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC = 0.95) than ACE (AUPRC = 0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being “father” (21.8%) and “mother” (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%–47.6%). Conclusion We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.

scholarly journals Incorporating Social Determinants of Health in Electronic Health Records: Qualitative Study of Current Practices Among Top Vendors

10.2196/13849 ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. e13849 ◽  
Author(s):  
Maysoun Freij ◽  
Prashila Dullabh ◽  
Sarah Lewis ◽  
Scott R Smith ◽  
Lauren Hovey ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Health Records ◽  
Electronic Health ◽  
Current Practices

scholarly journals Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review

2020 ◽  
Vol 27 (11) ◽  
pp. 1764-1773 ◽  
Author(s):  
Min Chen ◽  
Xuan Tan ◽  
Rema Padman
Keyword(s):  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Risk Prediction ◽  
Social Determinants ◽  
English Language ◽  
Determinants Of Health ◽  
Screening Tools ◽  
Health Records ◽  
Individual Level ◽  
Electronic Health

Abstract Objective This integrative review identifies and analyzes the extant literature to examine the integration of social determinants of health (SDoH) domains into electronic health records (EHRs), their impact on risk prediction, and the specific outcomes and SDoH domains that have been tracked. Materials and Methods In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a literature search in the PubMed, CINAHL, Cochrane, EMBASE, and PsycINFO databases for English language studies published until March 2020 that examined SDoH domains in the context of EHRs. Results Our search strategy identified 71 unique studies that are directly related to the research questions. 75% of the included studies were published since 2017, and 68% were U.S.-based. 79% of the reviewed articles integrated SDoH information from external data sources into EHRs, and the rest of them extracted SDoH information from unstructured clinical notes in the EHRs. We found that all but 1 study using external area-level SDoH data reported minimum contribution to performance improvement in the predictive models. In contrast, studies that incorporated individual-level SDoH data reported improved predictive performance of various outcomes such as service referrals, medication adherence, and risk of 30-day readmission. We also found little consensus on the SDoH measures used in the literature and current screening tools. Conclusions The literature provides early and rapidly growing evidence that integrating individual-level SDoH into EHRs can assist in risk assessment and predicting healthcare utilization and health outcomes, which further motivates efforts to collect and standardize patient-level SDoH information.

scholarly journals Meaningful Information in the Age of Big Data: A Scoping Review on Social Determinants of Health Data Collection for Electronic Health Records

2019 ◽  
Author(s):  
Kelsey Berg ◽  
Chelsea Doktorchik ◽  
Hude Quan ◽  
Vineet Saini
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Health Systems ◽  
Social Determinants ◽  
Scoping Review ◽  
Determinants Of Health ◽  
Health Records ◽  
Automated Technology

Abstract Background: Electronic Health Records (EHRs) are key tools for integrating patient data into health information systems (IS). Advances in automated data collection methodology, particularly the collection of social determinants of health (SDOH), provide opportunities to advance health promotion and illness prevention through advanced analytics (i.e. “Big Data” techniques). We ask how current data collection processes in EHRs permit SDOH data to flow throughout health systems. Methods: Using a scoping review framework, we searched through medical literature to identify current practices in SDOH data collection within EHR systems. We extracted relevant information on data collection methodology, specifically focusing on uses of automated technology. We discuss our findings in the context of research methodology and potential for health equity. Results: Practitioners collect a variety of SDOH data at point of care through EHR, predominantly via embedded screening tools and clinical notes, and primarily capturing data on financial security, housing status, and social support. Health systems are increasingly using digital technology in data collection, including natural language processing algorithms. However overall use of automated technology is limited to date. End uses of data pertain to improving system efficiency, patient care-coordination, and addressing health disparities. Discussion & Conclusion: EHRs can realistically promote collection and meaningful use of SDOH data, although EHRs have not extensively been used to collect and manage this type of information. Future applied research on systems-level application of SDOH data is necessary, and should incorporate a range of stakeholders and interdisciplinary teams of researchers and practitioners in fields of health, computing, and social sciences.

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