scholarly journals International Classification of Diseases, Tenth Revision, Clinical Modification social determinants of health codes are poorly used in electronic health records

Medicine ◽  
2020 ◽  
Vol 99 (52) ◽  
pp. e23818
Author(s):  
Yi Guo ◽  
Zhaoyi Chen ◽  
Ke Xu ◽  
Thomas J. George ◽  
Yonghui Wu ◽  
...  
2017 ◽  
Vol 57 (9) ◽  
pp. 1041-1052 ◽  
Author(s):  
Canan Karatekin ◽  
Brandon Almy ◽  
Susan Marshall Mason ◽  
Iris Borowsky ◽  
Andrew Barnes

International Classification of Diseases codes for child maltreatment can aid surveillance and research, but the extent to which they are used is not well established. We documented prevalence of the use of maltreatment-related codes, examined demographic characteristics of youth assigned these codes, and compared results with previous studies. Data were extracted from electronic health records of 0- to 21-year-olds assigned 1 of 15 maltreatment-related International Classification of Diseases, Ninth Revision, codes who had encounters in a large medical system over a 4-year period. Only 0.02% of approximately 2.5 million youth had a maltreatment-related code, replicating other studies. Results provide a dramatic contrast to much higher rates based on self-report or informant-report and referrals to Child Protective Services. Lack of documentation of maltreatment in electronic health records can lead to missed chances at early intervention, inadequate coordination of health care, insufficient allocation of resources to addressing problems related to maltreatment, and flawed public health data.


2017 ◽  
Vol 25 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Cosmin A Bejan ◽  
John Angiolillo ◽  
Douglas Conway ◽  
Robertson Nash ◽  
Jana K Shirey-Rice ◽  
...  

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.


2020 ◽  
Vol 27 (11) ◽  
pp. 1764-1773 ◽  
Author(s):  
Min Chen ◽  
Xuan Tan ◽  
Rema Padman

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.


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