Chenyu Li, Danielle L Mowery, Xiaomeng Ma, Rui Yang, Ugurcan Vurgun, Sy Hwang, Hayoung K Donnelly, Harsh Bandhey, Yalini Senathirajah, Shyam Visweswaran, Eugene M Sadhu, Zohaib Akhtar, Emily Getzen, Philip J Freda, Qi Long, Michael J Becich
{"title":"发挥电子病历系统中社会决定因素数据的潜力:对筛选、链接、提取、分析和干预方法的范围审查。","authors":"Chenyu Li, Danielle L Mowery, Xiaomeng Ma, Rui Yang, Ugurcan Vurgun, Sy Hwang, Hayoung K Donnelly, Harsh Bandhey, Yalini Senathirajah, Shyam Visweswaran, Eugene M Sadhu, Zohaib Akhtar, Emily Getzen, Philip J Freda, Qi Long, Michael J Becich","doi":"10.1017/cts.2024.571","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.</p><p><strong>Methods: </strong>We conducted a PubMed search using \"SDOH\" and \"EHR\" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.</p><p><strong>Results: </strong>Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.</p><p><strong>Discussion: </strong>Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"8 1","pages":"e147"},"PeriodicalIF":2.1000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523026/pdf/","citationCount":"0","resultStr":"{\"title\":\"Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.\",\"authors\":\"Chenyu Li, Danielle L Mowery, Xiaomeng Ma, Rui Yang, Ugurcan Vurgun, Sy Hwang, Hayoung K Donnelly, Harsh Bandhey, Yalini Senathirajah, Shyam Visweswaran, Eugene M Sadhu, Zohaib Akhtar, Emily Getzen, Philip J Freda, Qi Long, Michael J Becich\",\"doi\":\"10.1017/cts.2024.571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.</p><p><strong>Methods: </strong>We conducted a PubMed search using \\\"SDOH\\\" and \\\"EHR\\\" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.</p><p><strong>Results: </strong>Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.</p><p><strong>Discussion: </strong>Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.</p>\",\"PeriodicalId\":15529,\"journal\":{\"name\":\"Journal of Clinical and Translational Science\",\"volume\":\"8 1\",\"pages\":\"e147\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523026/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical and Translational Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/cts.2024.571\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical and Translational Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/cts.2024.571","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.
Background: Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.
Methods: We conducted a PubMed search using "SDOH" and "EHR" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.
Results: Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.
Discussion: Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.