Acceptance of Automated Social Risk Scoring in the Emergency Department: Clinician, Staff, and Patient Perspectives.
Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened...
| Publicado en: | Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health Vol. 25; no. 4; pp. 614 - 624 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health
Jul2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178448604&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178448604 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1936900X 8B61 jtl: Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health issn: 1936900X maglogo: N pubinfo: dt: Jul2024 vid: 25 iid: 4 pid: 29816 pub: Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health place: Orange, California artinfo: ui: 178448604 178448604 178448604 10.5811/westjem.18577 178448604 ppf: 614 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Acceptance of Automated Social Risk Scoring in the Emergency Department: Clinician, Staff, and Patient Perspectives. aug: au: Mazurenko, Olena Hirsh, Adam T. Harle, Christopher A. McNamee, Cassidy Vest, Joshua R. affil: Indiana University, Richard M. Fairbanks School of Public Health, Department of Health Policy and Management, Indianapolis, Indiana. sug: subj: Emergency Service Social Determinants of Health Psychosocial Factors Health Services Needs and Demand Risk Assessment Methods Automation Prediction Models Personnel, Health Facility Attitude of Health Personnel Evaluation Patient Attitudes Evaluation Human Health Status Disparities Funding Source Semi-Structured Interview Medical Practice Safety-Net Providers United States Thematic Analysis Coding Patient-Reported Outcomes Truth Disclosure Honesty ab: Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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