ArticleApplied clinical informatics2016
Using Electronic Case Summaries to Elicit Multi-Disciplinary Expert Knowledge about Referrals to Post-Acute Care.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Identifying and Reducing Stigmatizing Language in Home Health Care With a Natural Language Processing-Based System (ENGAGE): Protocol for a Mixed Methods Study.JMIR research protocols · 2025Article
- Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Improving Patient Prioritization During Hospital-Homecare Transition: Protocol for a Mixed Methods Study of a Clinical Decision Support Tool Implementation.JMIR research protocols · 2021Article
- Factors Affecting Patient Prioritization Decisions at Admission to Home Healthcare: A Predictive Study to Develop a Risk Screening Tool.Computers, informatics, nursing : CIN · 2020Article
- Using a Decision Support Algorithm for Referrals to Post-Acute Care.Journal of the American Medical Directors Association · 2019Article
- Identifying Factors Associated With Mobility Decline Among Hospitalized Older Adults.Clinical nursing research · 2018Article
- Nurse Generated EHR Data Supports Post-Acute Care Referral Decision Making: Development and Validation of a Two-step Algorithm.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- High-value care for older adults with complex care needs: Leveraging nurses as innovators.Nursing outlookArticle
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Authors and funding
7 authors.
Funding
Abstract
backgroundEliciting knowledge from geographically dispersed experts given their time and scheduling constraints, while maintaining anonymity among them, presents multiple challenges.
objectivesDescribe an innovative, Internet based method to acquire knowledge from experts regarding patients who need post-acute referrals. Compare, 1) the percentage of patients referred by experts to percentage of patients actually referred by hospital clinicians, 2) experts' referral decisions by disciplines and geographic regions, and 3) most common factors deemed important by discipline.
methodsDe-identified case studies, developed from electronic health records (EHR), contained a comprehensive description of 1,496 acute care inpatients. In teams of three, physicians, nurses, social workers, and physical therapists reviewed case studies and assessed the need for post-acute care referrals; Delphi rounds followed when team members did not agree. Generalized estimating equations (GEEs) compared experts' decisions by discipline, region of the country and to the decisions made by study hospital clinicians, adjusting for the repeated observations from each expert and case. Frequencies determined the most common case characteristics chosen as important by the experts.
resultsThe experts recommended referral for 80% of the cases; the actual discharge disposition of the patients showed referrals for 67%. Experts from the Northeast and Midwest referred 5% more cases than experts from the West. Physicians and nurses referred patients at similar rates while both referred more often than social workers. Differences by discipline were seen in the factors identified as important to the decision.
conclusionThe method for eliciting expert knowledge enabled national dispersed expert clinicians to anonymously review case summaries and make decisions about post-acute care referrals. Having time and a comprehensive case summary may have assisted experts to identify more patients in need of post-acute care than the hospital clinicians. The methodology produced the data needed to develop an expert decision support system for discharge planning.
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