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Abstract: Objective: Build and validate a clinical decision support (CDS) algorithm for discharge decisions regarding referral for post-acute care (PAC) and to what site of care. Materials and Methods: Case studies derived from EHR data were judged by 171 interdisciplinary experts and prediction models were generated. Results: A two-step algorithm emerged with area under the curve (AUC) in validation of 91.5% (yes/no refer) and AUC 89.7% (where to refer). Discussion: CDS for discharge planning (DP) decisions may remove subjectivity, and variation in decision-making. CDS could automate the assessment process and alert clinicians of high need patients earlier in the hospital stay. Conclusion: Our team successfully built and validated a two-step algorithm to support discharge referral decision-making from EHR data. Getting patients the care and support they need may decrease readmissions and other adverse events. Further work is underway to test the effects of the CDS on patient outcomes in two hospitals.

Learning Objective 1: Explain the development and validation of a CDS for discharge planning.

Learning Objective 2: Determine the patient characteristics associated with the need for post-acute care.

Authors:

Kathryn Bowles (Presenter)
University of Pennsylvania

Sarah Ratcliffe, University of Pennsylvania
Mary Naylor, University of Pennsylvania
John Holmes, University of Pennsylvania
Susan Keim, University of Pennsylvania
Emilia Flores, University of Pennsylvania

Presentation Materials:

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