Tsai-Ling Liu, Timothy C Hetherington, Marc Kowalkowski, Marvin E Knight, Jamayla Culpepper, Andrew McWilliams, Shih-Hsiung Chou, McKenzie Isreal, Stephanie Murphy
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引用次数: 0
Abstract
Identifying appropriate patients for hospital at Home (HaH) is challenged by the extensive inpatient population, the dynamic nature of hospitalizations, and the eligibility window for entry into the care model. This study presents the development of a rules-based algorithm (RBA) leveraging electronic health record (EHR) data to improve HaH patient identification, which is crucial for efficient HaH operations. RBA adjustments incorporated clinician feedback to align analytics resources and enhance clinical workflows. Our study highlights the importance of interdisciplinary collaboration and the potential for analytics to optimize efficiency for emerging care models.
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