• Laura A Schoenherr, Yuika Goto, Joanna Sharpless, David L O'Riordan, and Steven Z Pantilat.
• Division of Palliative Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, USA.
• J Palliat Med. 2024 Nov 28.

AbstractBackground: Population-based methods to identify patients with serious illness are necessary to provide equitable and efficient access to palliative care services. Aim: Create a validated algorithm embedded in the electronic medical record (EMR) to identify hospitalized patients with serious illness. Design: An initial algorithm, developed from literature review and clinical experience, was twice adjusted based on gaps identified from chart review. Each iteration was validated by comparing the algorithm's results for a subset of patients (approximately 10% of the populations screened in and screened out on a given day) with the expert consensus of two independent palliative care physicians. Settings/Subjects: The final algorithm was run daily for nine months to screen all hospitalized adults at our academic medical center in the United States. Results: Compared with the gold standard of expert consensus, the final algorithm for identifying hospitalized patients with serious illness was found to have a sensitivity of 89%, specificity of 82%, positive predictive value of 80%, and negative predictive value of 90%. At our hospital, an average of 284 patients a day (54%) screened positive for at least one criterion, with an average of 38 patients newly screening positive daily. Conclusions: Data from the EMR can identify hospitalized patients with serious illness who may benefit from palliative care services, an important first step in moving to a system in which palliative care is provided proactively and systematically to all who could benefit.

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