A Validated Electronic Medical Record-Based Algorithm to Identify Hospitalized Patients with Serious Illness

<b><i>Background:</i></b> Population-based methods to identify patients with serious illness are necessary to provide equitable and efficient access to palliative care services. <b><i>Aim:</i></b> Create a validated algorithm embedded in the electronic medical record (EMR) to identify hospitalized patients with serious illness. <b><i>Design:</i></b> 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. <b><i>Settings/Subjects:</i></b> The final algorithm was run daily for nine months to screen all hospitalized adults at our academic medical center in the United States. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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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Publication Details

Journal
Journal of Palliative Medicine
Published
2024-11-28
DOI
https://doi.org/10.1089/jpm.2024.0285
Primary Topic
Palliative Care and End-of-Life Issues
Type
article
Field-Weighted Citation Impact
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article

A Validated Electronic Medical Record-Based Algorithm to Identify Hospitalized Patients with Serious Illness

Yuika Goto, Steven Z. Pantilat, David L. O’Riordan, Laura A. Schoenherr et al.
Journal of Palliative Medicine
Palliative Care and End-of-Life Issues
article

A Validated Electronic Medical Record-Based Algorithm to Identify Hospitalized Patients with Serious Illness

Yuika Goto, Steven Z. Pantilat, David L. O’Riordan, Laura A. Schoenherr, Joanna Sharpless
article en

Abstract

<b><i>Background:</i></b> Population-based methods to identify patients with serious illness are necessary to provide equitable and efficient access to palliative care services. <b><i>Aim:</i></b> Create a validated algorithm embedded in the electronic medical record (EMR) to identify hospitalized patients with serious illness. <b><i>Design:</i></b> 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. <b><i>Settings/Subjects:</i></b> The final algorithm was run daily for nine months to screen all hospitalized adults at our academic medical center in the United States. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.

Journal of Palliative MedicineVol. 28(2)
University of California, San Francisco (US)
Openalex Percentile: Top 67%
Palliative Care and End-of-Life Issues
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