Schoenherr Laura A, Goto Yuika, Sharpless Joanna, O'Riordan David L, Pantilat Steven Z
Division of Palliative Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, USA.
J Palliat Med. 2025 Feb;28(2):201-206. doi: 10.1089/jpm.2024.0285. Epub 2024 Nov 28.
Population-based methods to identify patients with serious illness are necessary to provide equitable and efficient access to palliative care services. Create a validated algorithm embedded in the electronic medical record (EMR) to identify hospitalized patients with serious illness. 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. The final algorithm was run daily for nine months to screen all hospitalized adults at our academic medical center in the United States. 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. 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.
基于人群的方法来识别重症患者对于公平且高效地提供姑息治疗服务而言是必要的。创建一个嵌入电子病历（EMR）的经过验证的算法，以识别住院的重症患者。最初基于文献综述和临床经验开发的算法，根据病历审查发现的差距进行了两次调整。每次迭代都通过将算法针对一部分患者（给定日期筛选入组和筛选出组的人群中约10%）的结果与两名独立姑息治疗医生的专家共识进行比较来验证。最终算法在美国我们的学术医疗中心每天运行九个月，以筛查所有住院的成年人。与专家共识的金标准相比，识别住院重症患者的最终算法的灵敏度为89%，特异度为82%，阳性预测值为80%，阴性预测值为90%。在我们医院，平均每天有284名患者（54%）至少有一项标准筛查呈阳性，平均每天有38名患者新筛查呈阳性。电子病历中的数据可以识别可能从姑息治疗服务中受益的住院重症患者，这是迈向为所有可能受益的人主动且系统地提供姑息治疗的系统的重要第一步。
