Ingraham Nicholas E, Shyu Daniel, Phelan Tom, Mesfin Nathan, Langworthy Benjamin, Kohn Rachel, Kerlin Meeta Prasad, Dudley R Adams
Department of Medicine, University of Minnesota, Minneapolis, MN.
M Health Fairview, Minneapolis, MN.
Crit Care Explor. 2024 Dec 19;6(12):e1189. doi: 10.1097/CCE.0000000000001189. eCollection 2024 Dec 1.
Providers vary in their impact on clinical outcomes, but this is rarely accounted for in healthcare research. By failing to identify the provider responsible for a patient's care, investigators miss an opportunity to account for nonrandom variation in outcomes. Prior methods of identifying responsible providers have relied on manual chart review, which is time-consuming and expensive, or analysis of claims data, which has been demonstrated to be inaccurate. To address these gaps, we sought to develop an algorithm using electronic health record (EHR) data to identify the responsible provider for each day of a patient's hospitalization.
A multicenter retrospective cohort study.
Midwest healthcare system.
Hospitalized patients and their providers.
None.
We first confirmed high inter-rater reliability of manual chart review to identify the responsible provider. Using manual chart review as the gold standard, we then assessed the accuracy of an automated algorithm in a set of randomly selected patients. The agreement between two independent physicians in their determination of the responsible provider by chart review was 100%. Among 200 randomly selected patients, the algorithm identified the same responsible provider as the physician chart reviewer on 93% (3372/3626; 95% CI, 92-94%) of patient-days.
Readily available EHR data can be used to assign patients to providers daily with a high degree of accuracy. This methodology could be applied in healthcare research to identify sources of variation other than the intervention being studied.
医疗服务提供者对临床结果的影响各不相同,但在医疗保健研究中很少对此加以考虑。由于未能确定负责患者护理的提供者,研究人员错失了一个解释结果中非随机变异的机会。以往识别责任提供者的方法依赖于人工病历审查,这既耗时又昂贵,或者依赖于对索赔数据的分析,而后者已被证明是不准确的。为了填补这些空白,我们试图开发一种算法,利用电子健康记录(EHR)数据来确定患者住院期间每一天的责任提供者。
一项多中心回顾性队列研究。
中西部医疗系统。
住院患者及其医疗服务提供者。
无。
我们首先确认了人工病历审查在识别责任提供者方面具有较高的评分者间信度。以人工病历审查作为金标准,我们随后在一组随机选择的患者中评估了一种自动化算法的准确性。两位独立医生通过病历审查确定责任提供者的一致性为100%。在200名随机选择的患者中,该算法在93%(3372/3626;95%CI,92-94%)的患者日中识别出与病历审查医生相同的责任提供者。
现成的电子健康记录数据可用于每天以高度准确性将患者分配给提供者。这种方法可应用于医疗保健研究,以识别除所研究的干预措施之外的变异来源。