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新生儿科工作量(WORKLINE):一种集成到电子健康记录中的临床医生工作量测量系统的验证与可行性研究

Workload In Neonatology (WORKLINE): Validation and feasibility of a system for measuring clinician workload integrated into the electronic health record.

作者信息

Dye M Eva, Runyan Patti, Scott Theresa A, Dietrich Mary S, Hatch L Dupree, France Daniel, Alrifai Mhd Wael

机构信息

Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA.

Department of Pediatrics, Division of Neonatology, Vanderbilt University Medical Center, Nashville, TN, USA.

出版信息

J Perinatol. 2023 Jul;43(7):936-942. doi: 10.1038/s41372-023-01678-5. Epub 2023 May 2.

Abstract

OBJECTIVE

The purpose of the study was to validate WORKLINE, a NICU specific clinician workload model and to evaluate the feasibility of integrating WORKLINE into our EHR.

STUDY DESIGN

This was a prospective, observational study of the workload of 42 APPs and physicians in a large academic medical center NICU over a 6-month period. We used regression models with robust clustered standard errors to test associations of WORKLINE values with NASA Task Load Index (NASA-TLX) scores.

RESULTS

We found significant correlations between WORKLINE and NASA-TLX scores. APP caseload was not significantly associated with WORKLINE scores. We successfully integrated the WORKLINE model into our EHR to automatically generate workload scores.

CONCLUSION

WORKLINE provides an objective method to quantify the workload of clinicians in the NICU, and for APPs, performed better than caseload numbers to reflect workload. Integrating the WORKLINE model into the EHR was feasible and enabled automated workload scores.

摘要

目的

本研究旨在验证WORKLINE(一种针对新生儿重症监护病房(NICU)的临床医生工作量模型),并评估将WORKLINE整合到我们电子健康记录(EHR)中的可行性。

研究设计

这是一项前瞻性观察性研究,对一家大型学术医疗中心新生儿重症监护病房的42名高级实践提供者(APP)和医生在6个月期间的工作量进行了研究。我们使用具有稳健聚类标准误差的回归模型来测试WORKLINE值与美国国家航空航天局任务负荷指数(NASA-TLX)得分之间的关联。

结果

我们发现WORKLINE与NASA-TLX得分之间存在显著相关性。APP的病例数与WORKLINE得分无显著关联。我们成功地将WORKLINE模型整合到我们的电子健康记录中,以自动生成工作量得分。

结论

WORKLINE提供了一种客观方法来量化新生儿重症监护病房临床医生的工作量,对于高级实践提供者而言,在反映工作量方面比病例数表现更好。将WORKLINE模型整合到电子健康记录中是可行的,并能实现工作量得分的自动化。

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