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提高住院医师教育水平和患者安全:一种平衡学年末交接初始病例量的方法。

Improving resident education and patient safety: a method to balance initial caseloads at academic year-end transfer.

机构信息

Department of Psychiatry, UCSF School of Medicine, San Francisco, California 94143, USA.

出版信息

Acad Med. 2010 Sep;85(9):1418-24. doi: 10.1097/ACM.0b013e3181eab8d0.

Abstract

PURPOSE

In outpatient continuity clinics, incoming trainees may receive caseloads that are unbalanced in terms of the mental workload required from each resident. When significant, these imbalances may compromise resident learning and patient safety. Using data from psychiatric outpatient continuity clinics, this study tested a method for balancing initial caseloads.

METHOD

Adapting prior research on mental workload, the authors developed and implemented a workload-balancing method to balance initial caseloads regarding factors contributing to mental workload: number of patients, number of acute patients, complexity/time demands outside clinic, visits per month, and collaboration demands. For academic years 2006-2007, 2007-2008, 2008-2009, and 2009-2010, they compared these balanced caseloads with those that would have been created by the clinic's traditional method of largely preserving prior caseloads (with some redistribution to balance only the number of patients). The outcome measure was the intercaseload coefficient of variation for each of the chosen mental workload factors and for all factors combined.

RESULTS

Compared with the traditional method, the workload-balancing method generated lower intercaseload variation for each mental workload factor. Also, this method reduced overall intercaseload variation for all factors combined by 50% to 61% in each of the intervention years.

CONCLUSIONS

The workload-balancing method evenly distributes among resident panels factors known to contribute to mental workload. This method may reduce errors and stress likely to occur when residents inherit unbalanced caseloads that are overly challenging and, thus, may improve patient safety and resident learning. This model could be applicable to other caseload situations.

摘要

目的

在门诊连续性诊所中,新入职的受训者可能会接到病例量分配不均的任务,这会导致每位住院医师的精神工作负荷不平衡。如果不平衡程度较大,可能会影响住院医师的学习和患者安全。本研究利用精神科门诊连续性诊所的数据,测试了一种平衡初始病例量的方法。

方法

本研究借鉴先前关于精神工作负荷的研究,开发并实施了一种平衡初始病例量的方法,该方法平衡了导致精神工作负荷的因素,包括患者人数、急性患者人数、诊所外的复杂/时间需求、每月就诊次数和协作需求。在 2006-2007、2007-2008、2008-2009 和 2009-2010 学年中,与传统方法(主要通过重新分配来平衡患者人数,同时保留部分原有病例量)相比,研究人员比较了平衡后的病例量与传统方法下产生的病例量。选择的精神工作负荷因素的衡量标准是每个病例量的变异系数,以及所有因素的综合变异系数。

结果

与传统方法相比,平衡工作量的方法降低了每个精神工作负荷因素的病例量变异。此外,这种方法在干预年份中,所有因素的综合变异系数降低了 50%至 61%。

结论

平衡工作量的方法将已知会导致精神工作负荷的因素在住院医师的病例量中均匀分配。该方法可能会减少因继承不平衡的病例量而导致的错误和压力,这些病例量过大且具有挑战性,从而提高患者安全和住院医师学习。该模型可能适用于其他病例量情况。

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