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. 2023 Jul 19;23(1):775.
doi: 10.1186/s12913-023-09772-2.

Measuring and improving performance of clinicians: an application of patient-based records

Minye Dong #  1   2 , Yuyin Xiao #  1   2 , Chenshu Shi  2 , Guohong Li  3   4
Affiliations

Measuring and improving performance of clinicians: an application of patient-based records

Minye Dong et al. BMC Health Serv Res. .

Abstract

Backgound: Efforts to measure performance and identify its driving factors among clinicians are needed for building a high-quality clinician workforce. The availability of data is the most challenging thing. This paper presented a summary performance measure for clinicians and its application on examining factors that influence performance using routine patient-based records.

Methods: Perfomance indicators and difficulty score were extracted from electronic medical records (EMRs). Difficulty adjustment and standardized processing were used to obtain indicators which were comparable between specialties. Principal component analysis (PCA) was used to estimate the summary performance measure. The performance measure was then used to examine the influence of person-job fit and burnout through a mediator effect model and cluster analysis.

Results: A valid sample of 404 clinicians were included in this study, and 244 of them had valid response in the questionnaire. PCA explained 79.37% of the total variance presented by the four adjusted performance indicators. Non-performance attributes and performance driving factors help distinguish different clusters of clinicians. Burnout mediates the relationship between person-job fit and performance in a specific group of clinicians (β = 0.120, p = 0.008).

Conclusions: We demonstrated the analytical steps to estimate clinicians' performance and its practical application using EMRs. Our findings provide insight into personnel classified management. Such practice can be applied in countries where electronic medical record systems are relatively less developed to continuously improve the application of performance management.

Keywords: Benchmark; Clinician; Cluster; Difficulty adjustment; Electric medical record; Human resource management; Performance measurement; Principal component analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Frequency distribution histogram (a) and normal Q-Q plot (b) of SPS in total population (n = 404)
Fig. 2
Fig. 2
Clusters of the study sample
See this image and copyright information in PMC

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