Evidence map›Paper›PMID 37468896›Full record

ArticleBMC health services research2023

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

Minye Dong, Yuyin Xiao, Chenshu Shi, Guohong Li

Abstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Minye Dong *School of Public Health, Shanghai JiaoTong University School of Medicine, No. 227 South Chonqing Rd, Huangpu District, Shanghai, P.R. China.
Yuyin Xiao *School of Public Health, Shanghai JiaoTong University School of Medicine, No. 227 South Chonqing Rd, Huangpu District, Shanghai, P.R. China.
Chenshu ShiCenter for HTA, China Hospital Development Institute, Shanghai Jiao Tong University, No. 227 South Chonqing Rd, Huangpu District, Shanghai, P. R. China.
Guohong LiSchool of Public Health, Shanghai JiaoTong University School of Medicine, No. 227 South Chonqing Rd, Huangpu District, Shanghai, P.R. China. guohongli@sjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgoundEfforts 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.

methodsPerfomance 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.

resultsA 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).

conclusionsWe 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.

Indexed as

Burnout, ProfessionalElectronic Health RecordsHumansSurveys and QuestionnairesBenchmarkClinicianClusterDifficulty adjustmentElectric medical recordHuman resource managementPerformance measurementPrincipal component analysis

Identifiers

PMID37468896
PMCPMC10357785

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.