Research on Performance Evaluation of Clinical Physicians Based on Medical Big Data
- VernacularTitle:基于医疗大数据的临床医师工作绩效评价研究
- Author:
Mengjie LU
1
,
2
;
Guo-Hong LI
;
Zhensu SHI
;
Xiyang LI
;
Yuyin XIAO
;
Xianqun FAN
Author Information
1. 上海交通大学公共卫生学院 上海 200025
2. 上海交通大学中国医院发展研究院卫生技术评估研究所 上海 200025
- Keywords:
medical big data;
NPCA;
NSPCA;
clinical physician;
clinical performance
- From:
Chinese Hospital Management
2023;43(12):6-10
- CountryChina
- Language:Chinese
-
Abstract:
Objective It combines medical big data and machine learning techniques to explore clinical outcomes based clinical physician performance evaluation method.Methods The non-negative principal component analysis(NPCA)was used in cases.Based on the non-negative sparse principal component analysis(NSPCA),a comprehen-sive index fitting was performed on 11 clinical performance indicators of 170 clinicians treating cardiovascular diseases.At the same time,confidence intervals were constructed based on root cause assessment techniques to calculate the range of indicators for each clinician.Results The coincidence rate of outpatient discharge diagnosis,the rate of grade A healing of surgical incision,the proportion of surgical patients,the rate of 3-day diagnosis,the proportion of third-grade and fourth-grade surgery,the completion of surgery and the number of operations were significant in dis-tinguishing the work performance of clinicians.However,the average length of hospital stays before surgery,the rate of unplanned readmission within 30 days,the average length of hospital stays of discharged patients,the main diag-nosis and cure/improvement,and the number of patients admitted were not significant in distinguishing the clinical work performance of clinicians.The overall work performance of all clinicians can be ranked through comprehensive index fitting,and the further evaluation of high,middle and low performance of each specific index can reveal the potential reconstruction dimensions of each clinician.Conclusion It utilizes machine learning techniques to achieve a comprehensive evaluation of clinical performance,utilizing medical big data as the foundation.It holds the potential to provide important support for a more scientific and objective assessment of clinical performance.
