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Prediction of Medical Disputes Between Health Care Workers and Patients in Terms of Hospital Legal Construction Using Machine Learning Techniques: Externally Validated Cross-Sectional Study.
Yi, Min; Cao, Yuebin; Wang, Lin; Gu, Yaowen; Zheng, Xueqian; Wang, Jiangjun; Chen, Wei; Wei, Liangyu; Zhou, Yujin; Shi, Chenyi; Cao, Yanlin.
Affiliation
  • Yi M; Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Cao Y; Health Commission of Hunan Province, Changsha, China.
  • Wang L; Beijing Municipal Health Commission, Beijing, China.
  • Gu Y; Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Zheng X; Chinese Hospital Association Medical Legality Specialized Committee, Beijing, China.
  • Wang J; China-Japan Friendship Hospital, Beijing, China.
  • Chen W; Beijing Stomatological Hospital, Capital Medical University, Beijing, China.
  • Wei L; Beijing Hospital, Beijing, China.
  • Zhou Y; Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Shi C; Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Cao Y; Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
J Med Internet Res ; 25: e46854, 2023 08 17.
Article in En | MEDLINE | ID: mdl-37590041
ABSTRACT

BACKGROUND:

Medical disputes are a global public health issue that is receiving increasing attention. However, studies investigating the relationship between hospital legal construction and medical disputes are scarce. The development of a multicenter model incorporating machine learning (ML) techniques for the individualized prediction of medical disputes would be beneficial for medical workers.

OBJECTIVE:

This study aimed to identify predictors related to medical disputes from the perspective of hospital legal construction and the use of ML techniques to build models for predicting the risk of medical disputes.

METHODS:

This study enrolled 38,053 medical workers from 130 tertiary hospitals in Hunan province, China. The participants were randomly divided into a training cohort (34,286/38,053, 90.1%) and an internal validation cohort (3767/38,053, 9.9%). Medical workers from 87 tertiary hospitals in Beijing were included in an external validation cohort (26,285/26,285, 100%). This study used logistic regression and 5 ML techniques decision tree, random forest, support vector machine, gradient boosting decision tree (GBDT), and deep neural network. In total, 12 metrics, including discrimination and calibration, were used for performance evaluation. A scoring system was developed to select the optimal model. Shapley additive explanations was used to generate the importance coefficients for characteristics. To promote the clinical practice of our proposed optimal model, reclassification of patients was performed, and a web-based app for medical dispute prediction was created, which can be easily accessed by the public.

RESULTS:

Medical disputes occurred among 46.06% (17,527/38,053) of the medical workers in Hunan province, China. Among the 26 clinical characteristics, multivariate analysis demonstrated that 18 characteristics were significantly associated with medical disputes, and these characteristics were used for ML model development. Among the ML techniques, GBDT was identified as the optimal model, demonstrating the lowest Brier score (0.205), highest area under the receiver operating characteristic curve (0.738, 95% CI 0.722-0.754), and the largest discrimination slope (0.172) and Youden index (1.355). In addition, it achieved the highest metrics score (63 points), followed by deep neural network (46 points) and random forest (45 points), in the internal validation set. In the external validation set, GBDT still performed comparably, achieving the second highest metrics score (52 points). The high-risk group had more than twice the odds of experiencing medical disputes compared with the low-risk group.

CONCLUSIONS:

We established a prediction model to stratify medical workers into different risk groups for encountering medical disputes. Among the 5 ML models, GBDT demonstrated the optimal comprehensive performance and was used to construct the web-based app. Our proposed model can serve as a useful tool for identifying medical workers at high risk of medical disputes. We believe that preventive strategies should be implemented for the high-risk group.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Health Personnel / Dissent and Disputes Type of study: Clinical_trials / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Med Internet Res Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Health Personnel / Dissent and Disputes Type of study: Clinical_trials / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Med Internet Res Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: China