Construction of a Predictive Model for Negative Defensive Medical Behaviors of Doctors in Tertiary Grade A Hospitals in Shandong Province
- VernacularTitle:山东省三级甲等医院医生消极防御性医疗行为预测模型构建研究
- Author:
Xia LIU
1
;
Lusi YANG
1
;
Yuqiang GAO
1
Author Information
1. 青岛市市立医院 山东 青岛 156001
- Publication Type:Journal Article
- Keywords:
negative defensive medicine;
risk factors;
predictive model;
tertiary hospitals;
doctors
- From:
Chinese Hospital Management
2024;44(12):86-89
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the current situation and influencing factors of negative defensive medical practices among doctors in tertiary hospitals,construct a predictive model for negative defensive medicine.Methods Using cluster sampling,946 clinical doctors from 4 tertiary hospitals in Shandong Province were selected.A self-administered questionnaire was used to survey doctors'demographic characteristics,occupational characteristics,doctor-patient relationship status,and negative defensive medical behaviors.Logistic regression was used to screen the influencing factors of negative defensive medical behavior,while constructing a predictive model and analyzing the performance of the model.Results 72.1％ of doctors engaged in negative defensive medical practices,with the most common being reducing the number of patient contacts (671 people,70.9％).Logistic regression results showed that male gender,high seniority,high professional title,surgical specialty,negative doctor-patient relationship,experience of medical disputes,and high-risk perception were independent influencing factors for negative defensive medical behaviors (P<0.05).The logistic regression model incorporating the above 6 variables had an AUC of 0.846,good calibration,and a prediction accuracy of 88.6％ on the validation sample.Conclusion Negative defensive behavior is prevalent among doctors in tertiary hospitals.Interventions should focus on key populations and adopt targeted measures.The predictive model constructed in this study can be embedded into hospital management systems to achieve intelligent early warning.
