Establishing a model to measure and predict the quality of gastrointestinal endoscopy.

BACKGROUND Tens of millions of gastrointestinal endoscopic procedures are performed every year in China, but the quality varies significantly and related factors are complex. Individual endoscopist- and endoscopy division-related factors may be useful to establish a model to measure and predict the quality of endoscopy. AIM To establish a model to measure and predict the quality of gastrointestinal endoscopic procedures in mainland China. METHODS Selected data on endoscopy experience, equipment, facility, qualification of endoscopists, and other relevant variables were collected from the National Database of Digestive Endoscopy of China. The multivariable logistic regression analysis was used to identify the potential predictive variables for occurrence of medical malpractice and patient disturbance. Linear and nonlinear regressions were used to establish models to predict incidence of endoscopic complications. RESULTS In 2012, gastroscopy/colonoscopy-related complications in mainland China included bleeding in 4,359 cases (0.02%) and perforation in 914 (0.003%). Endoscopic-retrograde-cholangiopancreatography-related complications included severe acute pancreatitis in 593 cases (0.3%), bleeding in 2,151 (1.10%), perforation in 257 (0.13%) and biliary infection in 4,125 (2.11%). Moreover, 1,313 (5.0%) endoscopists encountered with medical malpractice, and 5,243 (20.0%) encountered with the disturbance from patients. The length of endoscopy experience, weekly working hours, weekly night shifts, annual vacation days and job satisfaction were predictors for the occurrence of medical malpractice and patient disturbance. However, the length of endoscopy experience and the ratio of endoscopists to nurses were not adequate to establish an effective predictive model for endoscopy complications. CONCLUSION The workload and job satisfaction of endoscopists are valuable predictors for medical malpractice or patient disturbance. More comprehensive data are needed to establish quality-predictive models for endoscopic complications.