廉晓丹
中国医学科学院阜外医院 信息中心
Background: Internet medical services (IMS) have been rapidly promoted across China, especially since the outbreak of COVID-19. However, a nationwide study is still lacking. Objective: To unveil the whole picture of IMS across tertiary and secondary hospitals in China, and to evaluate potential influence of the hospital general characteristics, medical staff reserve, and patient visiting capacity on IMS provision. Methods: An online cross-sectional survey was conducted, and 1,995 tertiary and 2,824 secondary hospitals completed questionnaires from 31 administrative regions in China during July 1 and October 31, 2021. Those hospitals are defined having abilities of providing IMS if at least one following service are available: (1) online appointment of diagnoses and treatments; (2) online disease consultation; (3) electronic prescription; and (4) drug delivery. The logistic regression models are used to detect the possible roles on developing IMS. Results: A majority (68.9%) of tertiary hospitals and 53.0% secondary hospitals have provided IMS (p < 0.01). Tertiary hospital also had much higher proportions than secondary hospitals in online appointment of diagnoses and treatments (62.6% vs. 46.1%), online disease consultation (47.3% vs. 16.9%), electronic prescription (33.2% vs. 9.6%), and drug delivery (27.8% vs. 4.6%). In multivariate model, IMS hospitals may be associated significantly with having more licensed doctors (≥161 vs. <161: odds ratio [OR], 1.30; 1.13-1.50; p < 0.01), having more frequency of registration appointments (≥3,356 vs. <3,356: OR, 1.77; 1.54-2.03; p < 0.01), having higher frequency of patient follow-ups (≥1,160 vs. <1,160: OR, 1.36; 1.15-1.61; p < 0.01), having laboratory test appointments (Yes vs. No: OR, 1.25; 1.06-1.48; p = 0.01), and having treatment appointments (Yes vs. No: OR, 1.27; 1.11-1.46; p < 0.01) in the past 3 months. Conclusions: The coverage of IMS is appreciable in China, but the IMS market is still greatly extended and improved. The provision of IMS depends primarily on the scales of the hospitals, including medical staff reserve and patient visiting capacity.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association 2024
BACKGROUND:Computer-assisted clinical coding (CAC) based on automated coding algorithms has been expected to improve the International Classification of Disease, tenth version (ICD-10) coding quality and productivity, whereas studies oriented to primary diagnosis auto-coding are limited in the Chinese context.OBJECTIVE:This study aims at developing a machine learning (ML) model for automated primary diagnosis ICD-10 coding.METHODS:A total of 71,709 admissions in Fuwai hospital were included to carry out this study, corresponding to 168 primary diagnosis ICD-10 codes. Based on clinical implications, two feature engineering methods were used to process discharge diagnosis and procedure texts into sequential features and sequential grouping features respectively by which two kinds of models were built and compared. One baseline model using one-hot encoding features was considered. Light Gradient Boosting Machine (LightGBM) was adopted as the classifier, and grid search and cross-validation were used to select the optimal hyperparameters. SHapley Additive exPlanations (SHAP) values were applied to give the interpretability of models.RESULTS:Our best prediction model was developed based on sequential grouping features. It showed good performance in the test phase with accuracy and macro-averaged F1 (Macro-F1) of 95.2% and 88.3% respectively. The comparison of the models demonstrated the effectiveness of the sequential information and the grouping strategy in boosting model performance (P-value < 0.01). Subgroup analysis of the best model on each individual code manifested that 91.1% of the codes achieved the F1 over 70.0%.CONCLUSIONS:Our model has been demonstrated its effectiveness for automated primary diagnosis coding in the Chinese context and its results are interpretable. Hence, it has the potential to assist clinical coders to improve coding efficiency and quality in Chinese inpatient settings.
International journal of medical informatics 2021
