Research Assistant Professor National Institute of Health Data Science, Peking University Beijing, China (People's Republic)
Background: Innovation in the delivery of virtual and telehealth services increased significantly during the COVID pandemic. In China, one area of innovation was the development of “Digital Hospitals” where patients received online health care rather than traditional face-to-face services. While the digital hospitals were convenient and accessible for patients during the pandemic, little is known about the patterns of medicines prescribed by these hospitals.
Objectives: The aim of this study was to describe prescribing patterns and identify factors associated with medicines used for the management of diabetes from digital hospitals in a northwest city in China.
Methods: Visits for both diabetes-related diagnoses and prescriptions between January 1st, 2018, and March 31st, 2021 from all digital hospitals in the Yinchuan City of the Ningxia Hui Autonomous Region in China were analyzed in this study. Diabetes diagnoses were identified as type 1 diabetes (T1DM, ICD-10: E10), type 2diabetes (T2DM, E11), and others (E12-E14). A maximum of four medicines could be prescribed per visit. And diabetes-related medicines are defined as insulin and analogs (ATC: A10A), blood glucose lowering drug excluded insulins (A10B), and Chinese patent medicine (CPM). Proportions of prescriptions was calculated as the number of each anti-diabetic agent divided by total diabetes medicines. Patient characteristics associated with prescription patterns were identified via logistic regression.
Results: A total of 229811 visits were included. Among these visits, the mean age of patients was 53.6 years old,61.6% were made by males, and 84.0% by type 2 diabetes patients. A total of 393951 prescriptions were issued and 70.3% of these (n=276794) were for diabetes medications. On average, patients were prescribed 1.3±0.77 diabetes medicines per visit with patients prescribed ≥2 medications for 15.7% of visits. The top five most prescribed diabetes medicines were biguanides (A10B, 38.7%, 95% CI: 38.5-38.9),α-glycosidase inhibitor (A10BF, 17.3%, 95% CI: 17.2-17.5), sulfonylureas (A10BB, 13.0%, 95% CI: 12.9-13.1), sulfonamides (A10BC, 9.8%, 95% CI: 9.7-9.9), and CPM (9.6%, 95% CI: 9.5-9.7%). Similar patterns were also observed in visits with T2DM visits. In T1DM visits, there was an obvious increase in insulin use, and a decrease in biguanides use. Compared to metformin, thiazolidinediones (A10BG) were more likely to prescribe in the females (OR: 1.24, 95% CI: 1.15-1.34), as well as SGLT-2 inhibitors (A10BK) were less likely to prescribe (OR: 0.83, 95% CI: 0.73-0.94).
Conclusions: Diabetes medicines were commonly prescribed during online encounters with digital hospitals. As digital health continues to grow, understanding the impact of prescribing is an important area for future research.
