Long-Term Outcomes of a Health Information System-Based Feedback Intervention Study of Antimicrobial Prescriptions in Primary Care Institutions: Follow-Up of a Randomized Cross-Over Controlled Trial

Yuxing Yan; Junli Yang; Yun Lu; Zhezhe Cui; Yue Chang

doi:10.2147/IDR.S492367

Long-Term Outcomes of a Health Information System-Based Feedback Intervention Study of Antimicrobial Prescriptions in Primary Care Institutions: Follow-Up of a Randomized Cross-Over Controlled Trial

Infect Drug Resist. 2025 Jan 6:18:61-76. doi: 10.2147/IDR.S492367. eCollection 2025.

Authors

Yuxing Yan^#¹, Junli Yang^#¹, Yun Lu², Zhezhe Cui³, Yue Chang^{1

4}

Affiliations

¹ School of Medicine and Health Management, Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.
² School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.
³ Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi Province, People's Republic of China.
⁴ Center of Medicine Economics and Management Research, Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: To evaluate the long-term impacts of the feedback intervention on controlling inappropriate use of antimicrobial prescriptions in primary care institutions in China, as a continuation of the previous feedback intervention trial.

Methods: After the intervention ended, we conducted a 12-month follow-up study. The prescription data were collected from the baseline until the end of the follow-up period. The generalized estimation equation was employed to analyze the differences among four representative time points: at the baseline point, at 3 months, at 6 months, and at 18 months. The time-intervention interaction was utilized to evaluate the changing trends of group A and group B. Our primary outcome variable is the monthly inappropriate antimicrobial prescription rate (IAPR).

Results: After adjusting for covariates, the IAPRs in group A decreased by 1.00% on average from the baseline point to the 3 months, 5.00% from the 3 months to the 6 months, -0.92% from the 6 months to the 18 months, and 0.39% from the baseline point to the 18 months. During the corresponding four periods in group B, the average decline was 2.33%, 3.67%, -0.42%, and 0.72%, respectively. As for antimicrobial prescription rates (APRs), the average decline for group A was 1.33%, 3.67%, and 0.17% during the three periods: from the baseline point to the 3 months, from the 3 months to the 6 months, and from the 6 months to the 18 months, respectively. Accordingly in group B, the average decline was 1.00%, 3.67%, and 0.08%, respectively.

Conclusion: Our feedback intervention generated limited long-term impacts. Although the IAPRs and the APRs consistently remained below the baseline point, both rates experienced a rebound within a certain range following the stop of the intervention in the two groups. It is reasonable to think that the desired effects will be difficult to maintain without sustained implementation of feedback intervention.

Keywords: antimicrobial prescriptions; feedback intervention; health information system; long-term outcomes; primary care institutions.

Grants and funding

This research was supported by the National Natural Science Foundation of China Grant on “Research on Feedback Intervention Mode of Antibiotic Prescription Control in Primary Care Institutions Based on the Depth Graph Neural Network Technology” (71964009) and the Science and Technology Fund Project of Guizhou Provincial Health Commission Grant on “Application Research of Deep Learning Technology in Rational Evaluation and Intervention of Antibiotic Prescription” (gzwjkj2019-1-218). Corresponding author YC is the project leader. The funders played a role in the study which we should acknowledge. Specifically, the funders provided travel expenses incurred during the data collection process, as well as the experts’ fees for providing guidance on the study design, technological guidance, data analysis and interpretation.