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Descriptive analysis of prescription interception patterns: characterizing medication safety risks in an outpatient
Ning Li1, Weiwei Tang1, Nan Li1
1Department of Clinical Pharmacy, The First Hospital of Jilin University, Changchun, Jilin, China.
Frontiers in Pharmacology
|July 29, 2026
Summary
Forced Interception systems block many prescription errors, primarily due to incorrect treatment duration or dosage. Analyzing these intercepted prescriptions can improve medication safety and guide system upgrades.
Area of Science:
- Medical Informatics
- Pharmacovigilance
- Health Systems Research
Background:
- Prescription errors pose a significant risk to patient safety in hospitals.
- Forced Interception (FI) systems are crucial for preventing adverse drug events by blocking problematic prescriptions.
- Understanding the specific reasons for FI in diverse healthcare settings, like Chinese hospitals, is vital for targeted improvements.
Purpose of the Study:
- To analyze the characteristics and reasons for forced interceptions in a hospital setting.
- To identify patterns in intercepted prescriptions to guide system upgrades and clinical training.
- To inform policy interventions aimed at enhancing medication safety.
Main Methods:
- A retrospective analysis of FI prescriptions was conducted.
- Prescriptions were categorized by interception reason, drug class, specific drug, and prescribing department.
- Data were collected from the hospital's electronic prescribing system.
Main Results:
- Over prescriptions were the most common reasons for FI, accounting for the majority of cases.
- Traditional Chinese medicine and central nervous system drugs were the most frequently intercepted categories.
- Specific drugs like Duloxetine Hydrochloride Enteric Capsules and Atorvastatin Calcium Tablets were frequently intercepted, with distinct departmental prescribing patterns observed.
Conclusions:
- Targeted interventions, including electronic prescribing system upgrades and department-specific training, are recommended.
- Special review panels and future multicenter studies are suggested to further reduce prescription errors.
- Exploring artificial intelligence for dynamic risk prediction could optimize continuous medical quality improvement.
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