[Development and Application for Drug-safety and Efficacy Using a Large Claims Data]

Area of Science:

• Health Informatics
• Pharmacoeconomics
• Data Science in Medicine

Background:

• Medical big data analytics are increasingly vital for evaluating drug safety and efficacy.
• Traditionally used by academia, medical big data is now shifting focus towards hospital pharmacists.
• Large claims data analysis offers unique advantages for medical research.

Purpose of the Study:

• To review recent research utilizing medical big data, specifically large claims data.
• To demonstrate the application of medical big data in addressing practical clinical and operational questions.
• To highlight the value of big data analytics for hospital pharmacists and medical staff.

Main Methods:

• Analysis of large claims databases.
• Utilizing information technology for data assessment.
• Review of three distinct research applications: risk factor analysis, drug-drug interaction prevalence, and medication error assessment.

Main Results:

• Identified risk factors for low-density lipoprotein (LDL) level achievement in working-age populations.
• Determined the prevalence of drug-drug interactions in patients with atrial fibrillation.
• Assessed the impact of "look-alike" packaging designs on medication errors.

Conclusions:

• Medical big data, particularly large claims data, is effective for analyzing rare events and benchmarking hospital performance.
• These analyses provide valuable evidence for clinical decision-making and operational improvements.
• The application of medical big data meets the needs of medical staff for evidence-based practice.