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Prescribing in family practice: a method of data collection
Prescription data from family practices offer precise comparisons for medical education. Analyzing drug prescribing patterns requires controlling for patient, physician, and external variables for accuracy.
Area of Science:
- Family Medicine
- Pharmacoeconomics
- Medical Education
Background:
- Prescription data in family practice are more precise than morbidity data for interpractice comparison.
- Interpractice comparisons of prescribing offer a valuable method for continuing medical education.
- Drug prescribing patterns are influenced by complex patient, physician, and external factors.
Purpose of the Study:
- To describe methods for data collection and calculation of prescribing rates.
- To discuss challenges and problems associated with interpractice comparisons of prescribing data.
- To highlight the utility of prescription data for practice-based medical education.
Main Methods:
- Data collection methods were detailed for a 32-physician teaching practice.
- Calculation of prescribing rates was performed using collected prescription data.
- Variables influencing prescribing patterns were identified for control.
Main Results:
- Prescription data provide a precise basis for interpractice comparisons.
- Controlling for influencing variables is crucial for accurate prescribing pattern reviews.
- The described methods facilitate analysis within a teaching practice setting.
Conclusions:
- Prescription data are a valuable tool for interpractice comparisons in family medicine.
- Accurate analysis of prescribing patterns necessitates careful control of confounding variables.
- Further discussion on the problems of interpractice comparisons is warranted.
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