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Monitoring prescribing patterns using regression and electronic health records
Daniel Backenroth1, Herbert S Chase2, Ying Wei3
1Columbia University Mailman School of Public Health, 722 West 168th Street, #633, New York, NY, 10032, USA. db2175@cumc.columbia.edu.
BMC Medical Informatics and Decision Making
|December 21, 2017
Summary
Statistical methods analyzing electronic health records (EHR) can effectively monitor physician prescribing patterns. This approach helps ensure high-quality, cost-effective patient care by identifying medication associations and preferences.
Area of Science:
- Health Informatics
- Clinical Pharmacy
- Biostatistics
Background:
- Monitoring physician prescribing patterns is crucial for healthcare quality and cost-effectiveness.
- Linking medications and conditions in electronic health records (EHR) for pattern analysis is challenging.
- This study introduces a novel statistical approach to analyze institutional prescribing patterns.
Purpose of the Study:
- To demonstrate the use of statistical methods, specifically regression, for analyzing institutional prescribing patterns.
- To identify medications associated with specific conditions, using hypertension as a case study.
- To compare the effectiveness of regression-based methods against traditional tabulation methods.
Main Methods:
- Collected EHR data from outpatient notes.
- Employed a case/control study design with regression analysis.
- Identified medication-condition associations and drug class preferences.
Main Results:
- Regression methods proved more effective than tabulation in analyzing prescribing patterns.
- Successfully distinguished between medications treating hypertension and those that do not.
- Provided insights into the circumstances influencing the preference for specific hypertension agents.
Conclusions:
- The proposed statistical method enables healthcare institutions to monitor physician prescribing.
- Facilitates ensuring the appropriateness and quality of patient treatment.
- Offers a valuable tool for improving healthcare delivery and cost-effectiveness.
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