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Predicting the Physician's Specialty Using a Medical Prescription Database.

Mahboube Akhlaghi1, Hamed Tabesh2, Behzad Mahaki3

  • 1Department of Biostatistics and Epidemiology, School of Health, and Student Research Committee, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.

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Predicting physician specialty using common medication pairs, like amoxicillin-metronidazole, aids in data imputation for better healthcare planning and reduced costs. This method enhances prescription database accuracy.

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Area of Science:

  • Medical Informatics
  • Data Science in Healthcare
  • Pharmacovigilance

Background:

  • Large volumes of medical prescription data are generated daily.
  • Significant portions of this data often lack physician specialty information.
  • Accurate imputation of missing specialty data can optimize medication planning and reduce healthcare costs.

Purpose of the Study:

  • To predict physician specialty based on pairs of frequently co-prescribed medications.
  • To explore the utility of medication co-prescription patterns for data imputation in medical databases.

Main Methods:

  • Utilized KAy-means for MIxed LArge datasets (KAMILA) clustering.
  • Employed a random forest (RF) model for prediction.
  • Data sourced from outpatient prescriptions in Khorasan Razavi, Iran (April 2015 - March 2017).

Main Results:

  • Identified the importance of specific medication combinations in predicting physician specialty.
  • The amoxicillin-metronidazole combination demonstrated the highest predictive importance.
  • Findings are accessible via an R-shiny web application for broader use.

Conclusions:

  • Physician specialty can be effectively predicted using common medication prescription pairs.
  • The developed methodology offers a valuable tool for imputing missing specialty data in prescription databases.
  • This approach can contribute to improved medication management, enhanced healthcare quality, and cost reduction.