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A Comprehensive Approach to Days' Supply Estimation in a Real-World Prescription Database: Algorithm Development and

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This summary is machine-generated.

Accurate medication adherence requires precise days' supply (DS) calculations. This study improved DS accuracy using imputation methods, enhancing medication adherence metrics for real-world data.

Keywords:
CMAcontinuous multiple interval measures of medication availabilitydaily dosedays’ supplyimputationmedication adherenceprescription records

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Area of Science:

  • Health Informatics
  • Pharmaceutical Sciences
  • Data Science

Background:

  • Accurate medication usage statistics and adherence calculations depend on precise days' supply (DS).
  • Missing or incorrect DS information necessitates alternative data management strategies.
  • Developing robust methods for handling incomplete prescription data is crucial for reliable analytics.

Purpose of the Study:

  • To implement diverse data management techniques for incomplete and missing prescription data.
  • To enhance the accuracy of calculating days' supply (DS) across all medication types.
  • To assess the impact of these data correction methods on real-world medication adherence calculations.

Main Methods:

  • Utilized a large-scale Estonian prescription dataset (2012-2019, 150,824 patients).
  • Employed a three-step workflow: data cleaning, imputation, and DS calculation.
  • Combined imputation methods including mode-based daily dose and external guidelines (Summary of Product Characteristics, legislation).

Main Results:

  • Successfully determined DS for 98.3% of dispensed prescriptions through imputation.
  • Identified specific drug forms with low initial DS accuracy, such as insulin injections (3.1%) and intravaginal contraceptives (8%).
  • Observed that data correction methods reduced discrepancies in medication adherence between different time periods.

Conclusions:

  • A well-designed imputation pipeline, integrating data-driven approaches with domain knowledge, significantly improves prescription data quality.
  • The applied methods generate more accurate and consistent medication adherence metrics.
  • Future work should focus on refining imputation techniques, exploring machine learning, and external validation.