A novel method to create realistic synthetic medication data

Author:

Hodges Robert1ORCID,Tokunaga Kristen2,LeGrand Joseph3

Affiliation:

1. Utica University , Charlotte, North Carolina, USA

2. Data Product Management, Komodo Health , San Francisco, California, USA

3. HealthIT, Vanderbilt University Medical Center , Nashville, Tennessee, USA

Abstract

Abstract Objective Synthea is a synthetic patient generator that creates synthetic medical records, including medication profiles. Prior to our work, Synthea produced unrealistic medication data that did not accurately reflect prescribing patterns. This project aimed to create an open-source synthetic medication database that could integrate with Synthea to create realistic patient medication profiles. Materials and Methods The Medication Diversification Tool (MDT) created from this study combines publicly available prescription data from the Medical Expenditure Panel Survey (MEPS) and standard medication terminology/classifications from RxNorm/RxClass to produce machine-readable information about medication use in the United States. Results The MDT was validated using a chi-square goodness-of-fit test by comparing medication distributions from Synthea, Synthea+MDT, and the MEPS. Using a pediatric asthma population, results show that Synthea+MDT had no statistical difference compared to the real-world MEPS with a P value = .84. Discussion The MDT is designed to generate realistic medication distributions for drugs and populations. This tool can be used to enhance medication records generated by Synthea by calculating medication-use data at a national level or specific to patient subpopulations. MDT’s contributions to synthetic data may enable the acceleration of application development, access to more realistic healthcare datasets for education, and patient-centered outcomes’ research. Conclusions The MDT, when used with Synthea, provides a free and open-source method for making synthetic patient medication profiles that mimic the real world.

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Reference26 articles.

1. Generation and evaluation of synthetic patient data;Goncalves;BMC Med Res Methodol,2020

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