A novel method to create realistic synthetic medication data

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.

Word sense disambiguation of acronyms in clinical narratives

“…Early efforts have demonstrated that even though the generated text was of poorer quality relative to the original text, when it was used to augment the training data, it still boosted the performance of downstream NLP tasks ( 46 ). Domain-specific databases can be used to mitigate the inherently stochastic nature of large language models ( 47 ) in an attempt to improve the accuracy, diversity and complexity of generated clinical data ( 48 ). Nonetheless, the data annotation bottleneck still persists but may be addressed with strategic prompt engineering.…”