Cargando…

A novel method to create realistic synthetic medication data

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 medic...

Descripción completa

Detalles Bibliográficos
Autores principales: Hodges, Robert, Tokunaga, Kristen, LeGrand, Joseph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343944/
https://www.ncbi.nlm.nih.gov/pubmed/37457749
http://dx.doi.org/10.1093/jamiaopen/ooad052
_version_ 1785072856048074752
author Hodges, Robert
Tokunaga, Kristen
LeGrand, Joseph
author_facet Hodges, Robert
Tokunaga, Kristen
LeGrand, Joseph
author_sort Hodges, Robert
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10343944
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-103439442023-07-14 A novel method to create realistic synthetic medication data Hodges, Robert Tokunaga, Kristen LeGrand, Joseph JAMIA Open Research and Applications 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. Oxford University Press 2023-07-13 /pmc/articles/PMC10343944/ /pubmed/37457749 http://dx.doi.org/10.1093/jamiaopen/ooad052 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research and Applications
Hodges, Robert
Tokunaga, Kristen
LeGrand, Joseph
A novel method to create realistic synthetic medication data
title A novel method to create realistic synthetic medication data
title_full A novel method to create realistic synthetic medication data
title_fullStr A novel method to create realistic synthetic medication data
title_full_unstemmed A novel method to create realistic synthetic medication data
title_short A novel method to create realistic synthetic medication data
title_sort novel method to create realistic synthetic medication data
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343944/
https://www.ncbi.nlm.nih.gov/pubmed/37457749
http://dx.doi.org/10.1093/jamiaopen/ooad052
work_keys_str_mv AT hodgesrobert anovelmethodtocreaterealisticsyntheticmedicationdata
AT tokunagakristen anovelmethodtocreaterealisticsyntheticmedicationdata
AT legrandjoseph anovelmethodtocreaterealisticsyntheticmedicationdata
AT hodgesrobert novelmethodtocreaterealisticsyntheticmedicationdata
AT tokunagakristen novelmethodtocreaterealisticsyntheticmedicationdata
AT legrandjoseph novelmethodtocreaterealisticsyntheticmedicationdata