Modelling and extraction of variability in free-text medication prescriptions from an anonymised primary care electronic medical record research database

George Karystianis, Therese Sheppard, William G. Dixon, Goran Nenadic*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
20 Downloads (Pure)

Abstract

Background: Free-text medication prescriptions contain detailed instruction information that is key when preparing drug data for analysis. The objective of this study was to develop a novel model and automated text-mining method to extract detailed structured medication information from free-text prescriptions and explore their variability (e.g. optional dosages) in primary care research databases. Methods: We introduce a prescription model that provides minimum and maximum values for dose number, frequency and interval, allowing modelling variability and flexibility within a drug prescription. We developed a text mining system that relies on rules to extract such structured information from prescription free-text dosage instructions. The system was applied to medication prescriptions from an anonymised primary care electronic record database (Clinical Practice Research Datalink, CPRD). Results: We have evaluated our approach on a test set of 220 CPRD prescription free-text directions. The system achieved an overall accuracy of 91 % at the prescription level, with 97 % accuracy across the attribute levels. We then further analysed over 56,000 most common free text prescriptions from CPRD records and found that 1 in 4 has inherent variability, i.e. a choice in taking medication specified by different minimum and maximum doses, duration or frequency. Conclusions: Our approach provides an accurate, automated way of coding prescription free text information, including information about flexibility and variability within a prescription. The method allows the researcher to decide how best to prepare the prescription data for drug efficacy and safety analyses in any given setting, and test various scenarios and their impact.

Original languageEnglish
Article number18
Pages (from-to)1-10
Number of pages10
JournalBMC Medical Informatics and Decision Making
Volume16
DOIs
Publication statusPublished - 9 Feb 2016
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Fingerprint

Dive into the research topics of 'Modelling and extraction of variability in free-text medication prescriptions from an anonymised primary care electronic medical record research database'. Together they form a unique fingerprint.

Cite this