Journal article
Optimising medication data collection in a large-scale clinical trial
PloS one, Vol.14(12), pp.e0226868-e0226868
12/27/2019
DOI: 10.1371/journal.pone.0226868
PMCID: PMC6934269
PMID: 31881040
Abstract
Objective
Pharmaceuticals play an important role in clinical care. However, in community-based research, medication data are commonly collected as unstructured free-text, which is prohibitively expensive to code for large-scale studies. The ASPirin in Reducing Events in the Elderly (ASPREE) study developed a two-pronged framework to collect structured medication data for 19,114 individuals. ASPREE provides an opportunity to determine whether medication data can be cost-effectively collected and coded, en masse from the community using this framework.
Methods
The ASPREE framework of type-to-search box with automated coding and linked free text entry was compared to traditional method of free-text only collection and post hoc coding. Reported medications were classified according to their method of collection and analysed by Anatomical Therapeutic Chemical (ATC) group. Relative cost of collecting medications was determined by calculating the time required for database set up and medication coding.
Results
Overall, 122,910 participant structured medication reports were entered using the type-to-search box and 5,983 were entered as free-text. Free-text data contributed 211 unique medications not present in the type-to-search box. Spelling errors and unnecessary provision of additional information were among the top reasons why medications were reported as free-text. The cost per medication using the ASPREE method was approximately USD $0.03 compared with USD $0.20 per medication for the traditional method.
Conclusion
Implementation of this two-pronged framework is a cost-effective alternative to free-text only data collection in community-based research. Higher initial set-up costs of this combined method are justified by long term cost effectiveness and the scientific potential for analysis and discovery gained through collection of detailed, structured medication data.
Details
- Title: Subtitle
- Optimising medication data collection in a large-scale clinical trial
- Creators
- Jessica E. Lockery - Monash UniversityJason Rigby - Monash UniversityTaya A. Collyer - Monash UniversityAshley C. Stewart - Monash UniversityRobyn L. Woods - Monash UniversityJohn J. McNeil - Monash UniversityChristopher M. Reid - Monash UniversityMichael E. Ernst - Roy J. and Lucille A. Carver College of MedicineASPREE Investigator Group
- Resource Type
- Journal article
- Publication Details
- PloS one, Vol.14(12), pp.e0226868-e0226868
- DOI
- 10.1371/journal.pone.0226868
- PMID
- 31881040
- PMCID
- PMC6934269
- NLM abbreviation
- PLoS One
- ISSN
- 1932-6203
- eISSN
- 1932-6203
- Publisher
- Public Library Science
- Number of pages
- 12
- Grant note
- U01AG029824 / National Cancer Institute at the National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI) Victorian Cancer Agency U19AG062682; U01AG029824 / NATIONAL INSTITUTE ON AGING; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Aging (NIA) National Institute on Aging; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Aging (NIA) 334047; 1127060 / National Health and Medical Research Council of Australia; National Health and Medical Research Council (NHMRC) of Australia Monash University
- Language
- English
- Date published
- 12/27/2019
- Academic Unit
- Family and Community Medicine; Pharmacy Practice and Science
- Record Identifier
- 9984297334702771
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