Optimising medication data collection in a large-scale clinical trial


Autoři: Jessica E. Lockery aff001;  Jason Rigby aff001;  Taya A. Collyer aff001;  Ashley C. Stewart aff001;  Robyn L. Woods aff001;  John J. McNeil aff001;  Christopher M. Reid aff001;  Michael E. Ernst aff003
Působiště autorů: Department of Epidemiology & Preventive Medicine, Monash University, Melbourne, Victoria, Australia aff001;  School of Public Health, Curtin University, Perth, Western Australia, Australia aff002;  Department of Pharmacy Practice and Science, College of Pharmacy and Department of Family Medicine, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226868

Souhrn

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.

Klíčová slova:

Cardiology – Clinical trials – Dermatology – Elderly – Endocrinology – Geriatrics – Medicine and health sciences – Oncology


Zdroje

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Článek vyšel v časopise

PLOS One


2019 Číslo 12