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Assessment of the learning curve in health technologies: a systematic review

Ramsay, Craig R.; Grant, Adrian M.; Wallace, Sheila A.; Garthwaite, Paul H.; Monk, Andrew F. and Russell, Ian T. (2000). Assessment of the learning curve in health technologies: a systematic review. International Journal of Technology Assessment in Health Care, 16(4) pp. 1095–1108.

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Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past.
Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:"

Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%).

Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning.

Item Type: Journal Article
Copyright Holders: 2000 Cambridge University Press
ISSN: 0266-4623
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 17978
Depositing User: Sara Griffin
Date Deposited: 25 Aug 2009 09:33
Last Modified: 04 Oct 2016 11:23
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