Ramsay, Craig R.; Grant, Adrian M.; Wallace, Sheila A.; Garthwaite, Paul H.; Monk, Andrew F. and Russell, Ian T.
(2000).
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| DOI (Digital Object Identifier) Link: | https://doi.org/76803 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
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 Item |
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| Copyright Holders: | 2000 Cambridge University Press |
| ISSN: | 0266-4623 |
| Academic Unit/School: | 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: | 08 Dec 2018 18:41 |
| URI: | http://oro.open.ac.uk/id/eprint/17978 |
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