Ramsay, Craig R.; Grant, Adrian M.; Wallace, Sheila A.; Garthwaite, Paul H.; Monk, Andrew F. and Russell, Ian T.
(2000).
![[img]](http://oro.open.ac.uk/style/images/fileicons/application_pdf.png)
|
PDF (Not Set)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (108kB) |
| DOI (Digital Object Identifier) Link: | https://doi.org/76803 |
|---|---|
| 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 |
|---|---|
| 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: | 24 Jul 2017 15:23 |
| URI: | http://oro.open.ac.uk/id/eprint/17978 |
| Share this page: |     |
Altmetrics |
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.