Assessment of the learning curve in health technologies: a systematic reviewReport as inadecuate

Author: Craig R. Ramsay, Adrian M. Grant, Sheila A. Wallace, Paul H. Garthwaite, Andrew F. Monk and Ian T. Russell



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.\ud 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:"\ud \ud 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%).\ud \ud 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 ...

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