Objective: We reviewed and appraised the methodsby which the issue of the learning curve has been addressedduring health technology assessment in the past.
Method: We performed a systematic review of papers inclinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE,HealthSTAR, MEDLINE, Science Citation Index, and Social ScienceCitation Index) using the search term “learning curve.”
Results: The clinical search retrieved 4,571 abstracts forassessment, of which 559 (12%) published articles were eligiblefor review. Of these, 272 were judged to have formally assesseda learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). Themajority of the studies were case series (95%). Some 47% ofstudies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the methodwas unclear for 31%. The statistical methods used were simplegraphs (44%), splitting the data chronologically and performinga t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%).
Conclusions: Learning curves are rarely considered formallyin health technology assessment. Where they are, the reportingof the studies and the statistical methods used are weak. As aminimum, reporting of learning should include the number and experience of the operators and a detailed description of datacollection. Improved statistical methods would enhance theassessment of health technologies that require learning.