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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.

Brown ( I 976) has provided an analysis of the effect of the memorability of an item on the confidence with which it is accepted or rejected in a test of recognition or recall. When the subject has no clear recollection of the inclusion of an item in an input list, he is assumed to evaluate its memorability in the context of the experiment before he decides whether to accept or reject it. If the judged memorability is high, the absence of a clear recollection is stronger evidence against the item than if it is low. A specific prediction is that memorable distractors in a recognition test will be more confidently rejected than nonmemorable ones. This prediction was tested and confirmed in three experiments in which recognition was tested by +category rating.Except in Experiment I, items memorable to individual subjects were identified by administering a questionnaire. For example, in Experiment I11 forenames of immediate family were assumed to have high memorability. This experiment also included word frequency as a variable. Low-frequency distractors were rejected significantly more firmly than high-frequency distractors : extraction of memorable names enhanced this effect. The relationship of memorability to word frequency is discussed.