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ASSESSING THE LEARNING CURVE EFFECT IN HEALTH TECHNOLOGIES

Lessons From The Nonclinical Literature

Published online by Cambridge University Press:  20 May 2002

Craig R. Ramsay
Affiliation:
University of Aberdeen
Sheila A. Wallace
Affiliation:
University of Aberdeen
Paul H. Garthwaite
Affiliation:
Open University
Andrew F. Monk
Affiliation:
University of York
Ian T. Russell
Affiliation:
University of York
Adrian M. Grant
Affiliation:
University of Aberdeen

Abstract

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better.

Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves.

Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects.

Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets.

Type
Research Article
Copyright
© 2002 Cambridge University Press

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