Cover: Designing Efficient Systematic Reviews Using Economical Allocation, Creation and Synthesis of Medical Evidence

Designing Efficient Systematic Reviews Using Economical Allocation, Creation and Synthesis of Medical Evidence

Published Oct 1, 2014

by Mike Scarpati

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Medical literature and the actions of policymakers have emphasized the importance of evidence-based medicine in recent years, but basing clinical practice on an exploding base of evidence is challenging. Systematic reviews, which are very resource-intensive, are a crucial channel in the pathway from medical literature to clinical practice. This thesis begins by estimating the value of one systematic review, finding that synthesized evidence regarding treatments to prevent osteoporotic fractures generated a net benefit of approximately $450M. Next, the time taken to screen articles in systematic reviews is analyzed, showing that user interface changes can result in significant reductions in resource requirements. Presenting multiple articles on one screen while reviewing titles leads to a seven-fold reduction in time taken per article. Experience and mental state are also related to screening times, with abstracts reviewed at ideal session lengths requiring 33% less time than those at the beginning of a session.

To further increase the speed at which articles can be screened and decrease the cost of preparing systematic reviews, machine learning techniques allow avoidance of up to 80% of articles. When updating an existing review, savings are increased by utilizing the information present in original screening decisions to train the machine learning model. Finally, implementation issues are addressed, paying attention to technical, organizational, and institutional challenges and opportunities.

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This document was submitted as a dissertation in June 2014 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Siddhartha Dalal (Chair), Kanaka Shety, and Jeffrey Wasserman.

This publication is part of the RAND dissertation series. Pardee RAND dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world's leading producer of Ph.D.'s in policy analysis. The dissertations are supervised, reviewed, and approved by a Pardee RAND faculty committee overseeing each dissertation.

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