Innovations in data collection, management, and archiving for systematic reviews

Tianjing Li, S. Swaroop Vedula, Nira Hadar, Christopher Parkin, Joseph Lau, Kay Dickersin

Research output: Contribution to journalArticlepeer-review


Data abstraction is a key step in conducting systematic reviews because data collected from study reports form the basis of appropriate conclusions. Recent methodological standards and expectations highlight several principles for data collection. To support implementation of these standards, this article provides a step-by-step tutorial for selecting data collection tools; constructing data collection forms; and abstracting, managing, and archiving data for systematic reviews. Examples are drawn from recent experience using the Systematic Review Data Repository for data collection and management. If it is done well, data collection for systematic reviews only needs to be done by 1 team and placed into a publicly accessible database for future use. Technological innovations, such as the Systematic Review Data Repository, will contribute to finding trustworthy answers for many health and health care questions

Original languageEnglish (US)
Pages (from-to)287-294
Number of pages8
JournalAnnals of internal medicine
Issue number4
StatePublished - Feb 17 2015

ASJC Scopus subject areas

  • Internal Medicine


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