Mini-Sentinel's systematic reviews of validated methods for identifying health outcomes using administrative and claims data: methods and lessons learned

Abstract

Purpose: To overview the methods used in the Mini-Sentinel systematic reviews of validation studies of algorithms to identify health outcomes in administrative and claims data and to describe lessons learned in the development of search strategies, including their ability to identify articles from previous systematic reviews which used different search strategies.

Methods: Literature searches were conducted using PubMed and the citation database of the Iowa Drug Information Service. Embase was searched for some outcomes. The searches were based on a strategy developed by the Observational Medical Outcomes Partnership (OMOP) researchers. All citations were reviewed by two investigators. Exclusion criteria were applied at abstract and full-text review stages to ultimately identify algorithm validation studies that used data sources from the USA or Canada, as the results of these studies were considered most likely to generalize to Mini-Sentinel data. Nonvalidated algorithms were reviewed if fewer than five algorithm validation studies were identified.

Results: The results of this project are described in the separate articles and reports written on algorithms to identify each outcome of interest.

Conclusions: The Mini-Sentinel systematic reviews of algorithms to identify health outcomes in administrative and claims data are expected to be relatively complete, despite some limitations. Algorithm validation studies are inconsistently indexed in PubMed, creating challenges in conducting systematic reviews of these studies. Google Scholar searches, which can perform text word searches of electronically available articles, are suggested as a strategy to identify studies that are not captured through searches of standard citation databases.