Lost in PubMed. Factors influencing the success of medical information retrieval

  • Authors:

  • Klaar Vanopstal;Joost Buysschaert;Godelieve Laureys;Robert Vander Stichele

  • Affiliations:

  • Faculty of Applied Linguistics, University College Ghent, Groot-Brittanniëlaan 45, 9000 Ghent, Belgium and Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan ...;Faculty of Applied Linguistics, University College Ghent, Groot-Brittanniëlaan 45, 9000 Ghent, Belgium;Faculty of Arts and Philosophy, Ghent University, Rozier 44, 9000 Ghent, Belgium;Faculty of Medicine and Health Sciences, Heymans Institute of Pharmacology, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

With the explosion of information available on the Web, finding specific medical information in an efficient way has become a considerable challenge. PubMed/MEDLINE offers an alternative to free-text searching on the web, allowing searchers to do a keyword-based search using Medical Subject Headings. However, finding relevant information within a limited time frame remains a difficult task. The current study is based on an error analysis of data from a retrieval experiment conducted at the nursing departments of two Belgian universities and a British university. We identified the main difficulties in query formulation and relevance judgment and compared the profiles of the best and worst performers in the test. For the analysis, a query collection was built from the queries submitted by our test participants. The queries in this collection are all aimed at finding the same specific information in PubMed, which allowed us to identify what exactly went wrong in the query formulation step. Another crucial aspect for efficient information retrieval is relevance judgment. Differences between potential and actual recall of each query offered indications of the extent to which participants overlooked relevant citations. The test participants were divided into ''worst'', ''average'' and ''best'' performers based on the number of relevant citations they selected: zero, one or two and three or more, respectively. We tried to find out what the differences in background and in search behavior were between these three groups.