Clinical decision velocity is increased when meta-search filters enhance an evidence retrieval system

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Objective: To test whether the use of an evidence retrieval system that uses clinically targeted meta-search filters can enhance the rate at which clinicians make correct decisions, reduce the effort involved in locating evidence, and provide an intuitive match between clinical tasks and search filters. Design: A laboratory experiment under controlled conditions asked 75 clinicians to answer eight randomly sequenced clinical questions, using one of two randomly assigned search engines. The first search engine Quick Clinical (QC) was equipped with meta-search filters (the combined use of meta-search and search filters) designed to answer typical clinical questions e.g., treatment, diagnosis, and the second 'library model' system (LM) offered free access to an identical evidence set with no filter support. Measurements: Changes in clinical decision making were measured by the proportion of correct post-search answers provided to questions, the time taken to answer questions, and the number of searches and links to documents followed in a search session. The intuitive match between meta-search filters and clinical tasks was measured by the proportion and distribution of filters selected for individual clinical questions. Results: Clinicians in the two groups performed equally well pre-search. Post search answers improved overall by 21%, with 52.2% of answers correct with QC and 54.7% with LM (χ2 = 0.33, df = 1, p > 0.05). Users of QC obtained a significantly greater percentage of their correct answers within the first two minutes of searching compared to LM users (QC 58.2%; LM 32.9%; χ2 = 19.203, df = 1, p < 0.001). There was a statistical difference for QC and LM survival curves, which plotted overall time to answer questions, irrespective of answer (Wilcoxon, p = 0.019) and for the average time to provide a correct answer (Wilcoxon, p = 0.006). The QC system users conducted significantly fewer searches per scenario (m = 3.0 SD = 1.15 versus m = 5.5 SD1.97, t = 6.63, df = 72, p = 0.0001). Clinicians using the QC system followed fewer document links than did those who used LM (respectively 3.9 links SD = 1.20 versus 4.7 links SD = 1.79, t = 2.13, df = 72, p = 0.0368). In 6 of the 8 questions, two meta-search filters accounted for 89% or more of clinicians' first choice, suggesting the choice of filter intuitively matched the clinical decision task at hand. Conclusions: Meta-search filters result in clinicians arriving at answers more quickly than unconstrained searches across information sources, and appear to increase the rate with which correct decisions are made. In time restricted clinical settings meta-search filters may thus improve overall decision accuracy, as fewer searches that could otherwise lead to a correct answer are abandoned. Meta-search filters appear to be intuitive to use, suggesting that the simplicity of the user model would fit very well into clinical settings.

LanguageEnglish
Pages638-646
Number of pages9
JournalJournal of the American Medical Informatics Association
Volume15
Issue number5
DOIs
Publication statusPublished - Sep 2008
Externally publishedYes

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@article{4b073107ee7b4a318ec425fab1ca8365,
title = "Clinical decision velocity is increased when meta-search filters enhance an evidence retrieval system",
abstract = "Objective: To test whether the use of an evidence retrieval system that uses clinically targeted meta-search filters can enhance the rate at which clinicians make correct decisions, reduce the effort involved in locating evidence, and provide an intuitive match between clinical tasks and search filters. Design: A laboratory experiment under controlled conditions asked 75 clinicians to answer eight randomly sequenced clinical questions, using one of two randomly assigned search engines. The first search engine Quick Clinical (QC) was equipped with meta-search filters (the combined use of meta-search and search filters) designed to answer typical clinical questions e.g., treatment, diagnosis, and the second 'library model' system (LM) offered free access to an identical evidence set with no filter support. Measurements: Changes in clinical decision making were measured by the proportion of correct post-search answers provided to questions, the time taken to answer questions, and the number of searches and links to documents followed in a search session. The intuitive match between meta-search filters and clinical tasks was measured by the proportion and distribution of filters selected for individual clinical questions. Results: Clinicians in the two groups performed equally well pre-search. Post search answers improved overall by 21{\%}, with 52.2{\%} of answers correct with QC and 54.7{\%} with LM (χ2 = 0.33, df = 1, p > 0.05). Users of QC obtained a significantly greater percentage of their correct answers within the first two minutes of searching compared to LM users (QC 58.2{\%}; LM 32.9{\%}; χ2 = 19.203, df = 1, p < 0.001). There was a statistical difference for QC and LM survival curves, which plotted overall time to answer questions, irrespective of answer (Wilcoxon, p = 0.019) and for the average time to provide a correct answer (Wilcoxon, p = 0.006). The QC system users conducted significantly fewer searches per scenario (m = 3.0 SD = 1.15 versus m = 5.5 SD1.97, t = 6.63, df = 72, p = 0.0001). Clinicians using the QC system followed fewer document links than did those who used LM (respectively 3.9 links SD = 1.20 versus 4.7 links SD = 1.79, t = 2.13, df = 72, p = 0.0368). In 6 of the 8 questions, two meta-search filters accounted for 89{\%} or more of clinicians' first choice, suggesting the choice of filter intuitively matched the clinical decision task at hand. Conclusions: Meta-search filters result in clinicians arriving at answers more quickly than unconstrained searches across information sources, and appear to increase the rate with which correct decisions are made. In time restricted clinical settings meta-search filters may thus improve overall decision accuracy, as fewer searches that could otherwise lead to a correct answer are abandoned. Meta-search filters appear to be intuitive to use, suggesting that the simplicity of the user model would fit very well into clinical settings.",
author = "Enrico Coiera and Westbrook, {Johanna I.} and Kris Rogers",
year = "2008",
month = "9",
doi = "10.1197/jamia.M2765",
language = "English",
volume = "15",
pages = "638--646",
journal = "Journal of the American Medical Informatics Association : JAMIA",
issn = "1067-5027",
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}

Clinical decision velocity is increased when meta-search filters enhance an evidence retrieval system. / Coiera, Enrico; Westbrook, Johanna I.; Rogers, Kris.

In: Journal of the American Medical Informatics Association, Vol. 15, No. 5, 09.2008, p. 638-646.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Clinical decision velocity is increased when meta-search filters enhance an evidence retrieval system

AU - Coiera, Enrico

AU - Westbrook, Johanna I.

AU - Rogers, Kris

PY - 2008/9

Y1 - 2008/9

N2 - Objective: To test whether the use of an evidence retrieval system that uses clinically targeted meta-search filters can enhance the rate at which clinicians make correct decisions, reduce the effort involved in locating evidence, and provide an intuitive match between clinical tasks and search filters. Design: A laboratory experiment under controlled conditions asked 75 clinicians to answer eight randomly sequenced clinical questions, using one of two randomly assigned search engines. The first search engine Quick Clinical (QC) was equipped with meta-search filters (the combined use of meta-search and search filters) designed to answer typical clinical questions e.g., treatment, diagnosis, and the second 'library model' system (LM) offered free access to an identical evidence set with no filter support. Measurements: Changes in clinical decision making were measured by the proportion of correct post-search answers provided to questions, the time taken to answer questions, and the number of searches and links to documents followed in a search session. The intuitive match between meta-search filters and clinical tasks was measured by the proportion and distribution of filters selected for individual clinical questions. Results: Clinicians in the two groups performed equally well pre-search. Post search answers improved overall by 21%, with 52.2% of answers correct with QC and 54.7% with LM (χ2 = 0.33, df = 1, p > 0.05). Users of QC obtained a significantly greater percentage of their correct answers within the first two minutes of searching compared to LM users (QC 58.2%; LM 32.9%; χ2 = 19.203, df = 1, p < 0.001). There was a statistical difference for QC and LM survival curves, which plotted overall time to answer questions, irrespective of answer (Wilcoxon, p = 0.019) and for the average time to provide a correct answer (Wilcoxon, p = 0.006). The QC system users conducted significantly fewer searches per scenario (m = 3.0 SD = 1.15 versus m = 5.5 SD1.97, t = 6.63, df = 72, p = 0.0001). Clinicians using the QC system followed fewer document links than did those who used LM (respectively 3.9 links SD = 1.20 versus 4.7 links SD = 1.79, t = 2.13, df = 72, p = 0.0368). In 6 of the 8 questions, two meta-search filters accounted for 89% or more of clinicians' first choice, suggesting the choice of filter intuitively matched the clinical decision task at hand. Conclusions: Meta-search filters result in clinicians arriving at answers more quickly than unconstrained searches across information sources, and appear to increase the rate with which correct decisions are made. In time restricted clinical settings meta-search filters may thus improve overall decision accuracy, as fewer searches that could otherwise lead to a correct answer are abandoned. Meta-search filters appear to be intuitive to use, suggesting that the simplicity of the user model would fit very well into clinical settings.

AB - Objective: To test whether the use of an evidence retrieval system that uses clinically targeted meta-search filters can enhance the rate at which clinicians make correct decisions, reduce the effort involved in locating evidence, and provide an intuitive match between clinical tasks and search filters. Design: A laboratory experiment under controlled conditions asked 75 clinicians to answer eight randomly sequenced clinical questions, using one of two randomly assigned search engines. The first search engine Quick Clinical (QC) was equipped with meta-search filters (the combined use of meta-search and search filters) designed to answer typical clinical questions e.g., treatment, diagnosis, and the second 'library model' system (LM) offered free access to an identical evidence set with no filter support. Measurements: Changes in clinical decision making were measured by the proportion of correct post-search answers provided to questions, the time taken to answer questions, and the number of searches and links to documents followed in a search session. The intuitive match between meta-search filters and clinical tasks was measured by the proportion and distribution of filters selected for individual clinical questions. Results: Clinicians in the two groups performed equally well pre-search. Post search answers improved overall by 21%, with 52.2% of answers correct with QC and 54.7% with LM (χ2 = 0.33, df = 1, p > 0.05). Users of QC obtained a significantly greater percentage of their correct answers within the first two minutes of searching compared to LM users (QC 58.2%; LM 32.9%; χ2 = 19.203, df = 1, p < 0.001). There was a statistical difference for QC and LM survival curves, which plotted overall time to answer questions, irrespective of answer (Wilcoxon, p = 0.019) and for the average time to provide a correct answer (Wilcoxon, p = 0.006). The QC system users conducted significantly fewer searches per scenario (m = 3.0 SD = 1.15 versus m = 5.5 SD1.97, t = 6.63, df = 72, p = 0.0001). Clinicians using the QC system followed fewer document links than did those who used LM (respectively 3.9 links SD = 1.20 versus 4.7 links SD = 1.79, t = 2.13, df = 72, p = 0.0368). In 6 of the 8 questions, two meta-search filters accounted for 89% or more of clinicians' first choice, suggesting the choice of filter intuitively matched the clinical decision task at hand. Conclusions: Meta-search filters result in clinicians arriving at answers more quickly than unconstrained searches across information sources, and appear to increase the rate with which correct decisions are made. In time restricted clinical settings meta-search filters may thus improve overall decision accuracy, as fewer searches that could otherwise lead to a correct answer are abandoned. Meta-search filters appear to be intuitive to use, suggesting that the simplicity of the user model would fit very well into clinical settings.

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