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Evaluating the decision accuracy and speed of clinical data visualizations.

ABSTRACT: Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

SUBMITTER: Pieczkiewicz DS

PROVIDER: S-EPMC3000782 | biostudies-literature | 2010 Mar-Apr

REPOSITORIES: biostudies-literature

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Evaluating the decision accuracy and speed of clinical data visualizations.

Pieczkiewicz David S DS Finkelstein Stanley M SM

Journal of the American Medical Informatics Association : JAMIA 20100301 2

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy an ...[more]

PMID: 20190060

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Evaluating the decision accuracy and speed of clinical data visualizations.

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Evaluating the decision accuracy and speed of clinical data visualizations.

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