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Finding best evidence for evidence-based best practice recommendations in health care: the initial decision support system design

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Abstract

A major problem for Canadian health organizations is finding best evidence for evidence-based best practice recommendations. Medications are not always effectively used and misuse may harm patients. Drugs are the fastest-growing element of Canadian health care spending, second only to hospital spending. Three hundred million prescriptions are filled annually. Prescription drugs accounted for 5.8% of total health care spending in 1980 and close to 18% today. A primary long-term goal of this research is to develop a decision support system for evidence-based management, quality control and best practice recommendations for medical prescriptions. Our results will improve accessibility and management of information by: (1) building an prototype for adaptive information extraction, text and data mining from (online) documents to find evidence on which to base best practices; and (2) employing multiply sectioned Bayesian networks (MSBNs) to infer a probabilistic interpretation to validate evidence for recommendations; MSBNs provide this structure. Best practices to improve drug-related health outcomes; patients’ quality of life; and cost-effective use of medications by changing knowledge and behavior. This research will support next generation eHealth decision support systems, which routinely find and verify evidence from multiple sources, leading to cost-effective use of drugs, improve patients’ quality of life and optimize drug-related health outcomes.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, York University, Toronto, ON, M3J 1P3, Canada

    Nick Cercone, Xiangdong An, Jiye Li, Zhenmei Gu & Aijun An

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  1. Nick Cercone
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  2. Xiangdong An
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  3. Jiye Li
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  4. Zhenmei Gu
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  5. Aijun An
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Correspondence to Nick Cercone.

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Cercone, N., An, X., Li, J. et al. Finding best evidence for evidence-based best practice recommendations in health care: the initial decision support system design. Knowl Inf Syst 29, 159–201 (2011). https://doi.org/10.1007/s10115-011-0439-8

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