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Multi-Criteria Clinical Decision Support

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  • James Dolan

Abstract

Current models of healthcare quality recommend that patient management decisions be evidence based and patient centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances in the decision-making process, and actively involve both patients and healthcare providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers. Advances in the decision sciences have led to the development of a number of multiple-criteria decision-making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine ‘hard data’ with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision-support systems based on multi-criteria decision-making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings. The goal of this article is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision making. Methods discussed include the balance sheet, the ‘even swap’ method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process. Copyright Adis Data Information BV 2010

Suggested Citation

  • James Dolan, 2010. "Multi-Criteria Clinical Decision Support," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 229-248, December.
  • Handle: RePEc:spr:patien:v:3:y:2010:i:4:p:229-248
    DOI: 10.2165/11539470-000000000-00000
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    References listed on IDEAS

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    4. Isabel Cristina Panziera Marques & Mário Franco, 2020. "Cooperation networks in the area of health: systematic literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1727-1750, March.
    5. Marjan Hummel & Fabian Volz & Jeannette Manen & Marion Danner & Charalabos-Markos Dintsios & Maarten IJzerman & Andreas Gerber, 2012. "Using the Analytic Hierarchy Process to Elicit Patient Preferences," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(4), pages 225-237, December.
    6. Do Hwa Byun & Rho Soon Chang & Myung-Bae Park & Hyo-Rim Son & Chun-Bae Kim, 2021. "Prioritizing Community-Based Intervention Programs for Improving Treatment Compliance of Patients with Chronic Diseases: Applying an Analytic Hierarchy Process," IJERPH, MDPI, vol. 18(2), pages 1-20, January.
    7. Angelis, Aris & Kanavos, Panos, 2017. "Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: The Advance Value Framework," Social Science & Medicine, Elsevier, vol. 188(C), pages 137-156.
    8. Maarten Ijzerman & Janine Til & John Bridges, 2012. "A Comparison of Analytic Hierarchy Process and Conjoint Analysis Methods in Assessing Treatment Alternatives for Stroke Rehabilitation," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(1), pages 45-56, March.
    9. Michael Hoerger, 2012. "Coping strategies and immune neglect in affective forecasting: Direct evidence and key moderators," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(1), pages 86-96, January.
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