The Patient: Patient-Centered Outcomes Research

, Volume 3, Issue 4, pp 229–248 | Cite as

Multi-Criteria Clinical Decision Support

A Primer on the Use of Multiple-Criteria Decision-Making Methods to Promote Evidence-Based, Patient-Centered Healthcare
Practical Application

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.

Notes

Acknowledgements

This work was supported by grant 5K24HL093488-02 from the National Heart Lung and Blood Institute (NHLBI), US National Institutes of Health. The NHLBI played no other part in the preparation of this manuscript.

The author has no conflicts of interest that are relevant to the content of this article.

Supplementary material

40271_2012_3040229_MOESM1_ESM.pdf (102 kb)
Supplementary material, approximately 105 KB.

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Copyright information

© Adis Data Information BV 2010

Authors and Affiliations

  1. 1.Department of Community and Preventive MedicineUniversity of RochesterRochesterUSA

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