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Applied Health Economics and Health Policy

, Volume 15, Issue 2, pp 155–162 | Cite as

Amplifying Each Patient’s Voice: A Systematic Review of Multi-criteria Decision Analyses Involving Patients

  • Kevin Marsh
  • J. Jaime Caro
  • Alaa Hamed
  • Erica Zaiser
Systematic Review

Abstract

Background

Qualitative methods tend to be used to incorporate patient preferences into healthcare decision making. However, for patient preferences to be given adequate consideration by decision makers they need to be quantified. Multi-criteria decision analysis (MCDA) is one way to quantify and capture the patient voice. The objective of this review was to report on existing MCDAs involving patients to support the future use of MCDA to capture the patient voice.

Methods

MEDLINE and EMBASE were searched in June 2014 for English-language papers with no date restriction. The following search terms were used: ‘multi-criteria decision*’, ‘multiple criteria decision*’, ‘MCDA’, ‘benefit risk assessment*’, ‘risk benefit assessment*’, ‘multicriteri* decision*’, ‘MCDM’, ‘multi-criteri* decision*’. Abstracts were included if they reported the application of MCDA to assess healthcare interventions where patients were the source of weights. Abstracts were excluded if they did not apply MCDA, such as discussions of how MCDA could be used; or did not evaluate healthcare interventions, such as MCDAs to assess the level of health need in a locality. Data were extracted on weighting method, variation in patient and expert preferences, and discussion on different weighting techniques.

Results

The review identified ten English-language studies that reported an MCDA to assess healthcare interventions and involved patients as a source of weights. These studies reported 12 applications of MCDA. Different methods of preference elicitation were employed: direct weighting in workshops; discrete choice experiment surveys; and the analytical hierarchy process using both workshops and surveys. There was significant heterogeneity in patient responses and differences between patients, who put greater weight on disease characteristics and treatment convenience, and experts, who put more weight on efficacy. The studies highlighted cognitive challenges associated with some weighting methods, though patients’ views on their ability to undertake weighting tasks was positive.

Conclusion

This review identified several recent examples of MCDA used to elicit patient preferences, which support the feasibility of using MCDA to capture the patient voice. Challenges identified included, how best to reflect the heterogeneity of patient preferences in decision making and how to manage the cognitive burden associated with some MCDA tasks.

Keywords

Health Technology Assessment Discrete Choice Experiment Multiple Criterion Decision Benefit Risk Assessment Weighting Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank Evan Davies, Tereza Lanitis, David Neasham and Panos Orfanos at Evidera for their support in reviewing abstracts and full texts.

Compliance with Ethical Standards

Evidera received financial assistance from Sanofi-Genzyme to conduct the study and assist in preparing the manuscript. Authors Kevin Marsh, Erica Zaiser and Jaime Caro are all employees of Evidera. Alaa Hamed is an employee of Sanofi-Genzyme.

Financial support

Financial support for this study was provided entirely by a contract with Sanofi-Genzyme. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The following author is employed by the sponsor: Alaa Hamed.

Authors’ contributions

All authors contributed to the conception, design and writing of this manuscript. KM and EZ were responsible for the implementation of the review.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.EvideraLondonUK
  2. 2.EvideraLexingtonUSA
  3. 3.Sanofi GenzymeCambridgeUSA

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