Integrating Evidence and Individual Preferences Using a Web-Based Multi-Criteria Decision Analytic Tool

Cunich, Michelle; Salkeld, Glenn; Dowie, Jack; Henderson, Joan; Bayram, Clare; Britt, Helena; Howard, Kirsten

doi:10.2165/11587070-000000000-00000

Cited by 21 publications

(17 citation statements)

References 15 publications

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“…We identified studies that actually only used their results in practice in the CDM context-for example, for development of a decision aid [33,70,71]. In most articles, explicit or implicit statements made by the authors referred to just an intended use of the results.…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Review to Identify the Use of Preference Elicitation Methods in Healthcare Decision Making

et al. 2014

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Introduction Preference elicitation methods help to increase patient-centred medical decision making (MDM) by measuring benefit and value. Preferences can be applied in decisions regarding reimbursement, including health technology assessment (HTA); market access, including benefit-risk assessment (BRA); and clinical care. These three decision contexts have different requirements for use and elicitation of preferences. Objectives This systematic review identified studies using preference elicitation methods and summarized methodological and practical characteristics within the requirements of the three contexts. Methods The search terms included those related to MDM and patient preferences. Only articles with original data from quantitative preference elicitation methods were included. Results The selected articles (n = 322) included 379 preference elicitation methods, comprising matching methods [MM] (n = 71, 18.7 %), discrete choice experiments [DCEs] (n = 96, 25.3 %), multi-criteria decision analysis (n = 12, 3.2 %) and other methods (n = 200, 52.8 %; i.e. rating scales, which provide estimates inconsistent with utility theory). Most publications of preference elicitation methods had an intended use in clinical decisions (n = 134, 40 %). Fewer preference studies had an intended use in HTA (n = 68, 20 %) or BRA (n = 12, 4 %). In clinical decisions, rating, ranking, visual analogue scales and direct choice are used most often. In HTA, DCEs and MM are both used frequently, and elicitation of preferences in BRA was limited to DCEs. Conclusion Relatively simple preference methods are often adequate in clinical decisions because they are easy to administer and have a low cognitive burden. MM and DCE fulfil the requirements of HTA and BRA but are complex for respondents. No preference elicitation methods with a low cognitive burden could adequately inform HTA and BRA decisions. Key PointsPreference elicitation methods can be used to quantify relative benefits and to value various aspects of a drug or health states.Most studies found in the current literature identify preferences for guiding clinical decisions.Fewer preference studies directly support reimbursement (health technology assessment [HTA]) or market access (benefit-risk assessment [BRA]) decisions.Clinical decisions require more patient-friendly and straightforward preference methods.Matching methods and discrete choice experiments fulfil almost all of the contexts' requirements of BRA and HTA. However, those methods can be cognitively complex for the respondents if the number of attributes is large or the attributes are difficult to understand. Marieke G. M. Weernink and Sarah I. M. Janus contributed equally to this work. Electronic supplementary material The online version of this article (

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Section: Resultsmentioning

confidence: 99%

“…DRW does not exhibit explicit trading of characteristics, and only criteria weights are calculated. One study employed this preference method [33].…”

Section: Multi-criteria Decision Methodsmentioning

confidence: 99%

A Systematic Review to Identify the Use of Preference Elicitation Methods in Healthcare Decision Making

et al. 2014

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“…We performed an analysis of personalization in current patient decision aids [35], reviewed existing solutions, performed focus group interviews eliciting users’ needs [36] and reviewed current MCDA-based decision dashboards [37–39]. …”

Section: Methodsmentioning

confidence: 99%

The development and feasibility of a personal health-optimization system for people with bipolar disorder

Eiring

Nytrøen

Kienlin

et al. 2017

BMC Med Inform Decis Mak

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BackgroundPeople with bipolar disorder often experience ill health and have considerably reduced life expectancies. Suboptimal treatment is common and includes a lack of effective medicines, overtreatment, and non-adherence to medical interventions and lifestyle measures. E- and m-health applications support patients in optimizing their treatment but often exhibit conceptual and technical shortcomings. The objective of this work was to develop and test the usability of a system targeting suboptimal treatment and compare the service to other genres and strategies.MethodsBased on the frameworks of shared decision-making, multi-criteria decision analysis, and single-subject research design, we interviewed potential users, reviewed research and current approaches, and created a first version using a rapid prototyping framework. We then iteratively improved and expanded the service based on formative usability testing with patients, healthcare providers, and laypeople from Norway, the UK, and Ukraine. The evidence-based health-optimization system was developed using systematic methods. The System Usability Scale and a questionnaire were administered in formative and summative tests. A comparison of the system to current standards for clinical practice guidelines and patient decision aids was performed.ResultsSeventy-eight potential users identified 82 issues. Driven by user feedback, the limited first version was developed into a more comprehensive system. The current version encompasses 21 integrated core features, supporting 6 health-optimization strategies. One crucial feature enables patients and clinicians to explore the likely value of treatments based on mathematical integration of self-reported and research data and the patient’s preferences. The mean ± SD (median) system usability score of the patient-oriented subsystem was 71 ± 18 (73). The mean ± SD (median) system usability score in the summative usability testing was 78 ± 18 (75), well above the norm score of 68. Feedback from the questionnaire was generally positive. Eighteen out of 23 components in the system are not required in international standards for patient decision aids and clinical practice guidelines.ConclusionWe have developed the first evidence-based health-optimization system enabling patients, clinicians, and caregivers to collaborate in optimizing the patient’s health on a shared platform. User tests indicate that the feasibility of the system is acceptable.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0481-x) contains supplementary material, which is available to authorized users.

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“…135 Interpretation is part of information use and involves assigning meaning by placing information in the context of a patient case. 85,136 Clinicians may struggle with interpretation 137 due to difficulties with comprehension [138][139][140][141] or ambiguity. 142 Clinical reasoning follows from interpretation.…”

Section: Part 2 Activity: Usual Activitymentioning

confidence: 99%

“…57 Clinicians also assess risk in relation to negative outcomes, such as falling or disease progression. 83,141,150,[155][156][157][158][159][160][161][162][163][164][165] Clinical reasoning (Box 6). Clinical reasoning is a "context-dependent way of thinking and decision making in professional practice to guide practice actions.…”

Section: Part 2 Activity: Usual Activitymentioning

confidence: 99%

Comprehensive process model of clinical information interaction in primary care: results of a “best-fit” framework synthesis

Veinot

Senteio

Hanauer

et al. 2017

Journal of the American Medical Informatics Association

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Integrating Evidence and Individual Preferences Using a Web-Based Multi-Criteria Decision Analytic Tool

Cited by 21 publications

References 15 publications

A Systematic Review to Identify the Use of Preference Elicitation Methods in Healthcare Decision Making

A Systematic Review to Identify the Use of Preference Elicitation Methods in Healthcare Decision Making

The development and feasibility of a personal health-optimization system for people with bipolar disorder

Comprehensive process model of clinical information interaction in primary care: results of a “best-fit” framework synthesis

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