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

Abstract

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.

Development of the WHO-INTEGRATE evidence-to-decision framework: an overview of systematic reviews of decision criteria for health decision-making

“…The criteria used across different types of decisions have been addressed in multiple reviews. Several reviews have explored the criteria used when applying multi-criteria decision analysis (MCDA) [13,[23][24][25][26][27][28], an"umbrella term to describe a collection of formal approaches which seek to take explicit account of multiple criteria in helping individuals or groups explore decisions that matter" [29]. Other reviews have explored the criteria employed in the context of health technology assessments (HTA), which intend to examine social, economic, organizational and ethical considerations in relation to health technologies in a comprehensive manner [30]; these covered both the criteria to inform decisions about health technologies by national or sub-national HTA institutions [19,[31][32][33], and the criteria used for selecting the technologies or interventions a HTA is to be conducted on [8,34].…”

Using co‐creation and multi‐criteria decision analysis to close service gaps for underserved populations

“…In other studies, MCDA has also included a Step 6 wherein the rank order implied by weighted averages has been validated or revised by key stakeholders during group discussion of criteria, weights, performance and priorities in a deliberative process . MCDA has previously been used to incorporate patient preferences into health‐care decision making for a wide range of diseases and interventions . For the present study, an online decision support system, Annalisa v1.0 [Maldaba Ltd], was used to implement Steps 2‐5 of the MCDA exercise .…”

“…This type of information can be used and re-used to predict how service improvements would be "valued" by existing users, potential users and partners/ carers, helping decision makers to navigate the often complex tradeoffs required when setting health-care priorities. 23 With regard to co-design or co-creation of services, existing frameworks offer structured processes for generating novel improvements or interventions that are consistent with consumer needs and preferences. 15,24 For example, one such process 24 was structured as a series of steps or phases: commencing with research and analysis "using qualitative methods… to gain deep insights into end users' context, wants and needs" (p20); progressing to idea generation "typically led by the design team" (p21); idea testing and refinement "using paper-or experience-prototyping" (p21); and concluding with evaluation and priority setting "to increase collective ownership of the outcome, and to encourage shared responsibility for progress" (p23).…”

“…30 MCDA has previously been used to incorporate patient preferences into health-care decision making for a wide range of diseases and interventions. 17,23 For the present study, an online…”

“…With regard to preference elicitation, MCDA can provide a detailed understanding of consumer preferences, including a description of potentially relevant performance criteria (components of “value” to consumers) and the relative weight given to these criteria by different types of consumer. This type of information can be used and re‐used to predict how service improvements would be “valued” by existing users, potential users and partners/carers, helping decision makers to navigate the often complex trade‐offs required when setting health‐care priorities …”