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A review and classification of approaches for dealing with uncertainty in multi-criteria decision analysis for healthcare decisions.

ABSTRACT: Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health economic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appropriate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles were read for methodological details. The search strategy identified 569 studies. The five approaches most identified were fuzzy set theory (45% of studies), probabilistic sensitivity analysis (15%), deterministic sensitivity analysis (31%), Bayesian framework (6%), and grey theory (3%). A large number of papers considered the analytic hierarchy process in combination with fuzzy set theory (31%). Only 3% of studies were published in healthcare-related journals. In conclusion, our review identified five different approaches to take uncertainty into account in MCDA. The deterministic approach is most likely sufficient for most healthcare policy decisions because of its low complexity and straightforward implementation. However, more complex approaches may be needed when multiple sources of uncertainty must be considered simultaneously.

SUBMITTER: Broekhuizen H

PROVIDER: S-EPMC4544539 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDecision coaching is non-directive support delivered by a trained healthcare provider to help people prepare to actively participate in making healthcare decisions. This study aimed to understand how and under what circumstances decision coaching works for people making healthcare decisions.MethodsWe followed the realist review methodology for this study. This study was built on a Cochrane systematic review of the effectiveness of decision coaching interventions for people facing healthcare decisions. It involved six iterative steps: (1) develop the initial program theory; (2) search for evidence; (3) select, appraise, and prioritize studies; (4) extract and organize data; (5) synthesize evidence; and (6) consult stakeholders and draw conclusions.ResultsWe developed an initial program theory based on decision coaching theories and stakeholder feedback. Of the 2594 citations screened, we prioritized 27 papers for synthesis based on their relevance rating. To refine the program theory, we identified 12 context-mechanism-outcome (CMO) configurations. Essential mechanisms for decision coaching to be initiated include decision coaches', patients', and clinicians' commitments to patients' involvement in decision making and decision coaches' knowledge and skills (four CMOs). CMOs during decision coaching are related to the patient (i.e., willing to confide, perceiving their decisional needs are recognized, acquiring knowledge, feeling supported), and the patient-decision coach interaction (i.e., exchanging information, sharing a common understanding of patient's values) (five CMOs). After decision coaching, the patient's progress in making or implementing a values-based preferred decision can be facilitated by the decision coach's advocacy for the patient, and the patient's deliberation upon options (two CMOs). Leadership support enables decision coaches to have access to essential resources to fulfill their role (one CMOs).DiscussionIn the refined program theory, decision coaching works when there is strong leadership support and commitment from decision coaches, clinicians, and patients. Decision coaches need to be capable in coaching, encourage patients' participation, build a trusting relationship with patients, and act as a liaison between patients and clinicians to facilitate patients' progress in making or implementing an informed values-based preferred option. More empirical studies, especially qualitative and process evaluation studies, are needed to further refine the program theory.

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Project description:The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding "by eye" may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions.

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A review and classification of approaches for dealing with uncertainty in multi-criteria decision analysis for healthcare decisions.

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