Multi-Criteria Clinical Decision Support

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 into the decision making process and actively involve both patients along with health care 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 paper 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 (AHP)

Government policy and healthcare management: proposal of a shared decision-making model

“…A number of researchers highlighted the benefits of using AHP to explore user needs in healthcare (Hummel and IJzerman, 2009;Pecchia et al, 2013) particularly for HTA (Danner et al, 2011;Bridges, 2005), for choosing treatments (Dolan, 1995), and to improve patient centered healthcare (Dolan, 2010;Bridges, 2008). Other methods that have attempted to elicit and quantify user needs in healthcare as the conjoint analysis (CA) , discrete choice experiments (de BekkerGrob, et al, 2012) and best-worst scaling (Gallego et al, 2011).…”

Development of an Artificial Intelligence Model to Guide the Management of Blood Pressure, Fluid Volume, and Dialysis Dose in End-Stage Kidney Disease Patients: Proof of Concept and First Clinical Assessment

“…Artificial intelligence has already been successfully applied at the point of care to support physicians in the decision-making process [6-9]. The availability of accurate, longitudinal, real-life data is a key factor for the development of reproducible predictive models.…”

Development of Framework for Assessing Influenza Virus Pandemic Risk

“…The approach described here uses opinions from subject matter experts to populate a simple, multiattribute additive model ( 6 ) that combines information from well-established decision analysis methods ( 7 , 8 ) to assist in decision making and prioritization processes. Traditional risk assessment approaches ( 9 ) are not directly applicable to the IRAT; however, the general guiding principles still apply (Figure 1).…”

“…For example, the definition of the element “Antivirals and Treatment Options” states, “For the purposes of the risk assessment tool, antiviral susceptibility refers to the predicted or demonstrated efficacy of available antiviral agents against animal influenza viruses.” A low-risk score ( 1 – 3 ) for this element is defined as “no evidence of clinically relevant resistance to any of the antiviral drugs approved for human use (neuraminidase inhibitors and M2 blockers).” A moderate-risk score ( 4 – 7 ) is defined as “sensitive to all neuraminidase inhibitors but resistant to M2 blockers.” A high-risk score ( 8 – 10 ) for this element is defined as “resistant to one or more neuraminidase inhibitor antiviral drugs.” All 10 elements have definitions for low-, moderate-, and high-risk scores.…”