Presentation (CTI)

OBJECTIVES: The development of novel value frameworks, such as the ‘ISPOR value flower’, has highlighted the potential for traditional cost-effectiveness frameworks to undervalue health interventions and the growing need to recognise value from the perspective of multiple stakeholders. To fully characterise all stakeholder value perspectives for a health condition, large amounts of evidence must be collected, organised and synthesised into a comprehensive framework. We propose a methodology for organising evidence into a graphical evidence framework, identifying value domains, value drivers, and their interdependencies, and with a visual representation of breadth and strength of evidence, using Alzheimer’s Disease (AD) as a case study.
METHODS: Ovid/PubMed searches were conducted to identify epidemiology, pathophysiology, outcomes (clinical, societal and economic) and healthcare resource use (HCRU) in AD. Researchers reviewed the evidence, identified value domains, categorised the relationships between the domains, and assessed the strength of evidence for each domain and connection. This information was graphically represented in an evidence framework.
RESULTS: A holistic evidence framework was developed, representing the stakeholder perspectives of patients (quality of life, disability), society (carer burden, indirect costs) and healthcare delivery (system capacity, HCRU). All stakeholder value perspectives were linked directly or indirectly via a network of evidence, including epidemiological, disease management (diagnosis and treatment), patient preference and costs, to create a connected system. Connections between value domains and the strength of the relevant supporting evidence were demonstrated visually, and evidence gaps highlighted. For AD, the framework showed symptom severity is strongly linked to informal care, indirect costs and HCRU, whilst limited evidence exists to link pharmacotherapy with key outcomes.
CONCLUSIONS: Utilizing a visual framework to organize and present large quantities of data can facilitate a better understanding of the consequences of healthcare decision-making and provide a more holistic representation of value than is possible using traditional approaches.