PS4-5 TRANSFORMING THE COST-EFFECTIVENESS THRESHOLD INTO A 'VALUE THRESHOLD': INITIAL FINDINGS FROM A SIMULATION MODEL

Tuesday, June 14, 2016
Exhibition Space (30 Euston Square)
Poster Board # PS4-5

Mike Paulden, MA., MSc., University of Alberta, Edmonton, AB, Canada and Christopher McCabe, PhD, Department of Emergency Medicine, University of Alberta, Edmonton, AB, Canada
Background:  The conventional model of the cost-effectiveness (CE) threshold adopts numerous assumptions, including constant returns and divisibility of technologies. The consequences of imperfect information are not considered, nor the possibility that interventions may represent disinvestments (releasing resources rather than displacing existing services). Furthermore, no consideration is made for aspects of ‘value’ not captured by the quality-adjusted life year (QALY).
Purpose:  Our objective is to transform the CE threshold into a ‘value threshold’ that is of greater use to decision makers, while addressing the limitations described above.
Method(s):  As a first step we developed a simulation model of a hypothetical health system in order to understand how a ‘value threshold’ may differ from a conventional CE threshold. Of interest are the implications of: (a) relaxing assumptions such as constant returns and divisibility of technologies; (b) incorporating imperfect information and ‘value’ considerations within a complex health system with multiple decision makers; and (c) extending the threshold so it may be used to appraise disinvestments.
Result(s):  Under conventional assumptions, the CE threshold has ‘kinks’ where displacement switches between services. Under diminishing returns these ‘kinks’ smooth out. When technologies are indivisible, the threshold instead follows a step function. Imperfect information and ‘value’ considerations beyond the QALY may justify different thresholds for investment and disinvestment decisions.
Conclusion(s):  This work represents a first attempt at constructing a more sophisticated theoretical model of value-based decision making within complex health systems. Our findings provide insights for future theoretical work and a rich source of hypotheses for empirical research.