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Measuring Delay Discounting in Humans Using an Adjusting Amount Task

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Published on: January 9, 2016

Quantifying short run cost-effectiveness during a gradual implementation process.

Gijs van de Wetering¹, Willem H Woertman, Andre L Verbeek

¹Department of Epidemiology, Biostatistics, and HTA, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands, g.vandewetering@ebh.umcn.nl.

The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care

|October 30, 2012

Summary

Gradual healthcare technology adoption causes short-term inefficiencies due to delayed health gains and old technology overcapacity. A new model quantifies these losses and optimizes implementation patterns for cost-effective technology transitions.

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Area of Science:

Health Economics
Health Technology Assessment
Healthcare Management

Background:

Standard cost-effectiveness analysis (CEA) often overlooks short-run inefficiencies during gradual technology implementation.
These inefficiencies arise from the temporary coexistence of old and new technologies, leading to underutilization and delayed benefits.
Existing CEA models typically assume a steady state, failing to capture dynamic implementation challenges.

Purpose of the Study:

To develop and apply a model that quantifies short-run inefficiencies during gradual healthcare technology adoption.
To inform decision-makers on optimal implementation strategies for new, cost-effective technologies.
To analyze the economic impact of technology transitions beyond steady-state assumptions.

Main Methods:

Construction of a model integrating incremental net benefit equations for periods of technology coexistence and complete substitution.
Incorporation of factors such as implementation rate, old technology capital depreciation, and demand curves.
Application of the model to a real-world case study: the transition from screen-film to digital mammography in the Netherlands.

Main Results:

The model quantifies short-run efficiency losses not captured by traditional CEA.
It identifies optimal implementation patterns considering factors like adoption speed and technology lifecycle.
The digital mammography case study demonstrates the practical application and insights gained from the model.

Conclusions:

Gradual implementation of new healthcare technologies can lead to significant, quantifiable short-run inefficiencies.
A dynamic modeling approach is crucial for accurate cost-effectiveness assessment during technology transitions.
Optimizing implementation patterns can mitigate short-run losses and maximize the benefits of cost-effective technologies.