Clinical Cost-Effectiveness as a Novel Metric for Steering Emerging Medical Technology
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richards-drichsdm-sm-sdm-2024-thesis.pdf
Description
Thesis PDF
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4.58 MB
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Adobe PDF
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a23dc600838796a8ae17cf78fd618131
Author(s)
Richards, Daniel Herndon
Advisor(s)
Cima, Michael J.
Westover, M. Brandon
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Background: Steering an emerging medical technology involves making decisions under uncertainty. Localized drug delivery (LDD) is an emerging medical technology that may be useful in treating epilepsy, which is burdensome and difficult to clinically manage. Costeffectiveness analysis (CEA) is a model-based, problem-oriented framework for determining whether a treatment should be prescribed and reimbursed, though it is typically used to compare treatment alternatives that are already clinically available. Two research questions were posed: How can a clinical CEA be constructed for an emerging medical technology to enhance its steering? And, under what conditions would an emerging technology, LDD, be prescribed in place of resective surgery for drug-resistant epilepsy? Methods: A CEA was constructed with the clinical decision point defined as pediatric patients with drug-resistant epilepsy of focal origin. A new treatment alternative, LDD, was proposed as a solution-neutral, generalized concept, and technological factors were posited that influence parameters in the CEA. A one-way sensitivity analysis was conducted to verify the model and observe its most sensitive parameters. A probabilistic sensitivity analysis was conducted to observe P10 and P90 values for clinical effectiveness. Results: The most sensitive driver of incremental effectiveness of LDD over surgery was, per the model, the potential of LDD to reduce systemic side effects. The potential clinical benefit of LDD over surgery was estimated, probabilistically, as between P10 and P90 values of 0.081 and 0.339 QALYs, respectively. Limitations of the model were discussed. A ‘utopia point’ was calculated. The relationship of the CEA to a total addressable market (TAM) calculation was discussed. The CEA modeling process enhanced learning about the problem and solution spaces. Conclusions: Despite its limitations, CEA modeling can enhance steering activities for emerging medical technologies. Insights from CEA may also help to assess trade-offs in capabilities and cost, as well as observe trends in clinical performance.
MIT Department
System Design and Management Program.
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