Resource Modelling: The Missing Piece of the HTA Jigsaw?

Thokala, Praveen; Dixon, Simon; Jahn, Beate

doi:10.1007/s40273-014-0228-9

Cited by 16 publications

(14 citation statements)

References 48 publications

(32 reference statements)

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“…10 Taking an economic perspective, this means that if some patients do not receive a potentially cost-effective intervention, then the maximum total incremental societal health gain from introducing the intervention may not be achieved. 11 Capacity constraints may also have a significant impact on cost-effectiveness if they affect the cost or consequences produced by the intervention. This situation may occur if the constraint increases the cost of providing the intervention to patients or reduces the expected benefit for each patient.…”

Section: Highlightsmentioning

confidence: 99%

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Quantifying the Impact of Capacity Constraints in Economic Evaluations: An Application in Precision Medicine

2021

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Background Examples of precision medicine are complex interventions featuring both testing and treatment components. Because of this complexity, there are often barriers to the introduction of such interventions. Few economic evaluations attempt to determine the impact of these barriers on the cost-effectiveness of the intervention. This study presents a case study economic evaluation that illustrates how the value of implementation methods may be used to quantify the impact of capacity constraints in a decision-analytic model. Methods A baseline decision-analytic model-based economic evaluation of ALK mutation testing was reproduced from a published technology appraisal. Three constraints (commissioning awareness, localization of testing, and pathology laboratory capacity) were identified using qualitative interviews, parameterized, and incorporated into the model. Value of implementation methods were used alongside incremental cost-effectiveness ratios (ICERs) to quantify the impact on the cost-effectiveness and net monetary benefit (NMB) of each capacity constraint and from the 3 constraints combined. Results Each of the 3 capacity constraints resulted in a loss of NMB ranging from £7773 (0.1% of the total) per year for localized testing to £4,907,893 (77%) for a lack of awareness about commissioning ALK testing. When combined, the constraints resulted in a loss of NMB of £5,289,414 (83%). The localization and limited pathology capacity constraints slightly increased the ICER, but the lack of commissioning awareness constraint did not change the ICER. Conclusions Capacity constraints may have a significant impact on the NMB produced by examples of precision medicine. Value of implementation methods can be used to quantify the impact of such constraints by combining the impact of the constraints on the cost-effectiveness of the intervention with the impact on the number of patients receiving the intervention. Highlights While capacity constraints may prevent the use of precision medicine in clinical practice, economic evaluations rarely account for the impact of such barriers. This study demonstrates how constraints can be identified using qualitative methods and subsequently incorporated into decision-analytic models using quantitative value of implementation methods. In addition, this article demonstrates how value of implementation methods can be used to account for the impact of capacity constraints on the costs and benefits of an intervention as well as the number of patients receiving the intervention. In the case study presented herein, a capacity constraint reducing patient access to an example of precision medicine caused the biggest loss of net monetary benefit. Health economists should consider moving beyond incremental cost-effectiveness ratios to measures of total net monetary benefit to fully capture the impact of implementing precision medicine.

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Section: Highlightsmentioning

confidence: 99%

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confidence: 99%

Quantifying the Impact of Capacity Constraints in Economic Evaluations: An Application in Precision Medicine

2021

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“…Other resource allocation problems may be even more complicated. There may be significant and complex interactions between different investments; and there may be additional constraints to be considered, such as limits on the number of staff or bed capacity [8]. For example, consider the case of allocating resources for the prevention and cure of an infectious disease such as HIV, hepatitis C, tuberculosis, malaria, or polio [9,10].…”

Section: Overview Of Applications Of Constrained Optimization In Healmentioning

confidence: 99%

Application of Constrained Optimization Methods in Health Services Research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force

et al. 2018

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Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods.

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“…Yet this can often be jeopardized by the lack of attention given to resource demands [13]. Implementation as described in a CEA may not always be feasible, as this assumes that all physical resources (i.e., doctors, scanners, drugs) required by the new strategy are immediately available, regardless of actual supply constraints (or likely demand).…”

Section: Introductionmentioning

confidence: 99%

“…Implementation as described in a CEA may not always be feasible, as this assumes that all physical resources (i.e., doctors, scanners, drugs) required by the new strategy are immediately available, regardless of actual supply constraints (or likely demand). Ignoring these constraints may result in negative consequences, from low levels of implementation through to the technology not being implemented at all [13]. Resource modelling is a method that quantitatively captures the resource implications of implementing a new technology.…”

Section: Introductionmentioning

confidence: 99%

Cost-effectiveness and resource use of implementing MRI-guided NACT in ER-positive/HER2-negative breast cancers in The Netherlands

et al. 2016

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BackgroundResponse-guided neoadjuvant chemotherapy (RG-NACT) with magnetic resonance imaging (MRI) is effective in treating oestrogen receptor positive/human epidermal growth factor receptor-2 negative (ER-positive/HER2-negative) breast cancer. We estimated the expected cost-effectiveness and resources required for its implementation compared to conventional-NACT.MethodsA Markov model compared costs, quality-adjusted-life-years (QALYs) and costs/QALY of RG-NACT vs. conventional-NACT, from a hospital perspective over a 5-year time horizon. Health services required for and health outcomes of implementation were estimated via resource modelling analysis, considering a current (4 %) and a full (100 %) implementation scenario.ResultsRG-NACT was expected to be more effective and less costly than conventional NACT in both implementation scenarios, with 94 % (current) and 95 % (full) certainty, at a willingness to pay threshold of €20.000/QALY. Fully implementing RG-NACT in the Dutch target population of 6306 patients requires additional 5335 MRI examinations and an (absolute) increase in the number of MRI technologists, by 3.6 fte (full-time equivalent), and of breast radiologists, by 0.4 fte. On the other hand, it prevents 9 additional relapses, 143 cancer deaths, 23 congestive heart failure events and 2 myelodysplastic syndrome/acute myeloid leukaemia events.ConclusionConsidering cost-effectiveness, RG-NACT is expected to dominate conventional-NACT. While personnel capacity is likely to be sufficient for a full implementation scenario, MRI utilization needs to be intensified.

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Resource Modelling: The Missing Piece of the HTA Jigsaw?

Cited by 16 publications

References 48 publications

Quantifying the Impact of Capacity Constraints in Economic Evaluations: An Application in Precision Medicine

Quantifying the Impact of Capacity Constraints in Economic Evaluations: An Application in Precision Medicine

Application of Constrained Optimization Methods in Health Services Research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force

Cost-effectiveness and resource use of implementing MRI-guided NACT in ER-positive/HER2-negative breast cancers in The Netherlands

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