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Background: Chronic hepatitis C is a major public health burden. With new interferon-free direct-acting agents (showing sustained viral response rates of more than 98%), elimination of HCV seems feasible for the first time. However, as HCV infection often remains undiagnosed, screening is crucial for improving health outcomes of HCV-patients. Our aim was to assess the long-term cost-effectiveness of a nationwide screening strategy in Germany.
Methods: We used a Markov cohort model to simulate disease progression and examine long-term population outcomes, HCV associated costs and cost-effectiveness of HCV screening. The model divides the total population into three subpopulations: general population (GEP), people who inject drugs (PWID) and HIV-infected men who have sex with men (MSM), with total infection numbers being highest in GEP, but new infections occurring only in PWIDs and MSM. The model compares four alternative screening strategies (no/basic/advanced/total screening) differing in participation and treatment rates.
Results: Total number of HCV-infected patients declined from 275,000 in 2015 to between 125,000 (no screening) and 14,000 (total screening) in 2040. Similarly, lost quality adjusted life years (QALYs) were 320,000 QALYs lower, while costs were 2.4 billion EUR higher in total screening compared to no screening. While incremental cost-effectiveness ratio (ICER) increased sharply in GEP and MSM with more comprehensive strategies (30,000 EUR per QALY for total vs. advanced screening), ICER decreased in PWIDs (30 EUR per QALY for total vs. advanced screening).
Conclusions: Screening is key to have an efficient decline of the HCV-infected population in Germany. Recommendation for an overall population screening is to screen the total PWID subpopulation, and to apply less comprehensive advanced screening for MSM and GEP.
Highlights
• Artificial intelligence systems for mechanically ventilated patients are increasing.
• The clinical and financial impact of these models are often unexamined.
• We developed a generic health-economic model for artificial intelligence systems.
• This model assesses the cost-effectiveness for many different scenarios.
• The developed framework is easily adjustable to other (clinical) situations.
Abstract
Purpose: The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients.
Materials and methods: Our generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness.
Results: The generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings.
Conclusions: Using the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.