OP29 Lifecycle Assessment Of Machine Learning-Derived Early Warning System. An Early Economic Evaluation Of An Intraoperative Hypotension Prediction Index
نویسندگان
چکیده
Introduction An iterative, life-cycle approach to the evaluation of healthcare technologies requires that clinical and economic evidence is collected since initial stages diffusion. Nevertheless, early cost-effectiveness models are challenging mainly due difficulties in estimating model parameters faithfully characterizing parameter uncertainty. This especially true with AI-based diagnostics, where attribution effects on costs patient-relevant outcomes more challenging. Empirical applications early-models useful identify main challenges iterative modelling provide recommendations best-practices. Here, we reported a case study machine learning-derived hypotension predictive index (HPI), predicts onset intraoperative trigger corrective measures. Methods A hybrid decision-tree/Markov was developed comparing an HPI-based intervention protocol standard-of-care during gynecological procedures. short-term component populated using data from individual patients at one hospital Italy. historical control group also defined propensity score matching. Long-term consequences HPI were modelled secondary data. probabilistic version headroom used determine maximum achievable price based available evidence. Value Information analysis conducted contribute most overall uncertainty, optimal future designs. Extensive deterministic sensitivity analyses characterize uncertainty over HPI. Results The preliminary results suggest has potential improve patients’ generate efficiency gains by reducing events permanent complications, such as acute kidney injury. link between reduction rate or long-term quality life Conclusions Early valuable tool inform further product development requirements, but characterization transparency assumptions key.
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ژورنال
عنوان ژورنال: International Journal of Technology Assessment in Health Care
سال: 2022
ISSN: ['1471-6348', '0266-4623']
DOI: https://doi.org/10.1017/s0266462322000861