Timely Clinical and Economic Evaluation

Theme Leads:  Professor Deborah Stocken and Professor David Meads

 

Background

Innovators working in the HealthTech space, face knowledge barriers relating to the regulatory and evidentiary requirements of decision makers. They may not have access to relevant expertise to help characterise the value of their technology, nor the resources required to generate the evidence to build a value proposition.

Large, randomised trials in this setting – where innovations evolve quickly and there are many competing technologies – can be inappropriate, requiring alternative data sources and analytical approaches. Furthermore, the pathway to market is unclear and funders are faced with substantial risk in adopting technologies.

We have extensive experience in securing funding to apply early economic modelling across diagnostics, surgical devices, and digital and AI technologies. Early economic modelling allows identification of drivers of value and estimation of break-even thresholds such as maximum viable cost (headroom analysis) and minimum performance/efficacy parameters required to achieve savings/cost-effectiveness. Early modelling can inform pricing strategies, help identify the target population and inform product development, for example via target product profiles.

The pathway of evidence generation requires formal early assessment of technology efficacy. Our experience in the development and delivery of early phase platform studies, adopting umbrella protocols and a network meta-analysis approach can streamline prospective assessment of technologies efficiently utilising non-concurrent, shared control patients and matching techniques. Our experience in early phase statistical methodologies, combined with exploration of the role of statistical simulation, will support decision making in innovation.

Our generic discrete event simulation model (SimSurg I), models the flow of patients through diagnostic, admission, procedure and discharge processes. The model captures resources (staff and facilities), queue times and costs/outcomes at a system level. We will showcase the model via our portal and at workshops with SMEs and invite applications to adapt the model to evaluate their technology.

 

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Areas of Focus

Strategic planning of evidence generation and competitive funding bids, providing research methods, health economics, statistical and mixed methods advice and expertise in study design and analysing real-world data.

Work closely with our Surgical Care Observatory to undertake gap analyses and use expert elicitation to prioritize promising technologies.

Use expertise in early economic and budget impact modelling to establish the potential financial impact and value for money of these technologies.

 

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