Generating economic evidence for digital health solutions
Economic evidence is arguably the most crucial type of evidence for digital health innovators. It is vital for decision makers to determine whether a digital health solution (DHS) can be funded through investment, adopted by patients and clinicians and reimbursed by payors. It is not currently a standard part of evidence generation in digital health. All major health systems are facing the converging pressures of growing demand for healthcare, increased costs and labour shortages. DHS can be part of the solution, but they need to provide value for money to cash-strapped health systems. Innovators must be able to demonstrate value for money. This white paper explores the need for economic evidence, challenges in generating this type of evidence and how to conduct economic evaluations using different methodologies.
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Summary of key messages:
Economic evidence is the most crucial type of evidence for digital health innovators. In a recent survey, 94% of investors said demonstrating value for money is either important or very important for the success of digital health companies.
This evidence is critical for a variety of stakeholders, including investors, payors, healthcare professionals and consumers. The onus is on digital health innovators to show the value for money for their solutions.
The reimbursement landscape for digital health solutions (DHS) is complex and evolving. Demonstrating value for money is paramount for innovators to ensure that their solution can be reimbursed.
The Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) in the United States, the Federal Institute for Drugs and Medical Devices (BfArM) in Germany, and the National Authority for Health (HAS) in France have published guidance to help innovators compile the evidence required to aid reimbursement.
NICE in the UK have published further guidance to advise on economic evaluation.
The value of early modelling is often not considered by companies. However, it can help them prioritise their evidence generation, support with funding and investment by showing potential cost-effectiveness and help optimise the most effective placement in the pathway/population for the technology.
Due to the complexity of DHS, it can be difficult to conduct economic evaluations. Challenges include a lack of comparators, differentiating comparators, population of interest (including channel shifting with the intervention), identifying unintended consequences and organisational impact.
Economic analysis helps to quantify the value proposition of DHS, the value for money arguments to support pricing and reimbursement decisions and the cost implications of implementing these solutions.
Different methodologies can be used to conduct economic evaluations. The most commonly used methodologies include cost-effectiveness analysis, cost benefit analysis and cost-comparison analysis.
It is prudent to consider whether economic analyses will still be relevant if there are post market changes to the DHS, or how potential post market changes could be incorporated into economic analysis. This is particularly relevant to AI-based solutions that may have algorithms updated after initial deployment.
