BACKGROUND: In recent years, the digital health landscape has rapidly evolved presenting new opportunities for the healthcare industry. Most countries lack a comprehensive reimbursement framework for digital products, hindering widespread adoption and accessibility.

OBJECTIVES: This analysis aims to explore the digital health landscape across the EU5 and US markets, examining current market access strategies and future reimbursement opportunities for digital products.

METHODS: Digital health landscape analysed for EU5 and US markets, reviewing reimbursement pathways, evidence requirements, recent developments and case studies. Qualitative analysis highlighted key landscape features between markets to identify trends in reimbursement.

RESULTS: As of June 2023, Germany has successfully reimbursed 47 DiGA over 20 disease areas, including Deprexis^® and Velibra^®. In 2018, Deprexis^® was assessed and recommended in the UK via the NICE IAPT programme, although Velibra^® was not following evaluation one year later. Deprexis^® was also recommended in France, with a notably similar evidence base to Germany and the UK, which included two randomised controlled trials (RCTs). Velibra^® has not been assessed. Spain, Italy and the US lack a standardised evaluation framework, and reimbursement decisions occur at the local level. To date, no digital products are reimbursed in Spain or Italy. In the US, digital products reSET^® and reSET-O^® failed to secure a widespread reimbursement contract despite an evidence base of over 40 studies including RCTs. However, Deprexis^® is reimbursed in the US under a 10-year contract.

CONCLUSIONS: Germany, France and the UK appear to be leading in digital product reimbursement, with standardised evaluation frameworks. This statement may be challenged though since digital products still receive reimbursement in markets without comprehensive frameworks, as highlighted by Deprexis^® in the US. However, it remains crucial that developers understand the importance of selecting the correct evidence generation method to minimise negative recommendations and adoption difficulties across markets in the future.