Juniper Research forecasts that digital therapeutics revenue from health insurers will increase to US$8 billion by 2026, up from US$1.1 billion in 2022, growing 610% over the next four years.
Digital therapeutics are clinically validated software programs for the treatment of chronic medical conditions, either independently or in conjunction with other therapies. According to the Digital Therapeutics Alliance: "digital therapeutics are different from wellness apps or medication reminders in that they require rigorous clinical evidence to substantiate intended use and impact on disease state.
It facilitates the proactive mitigation of chronic medical conditions before they require costly interventions; enabling health insurers to reduce long-term costs per patient. However, it highlighted that these savings will be limited to health insurers in developed regions, where consumer devices and digitalised health infrastructure are ubiquitous.
This research, Digital Therapeutics & Wellness: Key Trends, Business Models & Market Forecasts 2022-2026, identified that insurers will also benefit from an ongoing shift among digital therapeutics vendors towards engagement- and results-based payments.
It recommends that therapeutics providers looking to leverage this trend prioritise the development of performance benchmarks, as demonstrating improvement and preventing patient abandonment will become a direct monetary issue.
Machine learning in an advisory role
The report forecasts that the number of people using digital therapeutics will increase by 381% over the next four years and recognises that machine learning will be key to this growth by facilitating advanced data analytics, remote patient monitoring, and real-time conversational coaching.
It cautioned that an ongoing lack of standards surrounding the use of machine learning within digital therapeutics will result in vendors limiting its role in their offerings.
Research author Adam Wears explained: “As developers and healthcare providers increasingly grapple with issues of liability and malpractice, machine learning will transition from a patient-facing role to a diagnostic tool offered through provider-facing dashboards; to be used by clinicians and specialists in a manner akin to traditional computer-aided diagnostics.”
