While digital health companies strive to address important healthcare issues by developing products and service offerings in various categories such as genome sequencing, analytics, telemedicine, mobile apps, and population health management tools, little evidence is available in the digital space when it comes to peer-reviewed publications, measurement of potential impact, and effect on patients with the greatest burden of disease, says Meridith Peratikos, Director of Enterprise Research, axialHealthcare.
The digital health industry continues to grow at an unprecedented rate. In 2018, venture funding for digital health companies approached a record $8.1 billion in funding, according to Rock Health. These companies, including axialHealthcare, are striving to address important healthcare issues by developing products and service offerings in various categories such as genome sequencing, analytics, telemedicine, mobile apps, and population health management tools—all with the promise of increased insights and patient engagement, and better care coordination that improves outcomes, costs, and access to care. These goals are crucial to moving the US healthcare system toward value-based reimbursement.
That said, little evidence is available in the digital space when it comes to peer-reviewed publications, measurement of potential impact, and effect on patients with the greatest burden of disease.
Analyzing the Evidence Behind Digital Health
Health Affairs recently published a study by Kyan Safavi, Simon C. Mathews, David W. Bates, E. Ray Dorsey, and Adam B. Cohen that explored this observation. After identifying 20 of the top-funded, private US-based digital health companies, they analyzed their products and services related to peer-reviewed evidence, potential impact on patients with high-burden conditions, and impact on cost of or access to care.
The study found that of the few studies on digital health services published in peer-reviewed literature, most evaluated their products in healthy patients rather than high-burden patients. Also, clinical effectiveness studies with a high level of evidence were uncommon. Moreover, no studies evaluated the effectiveness of theproducts or services in terms of reducing costs or improving access to care.
These findings may lead to the assumption that digital health products and services from leading companies have had a limited impact on disease burden and cost in the healthcare system, but there’s another side to that narrative. There are myriad reasons that little evidence exists on digital health’s impact, and we discuss the most significant barriers below.
But for the company that developed the product or service, that means spending substantial money to contract with a third party. This is something many startups don’t inherently have to spend for studies they would not own that could negatively impact their businesses.
A timeline of “years” can pose a major problem for companies that are constantly innovating. Products can evolve drastically over a short period of time, but a company undergoing a randomized study would have to put product adjustments on hold. axialHealthcare is a great example of this challenge. We’re continually assessing new scientific evidence in the pain and opioid space and if the findings indicate that we should make an adjustment to our clinical insight or analytical models, we implement the needed changes. This type of product adjustment may have to be put on hold during a study.
What can a digital health company do to prove value?
A more pragmatic approach to evaluating digital health products may be to conduct observational data analysis on routinely collected data—an approach that requires data collection, study design, and statistical analysis skills. While not as rigorous as randomized controlled studies, a moderate level of evidence is better than none at all. Additionally, the academic medical community agrees that observational data analysis is important for informing research findings and have developed guidelines called STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). If followed, STROBE allow for transparency and reproducibility of observational data analysis.
Given the long wait times for publication, digital health companies should consider the release of impact results as a white paper rather than in a peer-reviewed journal. White papers allow for quicker production of scientific knowledge and enable digital health companies to publicize results sooner. While white paper results are still met with much skepticism and would not have even been evaluated by Safavi et al, if the culture around releasing results could emphasize transparency and reproducibility, then perhaps peer-review becomes less imperative for digital health companies pursuing innovative ways to make an impact.