As we’ve written about in previous posts, an honest assessment of how truly innovative a solution may be is critical to success. Everything from the skills needed to sell it to the role of marketing vs sales in that commercialization strategy are effected by the novelty of the solution. But here we focus on another consideration in the go-to-market strategy that may be effected by that novelty; validation data.
Governed by legal and regulatory controls, pharmaceutical and medical device manufacturers are accustomed to budgeting for, staffing for, and undertaking significant clinical trial efforts as they pursue regulatory clearances or licenses in order to be able to sell their products. These trials or studies are frequently peer-reviewed for validity, and then presented at professional conferences and in journals adding to their validity. Regulatory agencies such as USFDA or their similar agencies in other countries tend to focus on the safety of these products, and the legitimacy of the manufacturer’s claims of their clinical impact. In fact, after demonstrating safety in their studies a significant part of the regulatory approval process is a review of the marketing materials to be used by the distributors of these products and the claims therein as being supported by the clinical studies.
Given these regulatory controls, customers tend to rely-upon those claims and the sheer trust that if the device or drug is being sold, it is safe and efficacious relative to the claims “on the label” or in the marketing materials. For example, if the marketing materials for a drug indicate that it “has been shown to reduce blood pressure by up to 20 mm Hg” it is generally safe to assume this outcome has been demonstrated with a statistically significant level of confidence.
But as we move into digital solutions and apps, the vast majority of the solutions in the market are not obligated to these regulatory constraints. In fact, “digital health” is a bit of the “wild west” as we write this. The USFDA and other agencies around the world are aggressively working to establish and refine regulations around these solutions. For example, in the US to technically (i.e., be in alignment with regulations) for an application to be considered a “Digital Therapeutic” (DTx) it is to go through the regulatory process with the FDA as a Class II medical device. This starts to fall under the rubric of “Software as a Medical Device” or “SaMD.”
As a Class II medical device, the application must go through the same clinical studies and manufacturing rigor as a physical device like a cardiac monitor or MRI machine. This is a significant commitment on the part of the manufacturer as its not just an obligation to do the initial clinical trials and data submission, but ongoing audits of the design and manufacturing (software writing and QA) processes. And if enough “evolution” of the application ensues over time, additional clinical trials may be required, repeating the process.
Why would an digital health application developer do this? To be able to make it easier for the customer to accept the safety, efficacy, and performance of the device on face-value from the marketing claims. If a DTx solution for management of anxiety indicates in its label or marketing materials “Has been shown to reduce the frequency of acute anxiety by 20%” the customer can accept that as having been proven and scrutinized in clinical trials.
However, an application developer making such a claim today in most markets that has not gone through the rigor of regulatory validation can make the same claim. However, savvy buyers realize that the lack of an USFDA, Health Canada, MHRA (UK), TGA (Australia), etc. (as applicable) suggests the claim may not have been peer reviewed or statistically powerful enough to be true. And these customers may require additional studies.
Furthermore, even in the case of a licensed Class II medical device (DTx), there may not be studies demonstrating the operational or economic claims such as the ability to improve productivity of the physician or nursing staff. This is because again, the regulatory agencies are focused on validating safety and clinical efficacy of the solution, not the economic return-on-investment (ROI) of the device. This is true for non-regulated applications as well.
If the solution is truly novel there is unlikely to be economic data – optimally peer-reviewed – demonstrating how it has economically impacted its users. This means the vendor’s claims of financial savings or new revenue generation may not exist or at best, may be anecdotal. This creates a commercialization barrier as buyers are likely to be wary of the claims regarding financial impact, and they may require ‘pilots’ and ‘studies’ as the first step in buying process. This in turn prolongs the sales cycle, and reduces revenue until such time that there is a body of peer-reviewed, independent data demonstrating the financial ROI of the solution.
Adding to the commercialization challenge, while the application may have evidence of safety and clinical efficacy and there may be a growing body of economic impact evidence, unlike the clinical studies and data, where that economic evidence was collected matters. The ability for a drug to impact biochemistry likely transcends geographic borders as our biology is similar enough that the drug will perform the same on the majority of patients. But the economic impact is not as universal as we traverse public and private health care systems, universally covered populations, fee-for-service vs value-based care models, etc.
As such if the solution you are bringing to market is truly novel, your commercialization strategy should consider the need for supporting evidence both in terms of clinical claims being made as well as economic and return-on-investment claims. It may seem obvious that, for example, “using this app will help patients take their medications and thus avoid emergency room visits.” But in different markets the beneficiary of that ‘saved emergency room visit’ may be different. Furthermore, the operational effort to get patients to use the app may incur additional costs not currently in the hospital’s or insurer’s budget. How a hospital or insurer in the US is economically impacted by an application may change if the patient is a member of an Accountable Care Organization (ACO) vs. a commercially insured Fee-for-Service model; evidence from each model may be required in the commercialization process.
With or without regulatory clearances ala the USFDA or other agencies as appropriate, if the solution is truly novel has the organization budgeted time, money, and staff to initially do the studies customers will demand in order for them to feel confident in the ROI claims being made? Has the organization budgeted repeating these studies as it moves into new markets, defined as a change in the flow of funds through ‘the system?’ New markets may include going into a new country, a new customer type (payer vs. provider vs. government, etc.), or even a new insurance type (i.e. commercial vs. Medicare).
Has your commercialization model taken the need for this data into account?