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Creating a digital diagnostic that is supplemental and strategic to a company’s therapeutic asset requires due diligence to monetize the digital health innovation, or at least ensure optionality through the product development cycle.
Pharmaceutical companies interested in joining the global digital health revolution are increasingly facing tough decisions on how to leverage the innovative technology. Is it better to provide a low-risk digital tool that, say, enhances the patient experience, or to make a long-term investment in a digital technology, such as developing a diagnostic tool with the potential to help improve clinical outcomes? While the answer depends greatly on patient needs and business model, here are some important considerations for finding success in the digital diagnostic space.
Software functions1 that meet the definition of a device, or software as a medical device (SaMD), may be deployed on mobile platforms, other general-purpose computing platforms, or in the function or control of a hardware device. Digital technologies that fall under U.S. Food and Drug Administration (FDA) regulation include:2
The FDA regulates based on risk, so companies often believe the best bet is to build a Class I digital tool to avoid going through the much more rigorous Class II or Class III clearance and approval processes. However, going the Class I route usually means launching a digital tool that has limited functionality to address a treatment purpose or solve serious health care needs. It also generally will have limited opportunity for differentiation since consumers already can choose from hundreds of thousands of simple health-related apps. In comparison, investing in a higher-risk digital diagnostic product with proven clinical utility may provide clinicians with a more impactful tool and pharma companies with a path to monetization.
Fundamentally, digital entails pursing one of the following two core revenue models:
Many companies lead with the first option, but often inadvertently end up executing a mishmash of both. This can arise when there’s a lack of clarity on the business case or a material change in the companion therapeutic (FDA approval, clinical data, etc.) For example, a variety of pharmaceutical companies have invested in companion digital diagnostics, only to see the lead asset fail later on. In such instances, many companies let inertia carry the digital product forward without a reevaluation of the investment thesis and business model. Establishing a standalone revenue stream for a digital product requires a drastically different investment thesis, level of risk, and time horizon. In this case, the investment versus risk ratio might signal the need for a different strategy — including partnering to out-license the product or sunsetting the idea all together.
When developing a digital product, pharmaceutical companies should consider both direct and indirect revenue pathways early in the product development process, particularly when entering new markets or seeking FDA approval for companion therapeutics. While regulatory clearance is more efficient than approval for a traditional drug, digital products can face significant post-market adoption barriers, as well as commercial and reimbursement risks. Pivoting from an indirect to direct revenue model can be challenging and may require a complete reset on product design and features, regulatory strategy, and clinical planning. By mapping out both direct and indirect revenue pathways, companies can be in a better position to evaluate product development tradeoffs, and make strategic decisions with a clear end goal.
To fully understand your best option, conduct a strategic analysis of each pathway with consideration for your company’s market opportunities, positioning, assets, and risk tolerance. Consider the market need and what kind of digital diagnostic will best fulfill that need, and if a path exists in which you gain a potentially larger return on that investment, such as by pursuing a reimbursable diagnostic tool. Also, keep in mind, the results of a strategic analysis might reveal your best option is to not proceed. The time, opportunity cost, investment (clinical and otherwise), and risk — with no guarantee of revenue — might be too much.
Some companies aim to leverage artificial intelligence (AI) technologies to automate clinical labor. As physicians are typically the end customer, the prospect of automating their work is sensitive, and is likely to introduce additional adoption barriers, commercialization hurdles and legal considerations. In contrast, it stands to reason that a digital health product that supplements clinical decision-making, resulting in improved utility and outcomes, will better gain traction (and increase the odds of reimbursement). As you carve your digital diagnostic strategy, consider how the product can help enhance a physician’s practice and/or improve clinical outcomes.
Traditionally, software developers are accustomed to working agilely through development sprints, aiming to get a minimum viable product (MVP) to market as quickly as possible, and knowing they can make adjustments, fix bugs and implement upgrades in later versions as needed. While developers typically verify and validate these products along the way, the level of rigor required is usually far less intense than for a high-risk Class II or Class III medical product that directly impacts patient lives.
Making your way through the stringent regulatory approval process of the FDA requires both a deep understanding of compliance as well as personal patience. This means following regulatory design controls: keeping detailed documentation along the development path, as well as throughout the rigorous testing, evaluation and validation processes throughout the lifecycle of a product to obsolescence. And, while products can be updated and improved post-approval, the initial FDA-cleared version must be proven to fulfill its clinical purpose and perform at an exceptionally high standard.
In addition, while all machine learning (ML) model developers should be performing some form of model validation, the threshold for due diligence is much higher for medical products. If validation is done improperly, the clinical trial results will reveal that it does not work reliably in a real-world setting, and you will be back to research and development. In technical terms, this means employing best-practice validation, such as nested cross-validation, that is appropriate given the sample size, patient, and disease characteristics. It also means throughout the validation process avoiding “target leakage,” i.e., subtle validation errors that will make a model appear to be effective during the development process but ultimately fail when deployed in the real-world. This is critical in order for the clinical trial results to provide evidence as expected.
What all this comes down to is matching the digital health tool strategy to your business case and the market opportunity. For many companies, this will mean creating a digital diagnostic that is supplemental and strategic to a therapeutic asset. But without doing the due diligence to ascertain your best option, you could be missing out on opportunities to monetize the digital health innovation, or at least ensure optionality through the product development cycle. To introduce digital health technology of value, it is essential to consider and understand the needs of your target market and therapeutic landscape. This includes understanding the physician point of view, incentives, and barriers to adoption. It also means considering building a digital diagnostic that genuinely adds value for the patients and clinicians who rely on you for treatment options — even if it means a more intense development process and undergoing regulatory approval.
Bill Woywod is an associate director in Health; Jim Williams is an associate director in Life Sciences; and Jacob Graham is a partner in the Life Sciences practice, all at Guidehouse.