Promise vs. Reality – Will Healthcare Delivery Evolve?

While adaptability is necessary in any industry, the rate of change varies widely. For example, compare the toy industry, where “what’s hot” changes with the seasons, to the pharmaceutical industry, where new therapies may take decades to materialize (e.g. CAR-T therapy). Digital health solutions exist in a space where information technology (pace of change measured in months), electronics (pace of change measured in years), and healthcare (pace of change measured in half-decades) industries collide. They have struggled for adoption amid a lack of acceptance among care providers or reimbursement from payors.

While COVID-19 is not the catalyst anyone wanted, we have seen the pandemic spotlight the value of the “remoteness” of digital health solutions. Patients, providers, and payors are by necessity learning what these tools make possible. These are heady days for telemedicine where the future again looks bright and change is on the horizon.  

But when the catalyst is gone, will these changes take root and grow or wither back to their prior state of seeking sponsors and reimbursement? And as the world is hopeful that COVID-19 will be eliminated over the coming months, how can digital health solution providers ensure that the story has a happy ending?

I believe the answer is the same as in pre-coronavirus times, and offer these insights:

1. Build an Economic Model

Prior to COVID-19, digital health tools suffered from a distorted perception of value. While most stakeholders will agree that the tools are valuable, the value of convenience is not worth double the price. When investing in the technology, software, or solution that you intend to build/refine, the first step should be to understand the current economic model for that condition/ailment/disease/treatment. What costs will you add and what costs will you take away?  If your initial model does not show a decline in costs or at worst neutral cost, it is an uphill battle to remain relevant once the catalyst is removed.

2. Focus on the Users, not the Customers 

This is an important distinction. Many business plans lay out clearly how they will collect and monetize data streams to earn returns for investors. Fewer have a plan that is inwardly critical and realistic when it comes to user adoption and retention. Why will users value your technology and what will keep it relevant?  This is critical since the quality of the data drives most of the value and most of the solutions. Focus on meeting a need, continue to meet that need and offer incremental value.

3. Build Bridges to Data

As the COVID-19 pandemic has illustrated, we live in an increasingly linked society. Information islands are counter-productive and the value of digital health tools is in connecting the dots to see the larger picture.  This has public health implications and also directly impacts the users we are targeting. How do we bring the “brown bag of medications” philosophy (addressing risks to patients from polypharmacy) to digital solutions? 

The solutions here are less straightforward. Many have tried/are trying to bridge the information islands we have created, but no single winner has emerged. Frustratingly, taxpayers in the United States paid to electrify our medical records and standardize the coding of conditions and ailments, but now those data are being monetized and prized as commercial currency and are not freely available. The restriction to the knowledge base that “we the people” built and populated is a huge limiting factor, and the bridges between these information islands are more accurately represented as tollways.

Americans were promised predictive analytics and a total patient picture when funding the digital EMR revolution, but negotiations are hard and ongoing. Solutions are more likely to be legislative in the long term. In the near term, successful business models must account for the tolls to be paid to the keepers of these data troves.

4. Build to Last

Focus on creating assets of lasting value that build the users’ confidence in the data collected and provided. Going back to the toy example, we no longer build toys to last for generations. Likewise, early entrants in digital health expected a consumer electronics pace of change, and while some were rewarded with short term profits, most were not built to last. As we have seen with COVID-19 infection testing and again with antibody testing, the first solutions launched were not necessarily the best. Many offered short term impact and have faded away.

Those solutions launched with strong supporting data will win even if the technology is more mature; the mature technologies may even be preferred for their perceived safety. Innovators in digital health should subject their technologies to a market validation trial to confirm their utility and ability to impact behavior and outcomes.

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At Battelle, we have helped our customers overcome the challenges common to digital health for over a decade. Whether ensuring the security of OTA updates, linking massive data sets for purposes other than their intended function, or generating predictive insights to drive the value that is missing in healthcare, our team at Battelle has likely been down the path you are on.

Battelle’s mission calls us to advance innovation for the benefit of humankind. If you would like to learn more about our efforts, please connect with us to start a conversation. 

About the Author
Andre LaFreniere has been a trusted advisor to medical technology developers for more than fifteen (15) years. He has worked extensively with technologies that enable patient empowerment and self-directed care related to medication delivery, treatment adherence and point of care diagnostics. His knowledge base has benefitted leading R&D organizations to design, develop and commercialize dozens of successful diagnostic, medical, and pharmaceutical innovations.