Healthcare delivery in the United States is well, let’s say, complicated. It’s complicated due to the basics of medicine, biology, behavior, pharmacology, illness and the like. But it’s also complicated in its delivery, the systems of and access to care, opaque payment structures that disconnect consumer from the provider, and so on. Add to that technology and innovation and startups and things can get very messy very fast. “Users” of medical technologies aren’t solely patients, but also physicians and other healthcare providers, perhaps clinical trials, insurers, and regulatory bodies (whose rules can vary from state-to-state), all of which conspire to make sustainability, iteration and scaling an even tougher challenge for digital companies in this space.
Fool Me Once
Nevertheless, I have to say, I am continually amazed with the proverbial brave new world of tech and medicine and how it seems to have a Groundhog Day-like propensity to repeat over and over with just slight differences. And we still haven’t cracked the code for electronic medical/healthcare records to play well together or interoperate with other systems—practice management, outpatient with inpatient, registries, payers, etc.
Every time I think that this time it will be different when I hear about the latest gee-whiz announcement of something promising to be groundbreaking, paradigm-shifting or actually disruptive, I soon come away with a disappointing feeling of here we go again. I do have to admit my excitement with Atul Gawande’s new position as CEO of the Amazon-Berkshire-JPMorgan Chase (or “ABC”) healthcare partnership, as I have long been a fan of his writing and perspectives on healthcare and medical service provision—so time will tell—but I do like this mix of smart folks and solid funding.
Show Me the Money
Rock Health notes that funding in just US-based companies providing artificial intelligence (AI) and Machine Learning (ML) approaches to medical services have seen a 2010% increase in total investment between 2011 and 2017, to the tune of $98.4 million and via risk capital, aggregated investments are reaching $7 billion so far in 2018. CB Insights found that healthcare AI has more than 300 first equity rounds since 2016. Indeed, even non-per se medical companies are also heavily wading into the depths of healthcare and technology; between 2013 and 2017, Apple had filed 54 healthcare-related patents while Microsoft filed for 73, and Alphabet submitted a whopping 186 healthcare patents.
Not So Fast Unicorn
In spite of this level of growth in funding, not everything is butterflies and rainbows. In the world of digital health startups what leads to the almost certain death of the company is falling in love with the tech and then looking for a healthcare issue it can address. As Paul Yock wrote in Fast Company “Entrepreneurs and investors from the tech world mistakenly assume that (the) ‘lean startup’ approach, which works well for products like photo-sharing tools and meal-delivery apps, should be equally successful for tackling any kind of problem. However, this strategy is ill-suited to healthcare, a much more complex and regulated industry…For example, many founders coming from tech are focused on building and marketing products to consumers. They don’t realize until well into their company’s development that doctors and insurers are actually the gatekeepers and customers to whom they should be selling their products. This is why 61% of digital health companies that start B2C end up pivoting to B2B and selling to insurance companies, employers, hospitals, or other healthcare providers.”
Big Data and N=1
I have written about the Precision Medicine Initiative® (PMI) that former President Obama instituted during his tenure. Through advances in research, technology and policies that empower patients, the goal is to enable a new era of medicine in which researchers, providers, and patients work together to develop individualized care. It’s a bit “moonshot-ish,” which I like. It’s also very integrative, which I also like. Results may not be as immediate as anyone would prefer, but I think the sorting and sifting of the resulting big data will be aided by AI. I would like to see the combination of large patient outcome registries to be combined with the Federal findings in order to have the best of both worlds, or rather truly personalized medicine.
Many universities and professional guilds and even practice groups have perhaps become some of the best go-to entities to understand the real-world experiences of heterogeneous populations. For example, The University of Michigan plans to invest $100 million into a big data program. The University of Massachusetts Medical School developed the Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement and Quality Improvement (FORCE-TJR), a data system that guides total joint replacement practices. The North American Spine Society has a Spine Registry that is a “diagnosis-based clinical data registry that tracks patient care and outcomes.” The American Academy of Orthopedic Surgeons has developed the American Joint Replacement Registry. The American Association of Neurological Surgeons uses the Quality Outcomes Database to collect, analyze and report on nationwide clinical data from neurosurgical practices. And the list of registry developers is growing to include psychologists, physical therapists and other healthcare providers.
I’ve established one of these registries in our company that focuses on outpatient orthopedic rehabilitation cases. Each registry application goes through a vetting process at ClinicalTrials.gov and if approved, is added to Agency for Healthcare Research and Quality’s Registry of Patient Registries. Then clinicians and researchers have access to and can benefit from the clinical trials performed by other groups, or they have visibility into outcomes of certain interventions conducted in more “real-world” clinical settings. This also allows for research to be leveraged much more broadly than ever before and for clinicians and researchers to test hypotheses without incurring the time and expense of conducting primary research or doing their own data collection.
As the information that feeds AI and ML and deep learning (DL) comes from a number of often diverse places the internet of things (IoT), smart devices, wearables, apps and the like, all can produce feeds. In a report by IQVIA Institute, it was found that there were more than 318,000 health and wellness apps on the market, with a growth rate averaging about 200 a day. A promising relatively new aspect is that medical apps are increasingly being vetted via clinical trials, and IQVIA noted that there were about 860 such trials currently underway.
But clinical trials are not easy, inexpensive, or fast. Joseph Smith writing in STAT News said “One of the big promises of digital health is the speed with which it can transform health care delivery. The FDA has recognized that a different regulatory process is required for digital health solutions, signaling that regulation may be willing to move at the pace of innovation. But the full impact of digital health innovation is seen only with broad adoption of truly valuable solutions, and that rightfully requires evidence. Yet the best way to gather that evidence—the carefully conducted, prospective, randomized controlled trial and its subsequent publication—is currently ill-suited for the typical digital health startup.”
See? It’s Complicated (but Doable)
Medicine and healthcare has always been that way though, from the humors of Hippocratic medicine, to diagnosis via throwing bones, to leech selection for proper bleedings have been complicated and constantly (thank goodness) evolving. As noted by Yock “Taking what works in the tech sector and applying it to healthcare simply won’t cut it. As digital health continues to take off, success will be determined by getting the need right, designing innovative solutions that address stakeholders’ top priorities, and then demonstrating that a product provides better results…. Arlen Myers, president of the Society of Physician Entrepreneurs, echoes these concerns, indicating that many digital health startups fail because they ‘don’t involve end users early and often enough . . . don’t satisfy the needs of multiple stakeholders . . . make products that interfere with physician workflow instead of making it easier . . . [or] launch products that are not clinically validated.’”