Sean Fenske: As it relates to the medical device manufacturing industry, what does Deloitte do?
Glenn Snyder: As the largest private professional services firm in the world, we offer an extensive array of services for our medical device manufacturing clients. At the broadest level, we help our clients compete and deliver value to their patients and shareholders by understanding nuances of the changing healthcare landscape, making choices about where to play and how to win, building capabilities to excel in the areas needed for operational excellence, and tactically engage their staff to execute with precision. Thanks to the breadth of our practice, we also help our clients understand new and impending regulations, and execute on the tax, compliance, quality, and regulatory strategies necessary to comply. For manufacturing, specifically, we help clients design and implement optimized supply chains, make objective insource/outsource decisions, rationalize existing footprint (often post M&A), streamline product development, enhance technical operations, and apply innovations such as IoT and advanced robotics to create a “digital supply chain.”
Fenske: What trends in the medical device industry are most interesting to you?
Snyder: Definitely the application of “exponential technologies” to medical technology…call it “digital health” or “mHealth.” Sensor technology is getting so affordable that many devices—even catheters—are likely to have sensors in the future. Couple that with augmented intelligence (AI) that can help physicians and clinicians interpret a complex array of biometric, genetic, demographic, and other inputs in order to make better/faster diagnoses and treatment decisions, and we are seeing the beginning of a transformation of the healthcare process. High tech plus medtech equals the possibility to truly bend the cost curve of healthcare while improving the consumer experience. That’s exciting!
Fenske: How might these technologies (digital health, artificial intelligence, and similar advancements) result in a simplification of what has become a very technologically sophisticated healthcare system?
Snyder: Our increase in knowledge of health and the body has grown tremendously over the last century, and, with that knowledge, so has complexity. Technology has allowed us to peer deep into the body, cells, genetics, proteomics, etc., but usually in a very siloed manner where physicians and clinicians struggle to piece the information together into actionable insights. Last year, approximately 250,000 clinical studies were conducted, many providing important empirical evidence that could alter the way healthcare is delivered. The problem is, no one human mind can absorb, understand, and recall all this information. Further, studies are finding the most appropriate patient diagnoses and treatment decisions can vary based on subtle variations of biometric and genetic (and other) indicators; these subtle variations are also very hard for a human brain to identify and differentiate. But for both of these challenges, a computer with well-designed algorithms (machine learning) can assist our human decision-makers. At the extreme, these capabilities are being “hard wired” into hand-held devices (see the Qualcomm X-Prize Tricorder project).
Fenske: Where is the digital health space headed? What do you expect to see in the next five to ten years?
Snyder: While everything seems to move a bit slower than we would think or even “like” in the healthcare industry, I believe the power of exponentials and consumer demand will drive rapid adoption of digital technologies in the medical technology space. We’ll continue to see a lot of transformation at the two ends of the spectrum—wellness and chronic care management—and will more slowly see application of these technologies to the more complex “diagnosis” and “treatment decision” areas of the patient journey. I believe consumers will increasingly have devices at home to enable AI-driven or at least remote diagnostics for simple conditions; this should help with our growing shortage of physicians in many developed and developing markets. We will also likely see significant improvements in “personalized medicine” by applying machine learning to areas like cancer diagnosis. The implications should be more certainty on treatments, better outcomes, higher consumer engagement, and lower cost. It will also likely reduce the absolute volume of tests—and even treatments—being prescribed thanks to earlier understanding of the root cause of disease, which is a threat to manufacturers’ growth. On the other hand, the “consumerism” of healthcare provides a huge new market for manufacturers should they pivot their business models to go after this space.