Just like clinicians who specialize in specific areas of medicine, technical problems in medicine require specialized solutions because the healthcare industry has no single general problem to solve, but rather many separate issues that need to be addressed.
To further complicate things, healthcare isn’t one industry, but many industries that fall under the same umbrella: clinical care, devices, diagnostics, pharmaceuticals, hospitals, payers, and more, each with their own unique challenges and opportunities that must be addressed with their own solutions.
It’s easy to oversimplify and say, “These big tech companies are now coming into healthcare and they’re going to solve every problem.” But the reality is, often the solutions aren’t going to come from the big tech companies.
Our health system is built on a complex set of requirements and regulations that traditional technology solutions cannot address. Patient data privacy, regulatory compliance, interoperability, and confidentiality of health information require specialized solution sets. The solution to a payment problem is not the same as the solution to problems specific to patient records, networking, telehealth, provider data, or medical conditions.
These individual problems are solved by large numbers of innovative people working in smaller, more focused organizations where they experiment, iterate, and pivot to get closer and closer to a solution to the problem they’re working on. These teams are focused on individual problems and solutions in a way that larger, more general technologies aren’t.
To compound the problem, the healthcare industry is constantly changing, and solution providers must be agile to keep up with emerging trends, new discoveries, new regulations, and shifting patient and provider preferences. These smaller, more specialized companies may not have the resources of larger technology companies, but they are inherently adept at quickly iterating on solutions, keeping up with change, and adapting to evolving needs.
This is why specialized solutions and specialized technology providers are ultimately the problem solvers.
Does this mean there’s no place for big tech companies? Of course not. Big tech companies can do what they do best: identify some of these solutions, validate them, incubate them, and ultimately scale the right ones.
But what about funding? These entrepreneurial companies developing innovative tools are often start-ups, and they often raise funding at the same time they build their solutions.
A recent Pitchbook report featured by MedCity News contained a variety of news about entrepreneurial companies in the medtech space. The report noted that venture capital funding into medtech bottomed out in the first quarter of this year and appears to be rising slightly, which is good news. The worrying news is that medtech funding this year may not reach 2022’s total of $13.5 billion, and it certainly won’t come close to 2021’s total of more than $19 billion.
The stakes are high in healthcare, and any technology solution must function as a “mission-critical” part of the equation. Think NASA or car safety. There’s no room for error or experimentation like there is when developing a ride-sharing or shopping app. We deal with people’s health and lives every day. Risks should be treated as a matter of life and death, because they are. And the solutions we deploy must be more than sufficient. They must be foolproof.
Photo: shylendrahoode, Getty Images
Co-founder and CTO, Dr. Bob Lindner, oversees Veda’s science and engineering team. He provides strategic vision, builds innovative technology, and connects Veda’s scientists with the Scientific Advisory Board.
Bob is passionate about data science and solving big problems. With over 10 years of experience, Bob is a published and highly-acclaimed astrophysicist with expertise in data modeling and designing and building cloud-based machine learning systems.
During his years of research and investigation, Bob has made many important discoveries and “firsts.” Most notably, he created machine learning code that helps scientists automate and accelerate analysis of data from the next generation of telescopes. This program, Gausspy, continues to advance scientists’ understanding of the origins of galaxies. Bob received his PhD in Physics from Rutgers University and led the development of Gausspy as a postdoctoral researcher at the University of Wisconsin-Madison.
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