Just like clinicians who specialize in an area of medicine, healthcare’s tech problems need specialized solutions. That’s because the industry doesn’t have a single general issue to solve, healthcare has many discrete issues to address.
To further complicate things, healthcare is not one industry but many industries under the same umbrella. Clinical care, devices, diagnostics, pharmaceuticals, hospitals, payers and more and each has its own unique challenges and opportunities that need to be addressed with unique solutions.
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It’s easy to oversimplify and say, “These big tech companies are now doing healthcare and they’re going to solve everything.” But the reality is that often, the solutions are not going to come from big tech.
Our healthcare system is built on a series of complex requirements and regulations that conventional technology solutions aren’t built for. Patient data privacy, regulatory compliance, interoperability, and the sensitivity of medical information call for a specialized set of solutions. A solution for a payment issue isn’t the same as a solution for patient records or network construction, telehealth, provider data, or a condition-specific issue.
These individual problems are being addressed by legions of innovative people working in smaller, more focused organizations where they are experimenting, iterating, pivoting, and getting closer and closer to solutions to the issue they’re addressing. These teams are focusing on singular issues and solutions in a way that bigger, more general tech doesn’t.
To compound the issue, healthcare is an ever-changing industry and requires solution providers to be agile in order to keep up with emerging trends, new discoveries, new regulations, and shifts in patient and provider preferences. These smaller more specialized companies may not have the resources of large tech enterprises; however, they are inherently more adept at quickly iterating solutions, responding to changes, and adapting to evolving needs.
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This is why specialized solutions and specialized tech providers are ultimately going to be the problem solvers.
Does this mean that big tech doesn’t have a place? Of course not. Big tech can do what big tech does best: identify, vet, and foster some of these solutions and ultimately scale the right ones.
But what about the funding? These entrepreneurial companies who are developing innovative tools are often start-ups and frequently raising capital at the same time they are building the solution.
A recent Pitchbook report covered by MedCity News included a mixed bag of news for these entrepreneurial companies in the medtech space. The report noted that venture capital funding to medtech appears to have bottomed out in the first quarter of this year and has been ticking slightly upward. That’s the good news. The troubling news is that this year’s medtech funding total may not reach the 2022 funding total of $13.5 billion and certainly won’t even approach the 2021 funding total of more than $19 billion.
In healthcare the stakes are high, and any tech solution needs to operate as a “mission-critical” part of the equation. Think NASA or car safety where there are no margins for error or experimentation like there are if we were building a ridesharing or shopping app. We’re dealing with people’s health and lives on a daily basis. The stakes should be treated as life or death because they are. And the solutions we deploy need to be more than adequate. They need to be infallible.
Photo: shylendrahoode, Getty Images
As co-founder and CTO, Dr. Bob Lindner oversees Veda’s scientific and engineering teams. He provides strategic vision, builds innovative technologies, and connects veda’s scientists to its Scientific Advisory Board.
Bob fell hard for data science and has a passion for solving big problems. With over ten years’ experience, he is a published and acclaimed astrophysicist with expertise in modeling data and designing and building cloud-based machine learning systems.
Bob has had a number of significant discoveries and “first detections” in his years researching and studying. Most notably, he created machine learning code that automates and accelerates the ability for scientists to analyze data from next-generation telescopes. That program, Gausspy, continues to increase scientists’ understanding of our galaxy’s origins. He earned his Ph.D. in physics from Rutgers University and was a postdoctoral researcher at UW-Madison, where he led the development of Gausspy.
