AI. Algorithms. Machine learning. Deep learning. Digital therapeutics. Each of these terms has become embedded in our health tech lexicon and in the narrative of nearly every company in the space. Why? Because they’re key to transforming healthcare.
Healthcare innovation is in a rut. Industry is stuck trying to innovate its way toward sustainability. Providers are stuck trying to create new care models that consistently produce better value. One critical problem remains in the way: Data—there’s simply too much for human beings to synthesize and exploit.
Data is a tidal wave crashing down over the main drivers of our healthcare system: Physicians. Physicians are our chief data synthesizers, but the systems they’re using are limited. They’re supposed to simplify workflow and decision-making, but they’re doing neither. And many physicians worry that these systems are putting pressure on them to perform at ever-higher levels. It’s no wonder that the Triple Aim has become the Quadruple Aim—now including clinician burnout.
Clinicians are being pressured to transform their clinical practice. They’re supposed to be the change agents. But the explosion of data is overwhelming. Clinicians are too overburdened to leverage it, and, oftentimes, they don’t even know how to interpret it.
Data Is Now Just a Commodity
Over the last 15 years, hospitals and physicians have all digitized their practices. Data is interoperable enough to allow data transfer, and health systems now have massive data warehouses. We’ve conquered the digitization of data to such a degree that data has become a commodity.
The devices that create all this data are becoming commodities, too. Health tech companies that manufacture devices that simply generate enormous amounts of data are seeing their margins erode. In most cases, buyers are treating them like commodities, selecting products with the lowest prices.
A vice president at healthcare IT firm Cerner recently explained it to me this way: “The value is not in creating the 1s and zeros. The value is being able to do truly transformative things with the 1s and zeros.”
So where’s the value? The value in our emerging value-based healthcare system is in insights that enable timely interventions and produce better outcomes. Companies that can show that their solutions reduce the cost of care are the ones creating value. They’re the ones who will see healthy margins and strong revenues.
How do we generate insights with all this data? This is where AI, algorithms, machine learning, deep learning, and digital therapeutics come in. They handle datasets that are simply too large for humans to synthesize. And they not only provide insights and suggest interventions, they do it much faster and more efficiently than humans. They don’t sleep. They don’t tire. And they don’t burn out.
Bottom Line: Health Tech Companies Need to Become Software Companies
Health tech companies see the writing on the wall. To avoid commoditization, they have to create actionable insights that provide measurably better outcomes. They have to become software companies. For example:
- Orthopedic implant companies. They need to identify the right implant for the right patient and predict the patient’s long-term outcome. In this scenario, the implant is a commodity and the AI-enabled outcome becomes the differentiator.
- Lab companies. They’re realizing that their value lies not in quality results or system uptime—those are now commodities. To drive actionable insights, they need to become informaticists that use software to predict who’s at risk of developing disease. They also need to optimize test utilization and power more precise, less wasteful care.
- Imaging companies. They’ve always had software solutions, but these are limited to workflow. The industry must develop the AI tools to automate reads and redefine imaging practices. These solutions won’t replace radiologists, but they will empower them to support value-based care in novel ways.
- Critical care companies. If any space is on data-and-alarm overload, it’s critical care. These data-rich, insight-poor disciplines need help matching patients with the right treatment algorithms, and AI can do that.
- Home healthcare companies. They’ve successfully built sleep apnea and non-invasive ventilation systems that create lots of data. But for home healthcare providers to get paid by Medicare, they have to demonstrate positive outcomes for hundreds, if not thousands, of patients at a time. Software is the key. Health tech companies that provide SaaS models will be strongly positioned to close the gaps that lead to costly interventions and provide cost-effective, longitudinal care instead.
There Will Be Winners and Losers
The race is on. Health tech companies have to create greater value by deploying smart software with the power to transform clinical practice. The winners will be the ones that avoid four big mistakes and redefine their brand value:
Mistake #1: Waiting too long
When entire product categories are redefined by new technologies and evolving customer needs, first-mover advantage is critical. First-movers can create the narrative and take ownership over it. They can learn from their experiences sooner, and optimize. They can even have a whole new category to themselves and become synonymous with it.
Mistake #2: Remaining device-first, software-second
The winners will see that they need to become software companies first, and device manufacturers second. They’ll see that the device is a commoditized widget, but that the software powering the AI isn’t. Those with vision will flip their orientation and lead the market forward.
Mistake #3: Not revamping company culture
Becoming a software company is much more than shifting focus. It is about shifting identify and culture. To encourage change, companies need to allow the culture to become more data- and analytics-centric and less hardware-centric. This means promoting these values internally and externally, and promoting the individuals leading the change.
Mistake #4: In-sourcing the software development
Device companies struggle with software. They don’t instinctively think about what to do with data; they think about creating it. Their R&D teams lack software expertise beyond workflow-management tools. To deliver on the promise of software, they must either outsource product development or purchase a software category leader.
A Massive Shift Is Taking Place
In a value-based reimbursement model, it’s more critical than ever for device manufacturers to prove their value by quantifiably reducing costs and powering better outcomes. That’s why the most visionary companies are already shifting significant resources from creating hardware to creating software.
As manufacturers face increased pressure to help drive healthcare transformation, their first move must be to transform themselves.