Everyone who works in healthcare is currently sitting in the center of a turbulent convergence. The drive toward value-based care is demanding better outcomes for less cost, while consumer-driven care demands patient self-empowerment and a more collaborative relationship between patient and physician. In my latest book, The Big Unlock, I discuss how the Internet of Things (IoT) and a connected healthcare ecosystem serve as a beacon for stakeholders within the convergence to help them navigate their way to the goals of value-based care. But many still struggle to see value in this approach as the technologies involved continue to mature, and the standards and processes surrounding them continue to coalesce.
In this context, it is helpful to define what this connected IoT ecosystem of the future can look like through a specific example, breaking it into the components that will make it work.
Imagine a patient with type 1 diabetes spending a day with her family. Instead of finger-sticks, this patient wears a medical device—a small, personal sensor that updates glucose levels in real time. Predictive analytics compiles the data to identify clear trendlines, compares this to population health norms, and determines the level of control the current treatment plan is exerting over the condition. Later that evening, the patient looks at the trendlines on her chosen, mainstream smart device. She compares the data to both the past week and the past month, and sends a message to her physician to share how she feels she is doing. The physician looks up this same data, as part of the patient’s universal health record, while on the way to dinner. The physician responds to the patient and is pleased with the current treatment plan. The doctor notifies the practice management system to schedule a regular follow-up with the patient.
Now, let us look at what just happened in detail:
- A "smart" medical device (IoT) collects patient data 24 hours a day, seven days a week, 365 days a year to ensure a consistent level of health monitoring not possible with annual, bi-annual, or even monthly regular tests.
- This data is transmitted wirelessly to a patient health record that is accessible as part of a holistic ecosystem including the networks of the primary care physician, specialists, hospitals, and of course the patient’s mobile device.
- Cognitive computing and Artificial Intelligence (AI) are the engines behind the next generation of analytics that enable actionable decisions to be made based on the information provided.
- With universal, but secure and controlled access, physician, patient, and other clinicians can collaborate on the treatment plan without the need for a physical office appointment.
Now, let us look at what was accomplished during this single event:
- The physician now has the ability to track patient glucose levels continuously. That means dangerous spikes will no longer be missed, not only improving the overall health of the patient, but reducing the possibility of hospitalization or costly additional treatments.
- The physician also has a higher level of confidence in the diabetes control of their patient. This means fewer appointments, reducing the cost of care. But more importantly, the physician can focus on patients that are not under a proper level of control and focus preventive care efforts in a process stratified by risk, not by repetition according to a set formula.
- Patients feel like they have a dynamic level of control over their own health. By accessing their numbers regularly, patients have a strong motivation to improve. (It’s human nature—how many times have you looked at the arrival time on your GPS, and tried to find a better, more efficient route to beat that time?) This level of control increases the patient’s level of satisfaction overall, leading to a better physician/patient relationship, which can improve outcomes even further.
And there you have it. The goals of value-based care (better health outcomes, delivered more efficiently at a lower cost) and consumer-driven demands to take charge of their own health all met simultaneously. The convergence has just been successfully navigated.
Big Challenges Remain
So what roadblocks remain between this day-in-the-life of better care scenario and where we are today? We are closer than you might think, but challenges remain. The first is that we need to increase the quality of the sensors of the medical devices we’re using to record patient biometric data. Everything that moves forward in the patient record and the subsequent plan of care will be dependent on the quality of that data. But research suggests that the quality of improvement in these sensors is moving along well.
But the largest challenge is the need for true interoperability between all connected IoT devices and all systems connected to the patient care record. This does not mean simply building interfaces, but developing industry standards that enable data to flow freely across any quadrant of the spectrum of care.
Then there's the ROI question: Medtech, along with other parts of the healthcare ecosystem, is increasingly required to demonstrate the economics of new therapies as the industry shifts away from fee-for-service to an outcomes-based reimbursement model.
We also need a completely new, lean, dynamic and fully cohesive management theory on how to unify all of the inputs, end user, care, efficiency and cost goals into one successful working model. IoT and AI have struggled in the past in a general purpose usage setting. For these technologies to work effectively in a healthcare setting, the thinking surrounding them needs to rise to a level worthy of the potential benefits they offer.