Why True Population Health Is Still on the Horizon

More than 20 percent of healthcare industry CIOs say their top investment priority over the next three years is population health technology.

It’s easy to see why: According to the Centers for Disease Control, individuals coping with one or more chronic diseases account for half of the U.S. population but a staggering 86 percent of all healthcare expenditures. Diabetes alone accounts for $1 out of every $5 spent on healthcare.

As these figures swell, so too has the number of technology-enabled population health management programs. Valued at $20 billion in 2015, researchers anticipate the market will balloon to nearly $90 billion by 2025. Employers, private insurers and government-sponsored health programs are scrambling to care for their members with chronic diseases while the tech industry promises turnkey solutions.

The reality? The vast majority of the tech-driven disease management programs on the market today promulgate population health while delivering subpopulation health.

Many Programs Bank on Predicting the Unpredictable

Identification and stratification remain core tenets of disease management. As a result, only a fraction of a given population receives tangible support in managing chronic diseases.

Payers weigh a variety of factors when enrolling members in disease management programs, but even the most advanced predictive modeling programs rely heavily on little more than individual risk scores and medical claims history. Naturally, insurers enroll the highest-risk, highest-cost members with the hope — and often the promise from vendors — of dramatic cost savings. Precise figures are difficult to ascertain, but rough estimates indicate program penetration rates of less than 10 percent of any given chronic population.

While this may appease the C-suite and shareholders, people who use “population health” to describe programs reaching fewer than 1 in 10 people need to aim higher. Although risk and costs are great indicators of where problems have been, they are woefully insufficient in predicting where the problems will be. Even the best predictive modeling programs currently rely on historical adverse health events and expenditures, most of which took place months ago.

No one predicts their own emergency. Emergencies arise at any time and don’t always come from the most predictable sources: the highest-risk, highest-cost members. Real-time biometric data would allow complex models to identify someone on the verge of a costly medical emergency, regardless of risk category.

Device Ubiquity Expands the Pool for Population Health

Today’s programs miss preventable emergencies every day, but effective population health programs must reach every member. Payers often rebuff this notion, assuming an untenable capital investment. But device ubiquity — driven by massive venture capital investment and frenzied mergers and acquisitions — is rapidly driving down the cost of remote patient monitoring (RPM) technology, making the democratization of population health programs more achievable than ever.

Many connected home medical devices, such as blood glucose meters, blood pressure cuffs and pulse oximeters, rely on Wi-Fi access, which puts RPM out of reach for large portions of the U.S. population. However, as the ability to connect through cellular infrastructure expands, companies are pursuing new approaches to telehealth, remote monitoring and disease management.

For example, connected scale manufacturer BodyTrace is following the lead of glucometer manufacturers like Telcare and Livongo by relying on cellular connectivity to securely transmit biometric data.

At the same time, new devices are taking home health monitoring to the next level. IBM’s wearables hub, for example, connects and transmits biometric data from disparate sources. It sifts health data, such as blood pressure and temperature, from wearable devices and sends it to the IBM cloud, where the information is analyzed and passed along to doctors.

Emerging technologies like these expand the reach of population health programs, most notably to underserved populations in rural areas.

Challenging Assumptions About Disease Management

While investment and enrollment in disease management programs have skyrocketed in the last 20 years, the actual practice remains largely unchanged. Promising candidates are identified, opted in to a program, and assigned a health coach or disease specialist to help them better manage their disease. Coaches generally interact with patients by phone on predetermined intervals, once or twice a month on average, with conversations ranging from diet and exercise to medication adherence and coping mechanisms.  

Data suggests a significant return on investment in disease management, but we must do more to expand the reach. Chronic disease will sink healthcare systems around the globe unless we’re willing to upend the paradigm. We can’t afford to accept subpopulation health programs.

Technology can inform and aid healthcare, but it can’t, by itself, fix the massive problems we wrestle with in the complex and intensely personal business of healthcare. We must create real-time visibility into 100 percent of a given population. We must intervene on every dangerous biometric reading at the precise moment of need. We must get people the help they need exactly when they need it, at a fraction of the cost of a trip to the emergency room.

Moreover, we must challenge assumptions and risk failure. We must achieve true, total population health. It won’t be easy, but it will be worth it.