Payers are the real customers for healthcare technology, and they care about cost.
It’s well understood that having great technology is an essential starting point for any tech company. But the companies that ultimately succeed will have also identified and executed a business model on which to achieve strong growth. In healthcare that means having a deep understanding of industry and market dynamics. Who are the buyers, as opposed to the users or influencers? An individual may really love an app for helping controlling blood sugar but she will very likely not be your customer.
In fact, most healthcare today is purchased by either private employers or the Federal Government (Medicare, Medicaid, and individual subsidies through Affordable Care Act plans)—these entities are called “payers.” Health insurance plans either facilitate payment for self-insured employers (think: Apple or IBM) or they assume financial risk when working with government or employers (the “purchasers”).
What do “payers” want to see when considering whether to purchase a digital health app? The most important question to answer: can this reduce the costs of care? And, can it do so demonstrably within a few years? Over the next ten years, the Kaiser Foundation estimates that healthcare spending will increase at about five percent each year.. While this is not the double digit increases experienced in 1970s, it still remains significant. In addition, since payers typically do not cover an individual for more than two years at a time, their time horizon is short. Every time a person changes jobs, she changes health plans, and therefore her health care costs becomes someone else’s problem.
Bending the cost curve is largely about targeting high-intensity interventions for the 5% of people who account for over 50% of healthcare costs.
So, if costs are the problem, health tech companies need to show that their technology or app can “bend the cost curve”—health policy speak for reducing the increase in costs over time. This holds true for consumer tools, digital health devices, new drugs, or telemedicine.
Consider telehealth. The employee who feels sick and run down late at night could go to the emergency department for an evaluation that cost thousands of dollars or pay less than one hundred dollars to talk to a doctor via the web. That represents a healthy amount of savings (no pun intended).
Since studies have demonstrated that over half of all healthcare costs result from caring for the sickest five percent of a population, the first step is to identify which group of people in a population are likely to cost the most (defined as high risk) and what can be done to modify (or lower) those costs.
Risk identification analytics allow organizations to single out the highest-cost patients, and deploy technology to address them.
There is an area of analytics which attempts to predict the likelihood (or risk) that an individual will incur high healthcare costs which could be modified with the right intervention: risk identification analytics.
By predicting risk of future costs, healthcare tech-enabled interventions can be better targeted and therefore can show greater gains for the payer. Prediction of future costs can be complicated to determine since an individual can experience an accident or some other unfortunate or unforeseen event. But, a critical first step in prediction is profiling an individual—which includes enumerating chronic diseases, medications, prior procedures, visit history, lab results and so on – and figuring out what aspects are associated with future modifiable costs.
With the right risk analytics coupled with the digital health intervention, a solid case can be made to a payer as to how and when to use the app or technology, and what the results will be.
Let’s consider a Bluetooth-connected scale for monitoring weight. Weight loss, especially in those who are morbidly obese, can improve control of other chronic diseases such as high pressure and diabetes, and lower the likelihood of developing heart disease, stroke or kidney disease (complications of poorly treated diabetes and high blood pressure). These are all laudable goals and can result in lower costs over a relative longer time horizon.
What will really catch the attention of the payer is coupling this high-tech scale with a care management program to avoid very expensive hospitalizations within a few months of use. Consider patients identified as having moderate or severe heart disease with a history of recent hospitalizations for worsening disease. Definitely a group with a high cost future which can be modified.
When the heart does not pump properly, fluid pools in the legs and lungs making it hard to walk and breath. A hospital stay for an acute worsening of heart failure can cost upwards of $23,000 or more. A connected scale coupled with algorithms which interpret the data and determine when weight increases more than five pounds over a three day period could be a signal of potential trouble ahead. If this occurs, the care team can be notified and then a discussion could ensue with the patient about whether a medication dosage change is needed to remove more body fluid. By targeting brittle heart failure patients and providing a bluetooth scale and connected care team, hospital visits can be avoided in this risky group.
This is not to say that payers will ignore other benefits of an application—improved individual health, engagement, satisfaction, etc. But i which ultimately makes the sale. Risk analytics and the ability to effectively target the right population with a technology will demonstrate the kinds of returns on investment which can attract high demand from payers. And this is the key to a successful healthcare technology company.