Untying the Gordian Knot of Payer Data
According to legend, the Gordian Knot was a puzzle comprised of many smaller, almost impossible to decipher knots. Anyone who could untie it was said to be destined to rule all of Asia. Enter Alexander the Great, and the rest is literally history.
To health payers trying to make sense of the seemingly endless mountain of incredibly rich data they currently own, untying a Gordian Knot must seem like child’s play. Especially since the data never stops growing.
According to America’s Health Insurance Plans (AHIP), regional health payers typically process $8 billion in claims each year. Every one of those claims contains a treasure trove of interesting data aggregated from across the healthcare spectrum, i.e., from providers, laboratories, imaging centers, pharmaceutical companies, etc.
The challenge is finding a way to untie the knots that keep it from being usable so they can gain a deeper view of member/patient health that enables them to improve health outcomes and reduce costs. It’s already a tall order. Then add demographic, psychographic, and social determinants of health (SDoH) into the analytics mix, as many payers are doing these days, and the task becomes even more complex. Still, if they can pull it off, payers can use all of that data to drive more reliable, and more actionable, conclusions.
Consulting the Seers
In ancient times, kings and emperors relied on seers to look into the future and help them rule their kingdoms. Payers today replace that mysticism with science, relying on predictive and prescriptive analytics to not only identify members/patients who are already in the high-risk category of health but also predict those who are trending toward it but have not fully acquired the condition yet.
It is becoming increasingly important for payers to identify this latter group in particular because they still have time to change that population’s destiny. In doing so, they can improve the population’s health while reducing their own benefit costs.
The fate of members/patients with diabetes offers a perfect example, especially since it is the seventh-leading cause of death in the U.S. The Centers for Disease Control and Prevention (CDC) says that more than 100 million adult Americans are now living with diabetes or pre-diabetes, which not only affects their quality of life (both clinically and financially) but can also lead to or complicate other chronic conditions as well.
Unlike many diseases or conditions, once diabetes is acquired it never goes away; it can only be controlled, which often requires a serious investment in clinical and financial resources. If, however, payers can untie the knot that shows which members/patients are trending toward acquiring diabetes, they can recommend steps to address the issues creating the risk and work with members/patients (and their providers) to alter their destiny.
Payers can also use that data to home in more precisely on the best approach to take depending on the member’s/patient’s circumstances. Prescribing an expensive medication to someone who lives in an underserved community is unlikely to drive compliance, particularly if the member/patient may be forced to choose between taking the medication as-prescribed and paying their rent. Understanding those SDoH factors will alert payers that less costly alternatives should be considered.
Changing the conditions
Alexander is said to have solved the Gordian Knot by creating his own alternative solution (including cutting the knot itself). Payers have that option as well by incorporating artificial intelligence (AI) into the analytics.
AI’s value is that it is able to work through all the complex, knotted data to discover subtle relationships between seemingly unrelated factors that a human might miss. For example, it could find that members/patients who say they own pets on their self-assessments tend to follow their plans of care more often than those who have not pets.
Payers can then help providers produce better health outcomes for members/patients by sharing that information, improving their quality of life while reducing costs.
Springing into action
The other great advantage payers have over providers is once these actionable insights have been created, payers have the financial resources to do something about them. At best, providers tend to only have enough care management and other resources to address the highest of their high-risk populations.
Mid-size regional payers and up, however, can usually afford to hire nurses or other clinicians to take what the predictive analytics have revealed and close the loop. These may be internal personnel, although many payers are now discovering the value of outsourcing this type of ground-level member/patient engagement to organizations that already have a complete infrastructure in place.
Either way, these payer-sponsored clinicians can contact members who are trending toward increased risk, monitor the adherence to plans of care across large populations, and work with social services providers to address any challenges created by SDoH. Smart payers will even have clinicians visit qualifying members/patients in their homes to gain a more complete view of factors that could affect compliance.
Conquering healthcare challenges
Having a wealth of data can be a double-edged sword. While more data seems better on the surface, having more than you can handle can be overwhelming. And like the Gordian Knot, the more you try to unravel it the more frustrating it can become.
AI-driven predictive and prescriptive analytics can help simplify the data puzzle in a way that enables solutions to each challenge to present themselves. By knowing not only what to do but also having the resources ready to do it payers can raise healthcare quality and member/patient satisfaction while significantly reducing benefit costs.