Reversing Prediabetes with Analytics and Collaboration

February 05, 2019 - When we first met Lucy*, she – like the overwhelming majority with her condition – didn’t know she was prediabetic. Nor did her physician, health plan or health system. But they did have reason to suspect her condition.

Unlike the 75 million prediabetics who don’t know about their condition, and likely aren’t engaged in the kind of diet and exercise lifestyle changes needed to prevent progressing to diabetes, Lucy is one of the lucky ones.

Today she knows she’s prediabetic and is motivated and engaged in her health. The personalized, individualized care she receives from her physician and care coordinator has made all the difference.

Earlier Identification and Engagement of Rising-Risk Patients

Over the past few years, Lucy’s health plan had become concerned about the prevalence of diabetes and prediabetes among its member population. That’s one of the primary reasons the health plan’s care coordinators and the quality leaders charged with improving the plan’s HEDIS^® and Medicare Stars performance are using a shared analytics and insights platform.

Increasingly, the health plan is focused on using analytics to identify and more effectively engage patients whose risk score is rising – like prediabetics – and involving them in their health earlier to prevent chronic illness and the adverse health effects and costs that accompany chronic disease.

READ MORE: Medicare Diabetes Prevention Program May Have Cost Concerns

The plan’s HEDIS^® director, Jaime, has used the analytics platform to identify subpopulations of members with gaps in care, many of whom also are the plan’s rising-risk patients. For example, she reviewed BMI trends to generate lists of members at-risk for or with a metabolic syndrome diagnosis, which were then shared with the plan’s value-based care practices.

Prediabetes Suspicion Confirmed

That’s why Lucy’s physician, Dr. Becker, suspected she was prediabetic. As a part of pre-visit planning, Dr. Becker viewed Lucy’s patient profile within the shared analytics platform and saw she was on the health plan’s list of members at-risk for metabolic syndrome. He ordered the fasting blood glucose test that showed Lucy, in fact, was prediabetic.

Lucy learned of her prediabetes diagnosis during her visit with Dr. Becker. He explained the seriousness of her condition to her as well as the great potential to reverse it with lifestyle changes. Hearing about her diagnosis and the steps needed to manage her health directly from her physician motivated Lucy.

As the result of Lucy’s prediabetes diagnosis, her health plan assigned a care coordinator to work with her to create a personalized, achievable care plan to improve her diet and activity-level. Not long ago, health plans reserved care coordinators for the sickest of the sick, but increasingly, health plans like Lucy’s are using care coordinators to engage rising-risk patients like prediabetics.

Before her care coordinator, Judy, contacted Lucy, she was able to learn more about her by accessing her patient profile within the analytics platform. There, Judy read Lucy is the mother of two teenagers, a caregiver to her elderly father, and a full-time employee at Lightning Laser, a company whose human resources department also uses the shared analytics platform to improve employee health and well-being.

READ MORE: Quality Analytics, Risk Adjustment Tools Prep Payers for Success

Together, Lucy and Judy designed a care plan to fit within the demands of her personal and professional life. Three key parts of Lucy’s care plan were:

1. Meeting with the health plan’s nutritionist to learn how to quickly prepare healthy meals,
2. Regularly submitting her BMI via a Bluetooth-enabled scale in her home and
3. Having an annual fasting blood glucose test.

Collaboration and Coordination

In the time since Lucy’s diagnosis, all of the professionals with an interest in Lucy’s health – Dr. Becker and his team as well as her health plan care coordinator and nutritionist – have used the analytics platform to monitor her progress on her care plan. They each have real-time insight into how the members of her team are working with her towards their collective goal of personalized care and improved health.

And now the good news.

It hasn’t always been easy, but today, Lucy is feeling pretty good. She exercises regularly, cooks and enjoys healthy meals, tracks progress on her weight loss goal, and her annual fasting blood glucose test shows her prediabetes has been reversed.

Data and analytics, which helped identify Lucy earlier, and collaboration among her health plan and care team, made possible through the collective use of the analytics platform, means Lucy has received targeted, personalized and coordinated support on every step of her journey to better health. Lucy is indeed one of the lucky ones.

READ MORE: Preventing Provider Fraud through Health IT, Data Analytics

*Lucy is fictional and not intended to represent any specific person. This information is provided for illustrative purposes only.

Author: Heather Lavoie, Geneia President, www.Geneia.com