How to Use Big Data for Tailored Population Health Management

Let’s travel to the very near future of population health management. The hypothetical ABC Health Plan recently got a new member: Selma, a 57-year-old woman with heart failure.

To help Selma manage her condition, improve her quality of life, and prevent unnecessary and costly hospitalizations, the health plan assigns her to a care management program.  

Historically, a care manager might have spent days tracking down the correct phone number for Selma. Three phone messages later, Selma might have called back. But then Selma learned she had to take a 45-minute comprehensive intake questionnaire about her medical history, home life, economic status, etc.

Not surprisingly, Selma made an excuse and hung up.  

In the near future, a more targeted, problem-specificapproach to care managementwill be used. A predictive model using big data analytics will flag Selma as a high-risk patient with medication adherence problems.

The care manager will see that Selma is two weeks late in filling her prescriptions, based on pharmacy claims data. Plus, Selma has gained 4 pounds in a week, which is discovered from data retrieved from Selma’sdigitally connected scale. This is a warning sign that her heart failure is out of control.

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After exchanging a few texts, which is Selma’s preferred communication mode, the care manager learns that Selma’s car broke down. The care manager then quickly solves the problem by arranging for Selma’s medicines to be delivered to her.

Brief, targeted, and successful patient engagement scenarios like this will be possible thanks to the growing availability of various types of big data.

New insights will be obtained from digital health monitors, social media, electronic health records, and other sources. Predictive models are also becoming more accurate in pinpointing how to best allocate scarce resources for care management.

Given these advancements, we foresee a five-step model that will help health plans improve engagement with members around key issues impacting their health.  

Much has been learned in recent decades about which care management interventions tend to bring the biggest benefits—for patients and payers. By focusing on these interventions, health plans can get a solid return on investment while helping their members.

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One example of an impactful intervention is getting patients totake their medications as prescribed. By improving medication adherence, health plans can significantly improve patient outcomes while reducing hospitalizations and other costly interventions.

This is true for many types of health conditions, including heart failure, asthma, chronic kidney disease. The same types of care management approaches can be used to solve medication adherence problems regardless of what conditions a patient has.

Another example of an impactful intervention is ensuring patients with serious or chronic conditions go to regular follow-up appointments with their physicians. This helps ensure patients are getting needed preventive care and screening tests to keep their conditions in check.

A follow-up appointment after being discharged from the hospital has been shown to be particularly important in preventing readmissions.

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