Prioritizing care management resources for the hypertension population
Data Science

Simplify care management with predictive analytics

December 15, 2020
Prioritizing care management resources for the hypertension population.
Natalie Benner


The NCQA’s population health standards require health plans to evaluate their population annually and then to use that analysis to update their programs and initiatives. In short, the NCQA population heath standards require health plans to continuously analyze, implement and evaluate.

Hypertension or high blood pressure affects one in three American adults.

A population analysis for one of Geneia’s health plan clients identified hypertension as a pertinent condition in prevalence, cost and utilization across all lines of business. Hypertension or high blood pressure affects one in three American adults. The prevalence has been increasing across all ages, even children, and increases with age:

  • 20-44 years: 26 percent
  • 45-64 years: 59 percent
  • 65+ years: 78 percent

Hypertension is often called the “silent killer” because “most of the time, high blood pressure has no obvious symptoms to indicate something is wrong.” Despite the absence of obvious symptoms, hypertension can cause a host of serious problems. Undetected and uncontrolled hypertension can lead to:

  • Heart attack
  • Stroke
  • Heart failure
  • Kidney disease or failure
  • Vision loss
  • Sexual dysfunction
  • Angina
  • Peripheral artery disease

Given the prevalence of hypertension, a health plan with 750,000 adult members can expect that approximately 225,000 are hypertensive. Health plans do not have the care management resources to engage all of the impacted members. Until now, it’s been challenging to determine which hypertensive patients to target.

In partnership with our health plan client, we apply enhanced identification and stratification to the population to find the members most in need.  Geneia’s Theon® platform uses risk scores, demographics, 100 clinical markers and gaps in care to identify and stratify every member of the population into one of four risk groups:

  • Healthy: Identify and keep the healthy, healthy
  • Rising Risk: Understand rising risk in the population to drive intervention planning to prevent or delay progression of disease
  • Chronic: Identify members who have a chronic disease and engage them in management of their disease to slow or stop its progression
  • Catastrophic: Identify and manage the complex needs of the catastrophically injured and ill

Together, we use a predictive model developed by the  Geneia Data Intelligence Lab with key input from Geneia clinical experts to further stratifies the population. The GDI Lab’s hypertension complications model predicts, for those with hypertension, which patients are likely to experience a complication like retinopathy and coronary artery disease in the next 12 months. Even more importantly, the model uses variables like age, total medical costs, outpatient visits and emergency department visits to yield binary classification with probability and risk buckets for each of the three complication stages. Knowing which members are at risk for Stage 1, 2 and 3 hypertension complications helps to prioritize patient activation efforts.

Hypertension's potential affects on the body.

We start with all members predicted to be at high risk for Stage 3 complications followed by Stage 2, high risk, Stage 3, medium risk, Stage 2, medium risk and Stage 3 and 2, low risk members.

As a result, the health plan clinical team starts outreach efforts with all members predicted to be at high risk for Stage 3 complications followed by Stage 2, high risk, Stage 3, medium risk, Stage 2, medium risk and Stage 3 and 2, low risk members.

Through the combination of clinical expertise and GDI Lab’s hypertension complications model, our client successfully triages the estimated 225,000 members with hypertension and focus care management resources to those members most likely to experience complications in the coming year.

As one who has worked in care management for decades, I am excited that the addition of predictive analytics to identification and stratification efforts enables our clients to better prioritize efforts and align interventional strategy to risk level.