Using Risk Stratification in Patient Engagement Strategies

As healthcare professionals continue to develop meaningful patient engagement strategies, they should consider using risk stratification to determine how and with which patients providers will engage.

Risk stratification is “the process of assigning a health risk status to a patient, and using the patient’s risk status to direct and improve care,” according to the American Academy of Family Physicians (AAFP).

“The goal of [risk stratification] is to help patients achieve the best health and quality of life possible by preventing chronic disease, stabilizing current chronic conditions, and preventing acceleration to higher-risk categories and higher associated costs.”

Said otherwise, risk stratification helps healthcare providers tailor their patient engagement and care management efforts to individual populations with varying healthcare needs. A patient with no comorbidities might not need a lot of patient engagement at the moment. Meanwhile, someone with multiple complex chronic illnesses might need a high-touch engagement strategy.

Intensive patient engagement for an otherwise healthy patient may not be a great investment for primary care providers operating under tight margins. On the other hand, a low-touch patient engagement strategy could lead to costly acute episodes in the emergency department for complex patients.

Healthcare professionals can determine how they will target patient engagement strategies by understanding patient needs and which will require more provider attention. These patient engagement strategies will ideally lead to healthier patients who cost the industry less money.

HOW TO APPROACH RISK STRATIFICATION

In patient engagement, risk stratification means determining which patients are most at risk of developing a costly condition, of costing the organization money, or of seeing a pre-existing healthcare condition worsen. Providers can use risk stratification to better tailor their patient engagement efforts.

For example, a young and healthy patient will likely not need extensive patient engagement from her provider. This low-risk population will usually benefit from annual check-ups, preventive care, and providers who are openly available on the patient portal and other preferred communication channels for potential health emergencies.

However, a diabetic patient will need more sophisticated patient engagement, complete with strong patient-provider communication, regular check-ups, medication management, and care coordination.

Risk stratification in patient engagement means targeting activation efforts appropriately, according to a report from the Commonwealth Fund.

“The success of population health management depends, in part, on accurately identifying patients at high risk for poor health outcomes as well as preventable and costly health events,” the report authors explained. “Risk-stratification approaches typically focus on clinical markers.”

Healthcare organizations usually use artificial intelligence algorithms leveraging data from the EHR to risk stratify patients. Algorithms might look at:

• Disease state or comorbidities
• Social determinants of health
• Patterns of healthcare utilization
• Patterns of visits to high-cost settings, like the emergency department

Typically, healthcare experts refer to patients as being in one of four risk categories: highly complex, high risk, rising risk, and low risk. Using an algorithm factoring in the above metrics (or other metrics important to the unique heath system) can help providers categorize patients into those categories and then tailor patient engagement strategies.

“With such an approach, a care team can quickly react to different intensities of illness in their panel and proactively manage risk,” researchers wrote in a 2019 Journal of Health Care for the Poor and Underserved article. “This approach allows a multidisciplinary care team to target high- risk patients inside the walls of the clinic and the communities where they live.”

Providers should be aware of patients who move in and out of different risk levels. A patient might have a heart attack one year, placing him in a high-risk category. But by the following year, that patient may have quit smoking and started exercising, moving him down into the rising risk or even low-risk category.

Conversely, a patient can move from rising- to high-risk very quickly. Keeping track of rising-risk patients, and even designing some upstream interventions for that risk category, will be critical to keeping healthcare costs at bay.

Keeping up with those fluctuations will require access to big data in real time.

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