AI-Driven Risk Stratification: A New Approach to Patient Engagement

There is no doubt that chronic diseases have a significant economic impact on the US healthcare system. People with chronic and mental health conditions spend approximately 90% of America’s healthcare expenditure. Wouldn’t it be great if we could distinguish such patients from the rest of the population and provide them with targeted and value-based care? One approach that has shown promise is risk stratification. Risk stratification is a way of identifying which patients are at the highest risk for adverse outcomes so that you can target your resources to those who need them the most.

Risk stratification is the process of assessing a patient’s risk for developing a certain health condition. It is important for patient care because it helps doctors and other healthcare providers tailor treatment plans for each patient. Patients are divided into three categories based on their risk level: high, medium, and low. The first two require care management teams to oversee patients, while the latter can be self-managed.

Risk Stratification in patient care

Risk stratification helps identify high-risk patients requiring frequent screenings or other preventative measures. In the era of value-based care, risk stratification is becoming increasingly important for improving patient outcomes and reducing costs. By identifying high-risk patients and addressing their needs early on, risk stratification can help prevent more serious health problems. For patients with higher risks, additional services may be provided, such as frequent follow-ups, social support, improved care coordination, medication guidance, or an invitation to enroll in an educational patient support program. Even those with low-risk scores may benefit from telehealth options or automated screening reminders.

HealthEM.AI typically uses AI algorithms to stratify patients based on factors such as age, medical history, gender, lifestyle, claims data, hospital data, social and other factors. For instance, a patient with a history of cardiac disease may be placed in a higher risk category than a patient without any known risk factors. As a result, the provider may recommend more aggressive treatment for the high-risk patient, such as regular monitoring or lifestyle changes. In contrast, the low-risk patient may only require routine care. Also, with continuous care, the heart patient may eventually move to a low-risk category by next year.

A provider can use the above metrics to create distinct patient groups before tailoring engagement strategies based on those groups. As a result of this strategy, a multidisciplinary care team can focus on high-risk patients both inside and outside of a clinic. By providing targeted and more personalized care to the risk category each patient falls under, patient risk stratification empowers value-based care and ultimately improves patient engagement and experience.

A patient’s risk can quickly rise from medium to high. Monitoring people who pose an increasing risk and developing upstream interventions for that risk category will be crucial to controlling healthcare costs. Hence, it will be necessary to have real-time access to big data to keep up with these fluctuations. In addition, identifying which patients should receive more intensive patient engagement can improve healthcare efficiency since the physician will be more efficient with their resources and time.

Benefits of AI-driven risk stratification 

Many benefits are associated with using risk stratification in care management and patient engagement. Some of them are:

• Allocates resources more efficiently. You can target your resources to those needing them most by identifying at-risk patients.
• Helps improve communication between different members of your care team.
• Improves health outcomes and reduces overall healthcare costs by targeting high-risk patients.
• Customizes treatment to patient needs.
• Identifies disease trends in a population and applies them to smaller samples to improve healthcare.

Challenges associated with risk stratification

Some potential risks associated with using risk stratification tools in patient care are:

• Patients may be misclassified as being at high risk when they are not. This could lead to unnecessary interventions and anxiety for the patients involved.
• There is a potential for personal information to be mishandled if it is not stored securely.
• Risk scores may be interpreted incorrectly by healthcare providers. This could lead to problems with diagnosis, treatment, and prognosis.
• Provider bias may lead to some groups of patients being unfairly targeted for intervention while others are not.
• Not all data sources used for patient risk stratification may be accurate or up to date. Some of them may be based on data acquired years back.
• Healthcare data is scattered and unavailable in a standard format, hence not actionable.

The future of risk Stratification

Advanced AI-driven risk stratification models will empower providers to predict, prioritize and prevent disease progression for better health outcomes, thereby improving the patient experience. In the future, patient risk stratification will also involve data sets larger than today with more predictor factors.

With healthcare evolving from a traditional fee-for-service model, healthcare organizations are increasingly focusing on value-based care. The shift requires a new approach to care management, which is data-driven and patient-focused. That’s where HealthEM.AI comes in. We help healthcare organizations improve outcomes and reduce costs by leveraging artificial intelligence. We recently helped a leading primary care group:

• Improve assessment of high-risk patients and reduce hospital admissions by 28%
• With 90.2% accuracy and 51.7% precision, predict the likelihood of readmissions for the next 12 months
• Generate ~ 1,500 insights from patient health data, cost data, and social attributes
• Observe a 75% change in the case mix of those patients identified as highest risk strata