How does AI help with Patient Risk Stratification & Automated Communication in Value-based Care?

Over the last twenty years or so, artificial intelligence has gone from being a sci-fi concept to becoming reality. While many are raising concerns over its usage and the consequences our continued reliance on AI might lead to, the impact it has had on society and on our daily lives is undeniable. Whether it is the usage of virtual assistants like Siri and Alexa, or navigation systems like Google Maps and Waze, or even the algorithms used by social media platforms to curate our feeds, AI is an integral part of all our lives.

So, it is no surprise that AI has not only been integrated into the healthcare ecosystem, but it has innovated how we provide and receive care.

In many of our articles/blogs and other published content (which can be accessed here) we have brought attention to the shift that is currently occurring in the healthcare space: a shift from an antiquated, profit-centered, fee-for-service based care model to a principled, patient-centered, value-based care model.

AI is an essential component of this shift, and we’re going to explore two key reasons why that is so.

AI Refining Patient Risk Stratification

In simple terms, patient risk stratification categorizes patients into precise groups, based on the probability of them developing specific health problems or experiencing undesirable health outcomes. This information is used by payers/insurance providers to assign a risk score, which helps them determine and allocate what resources would be necessary for the patient to receive effective care.

This is something that has been done by insurance providers for a very long time. But, by general consensus, we can see that it wasn’t done very well. Two patients over the age of 60 are both considered to be at a higher risk of experiencing health problems; but without knowing the various other factors (such as their medical history, the environment they live in, family history, and more) that also affect their health, classifying them as the same leads to a probable scenario where one or the other (or maybe even both) would not receive efficient care.

The implementation of AI has changed that in many ways:

1. Precise Risk Prediction: AI can analyze a vast swath of data, such as demographics, social determinants of health, comprehensive patient health data, and so much more, to identify and classify patients and their needs accurately, so that they can be assigned a risk score that is appropriate for their needs.
2. Data-Driven Decision Making: Because AI provides more accurate and nuanced assessments of patient needs, both payers and providers are more well-informed about the patient’s needs and thus can be better prepared and are able to provide effective care.
3. Predictive Analytics & Preventative Care: AI can assess patient health data, analyze it in comparison to similar patient profiles, and predict the probability of patient health outcomes, which can be used to implement preventative measures, negating or reducing the possibility of future adverse health outcomes or events.

AI Improving Automated Communication

Automated communication tools are self-explanatory by name. Most of us use them in our daily lives; for example, the medication reminders in the health app of our iPhones. Or, receiving a text message or an email from our primary care physician, reminding us to get a yearly physical check-up. But with the integration of AI and patient risk stratification tools, automated communication can be better utilized in a multitude of ways:

1. Personalized Patient Engagement: Patient engagement has been proven to be a key variable in determining health outcomes. By providing patients with information that is tailored to them (whether it is through texts, emails, phone app notifications), they will be able to take an active role in their self-management, and it will empower them to stay on track with their treatment plans and other self-management protocols.
2. Targeted Messaging: AI infused communication tools can send the right messages specific to the patient’s preferences. High-risk patients receive frequent reminders about appointments, medications, and preventive measures, while low-risk patients receive fewer but relevant updates. For instance, a patient who is at high-risk for a medical event would receive regular messages (reminding them to schedule recurring appointments, take preventative measures, stick to their medication routines, and more), while a patient who is at a low-risk would not receive the same volume of messages. This ensures effective, need-specific communication.
3. Reduced Staff Burnout: We’ve written previously about the worrying burnout rates for healthcare professionals (link to some of our previous publications). AI integrated automated communication would not be a game-changer in this regard, but it would make Reduced Staff Burnout: We’ve written previously about the worrying burnout rates for healthcare professionals (link to some of our previous publications). AI integrated automated communication would not be a game-changer in this regard, but it would make a small, but noticeable difference on the demands and workload of the staff resources, which can be utilized more effectively.

How does it impact Value-Based Care?

1. Resource Optimization: We’ve written previously about the inevitability of our current care provision (fee-for-service) model and shifting demographics leading to a scarcity of resources necessary for providing care (link here). Through the integration of AI with tools such as risk stratification and automated communication, we can greatly reduce wasteful expenditure of these precious and finite resources, while enhancing patient care.
2. Better Patient Outcomes: We stated this under the impact of risk stratification, but with the addition of automated communication tools, patients will receive care that is tailored to their specific needs, and this will lead to better outcomes.
3. Cost Reduction: Integrating AI with risk stratification and automated communication, leads to patient’s receiving appropriate and efficient care, while also implementing steps in place that would greatly reduce the need for those measures to be necessary in the future. This would lead to improved financial returns for not only the patient, but the provider and payer as well under a VBC model of care.

The Road Ahead

By 2030, value-based care is poised to become the industry standard, and the role of AI in this transformation will be pivotal. Future advancements in AI-driven risk stratification will harness real-time data from wearables, genomic profiling, and environmental factors to predict patient risks with unparalleled precision. Predictive modeling will evolve further, allowing providers to anticipate complex health events and deliver timely, personalized interventions.

In automated communication, cutting-edge AI technologies such as conversational AI, voice recognition, and sentiment analysis will redefine patient engagement. Virtual assistants and AI-powered chatbots will provide seamless, hyper-personalized communication, offering patients tailored guidance, reminders, and support at every step of their care journey. These systems will bridge communication gaps, ensuring that patients feel supported and informed at all times.

The adoption of such futuristic AI technologies will not only enhance care quality and operational efficiency but will also empower providers and payers to meet their unified goal: delivering the best possible care to patients in a sustainable and impactful way. The road ahead is bright, driven by innovation and the shared commitment to reimagining healthcare delivery.

 

Learn how blueBriX can empower your care teams with innovative tools for targeted messaging and patient management.