Identifying high-risk patients and optimizing care plans are some of the main goals and purposes of implementing value-based care in your organization. Goals that require a lot of coordination and work to accomplish. Like most things in life, there are plenty of ways to go about this, but today we are going to be focusing on data analytics and predictive modeling.
We reached out to our amazing Healthcare IT Today Community and asked — what role do data analytics and predictive modeling play in identifying high-risk patients and optimizing care plans in a value-based care setting? Their answers are below.
Christopher Bayham, Chief Operating Officer at Xsolis
Predictive analytics has become the cornerstone of successful value-based care operations. We’ve seen this first-hand, having deployed more than a dozen machine learning models trained across billions of actual encounters to assess medical necessity, with accuracy rates over 85-90%. But the real value isn’t just in the predictions — it’s in the workflow integration it’s enabling, especially between payers and providers. Identifying high-risk patients is only valuable if you can intervene effectively. This means embedding prediction capabilities directly into clinical workflows, automating notifications and communication across the care team, and enabling interoperability between health systems and health plans.
Today’s sophisticated value-based care leaders are now moving beyond reactive risk stratification to proactive care optimization. They’re using natural language processing on clinical notes to identify care gaps, predictive models to assess discharge readiness and disposition, and generative solutions to increase the productivity and efficiency of over-burdened staff. The organizations succeeding in value-based care arrangements are those treating analytics not as a reporting function but as an operational necessity integrated into real-time care delivery.
Jason Prestinario, CEO at Particle Health
Data analytics and predictive modeling play a critical role in identifying high-risk patients and optimizing care in value-based settings. Whether you’re a health plan accepting risk for an employer group or a value-based care provider taking on downside risk, every risk-bearing organization needs to excel at two fundamentally different things. First, they need to understand the baseline chronic condition burden of their population and then create care plans that help manage the needs of this population. That part is table stakes. Then, they must be prepared to effectively manage care when patients have acute episodes that change their level of risk.
Unfortunately, most patients don’t gradually slide from high risk back to low risk over time. This is the most important part of predictive modeling and why better analytics matter. It’s easy to use previous claims history to identify chronic conditions, but what really sets you apart is the ability to predict and rapidly identify rising risk moments so you can optimize how you handle patient care. That’s the difference between analytics that describe what happened and intelligence that lets you actually do something about it.
Frank Vega, CEO at The Efficiency Group
Predictive analytics gives clinicians visibility for flagging high-risk patients before they fall through the cracks and aligning care plans with real-world data, not guesswork. The organizations doing well at providing value-based care are the ones treating analytics as a core clinical tool, not an afterthought.
Mary Sirois, Senior Vice President, Strategic Solutions at Nordic
Data analytics and predictive modeling can play an essential role in identifying high-risk patients and optimizing care plans. In this use case, analytics should be treated as a progression that advances from forecasting risk to averting events, then tightening accuracy, and finally tailoring interventions to each patient’s goals. AI can then be used as a spellcheck-like tool for surfacing patient details, risks, and recommendations into workflows exactly when clinicians need them.
This application of AI means that clinicians can spend more time interacting with patients instead of digging into a chart. By curating the information that matters in the moment (meds, allergies, care history, goals) at the point of care, computers can frame recommendations, leaving diagnoses and care planning for the provider, as well as the uniquely human traits of observing and listening and being present for a patient in need.
The key is seamlessness and the right build. Analytics are most valuable when they disappear into workflows, quietly elevating risks, gaps, and goals so care teams can act at the right time, in the right way, for each patient.
Erik Moore, Chief Technology Officer at Bamboo Health
In value-based care, analytics and predictive modeling are only as powerful as the actions they enable. Risk models can flag a high-need patient, but if that alert doesn’t trigger timely outreach or connect them to the right treatment, the opportunity is lost. That’s especially true in behavioral health, where missed interventions can escalate into emergency visits or hospitalizations. The future of value-based care lies in real-time, closed-loop systems that not only identify risk but mobilize care teams and networks to intervene when it matters most.
Ganesh Nathella, Executive Vice President and General Manager – HLS Business at Persistent Systems
In value-based care, analytics and predictive modeling shift risk management from retrospective review to proactive intervention. The most effective organizations use these tools not simply to label patients as “high risk,” but to understand why deterioration is likely and which factors are driving it. When clinical histories, utilization patterns, behavioral cues, and social determinants are combined into a longitudinal record, patient trajectories become far more predictable.
This level of insight enables earlier outreach, more precise care plans, and interventions calibrated to each patient’s actual barriers, ultimately leading to fewer avoidable hospitalizations and better chronic-condition stability. The key is embedding predictive intelligence into daily clinical decisions, not isolating it in reports.
For instance, one large payer organization now applies real-time clinical and claims data to intervene proactively, while another healthcare services firm uses predictive models to map Chronic Kidney Disease (CKD) progression and tailor care plans over time.
Bob Farrell, CEO at mPulse
Shifting care from reacting to conditions to preventing them is where value-based care starts. Anticipating risk before it becomes a cost can only happen if organizations are identifying inflection points in a patient journey through data analytics and predictive modeling. However, data analytics and insights mean nothing without action, and that’s where healthcare struggles most.
In today’s healthcare landscape, organizations that succeed in value-based care are those that collect and analyze diverse data sources, including clinical records, claims, social and environmental determinants, and engagement behavior to build a holistic picture of each member’s needs and risks. Predictive models capture inflection points and patterns that may otherwise be difficult to uncover, including patients likely to experience avoidable acute events, disengage from care, or those that may need additional support on treatments and next steps. These models can also catch care gaps and identify patients likely to progress to a chronic care condition.
However, data and insight alone are not enough; we need to connect the dots. The true value emerges when insights and points of inflection are paired with meaningful actions like personalized outreach that drives engagement through carefully tailored care plans, culturally relevant communication, and support models that empower individuals to engage in their health. That’s where a Health Experience and Insights approach becomes essential – creating a connective workflow with predictive analytics, omnichannel engagement, and health navigation portals under one streamlined ecosystem to predict risk, engage members, and drive care.
In a value-based environment, it’s not just about predicting who is high-risk; it’s about using those predictions and progressing from insight to action to close gaps, build trust, and guide members through their care journey in ways that improve outcomes and reduce avoidable costs. Sure, data analytics and predictive models are a foundation, but guidance on actionable steps that members need to take is true value-based care. It allows organizations to scale empathy, become proactive rather than reactive, and design care experiences that are member-centered and outcome-driven.
AJ Patel, CEO at TeleMed2U
In a value-based care setting, I believe that data analytics and predictive modeling are essential for proactively managing a patient’s health and optimizing outcomes while controlling costs. By analyzing large sets of clinical, behavioral, and demographic data, these tools help us identify high-risk patients before their conditions worsen, enabling earlier interventions and more personalized care plans.
In specialty telemedicine, specifically, we can leverage these analytics to detect gaps in care, for example, patients struggling to manage chronic conditions like diabetes or hypertension, and ensure they are promptly connected with the multidisciplinary care teams to support them. Predictive models also support capacity planning and resource allocation, helping providers better anticipate patient needs, prevent avoidable hospitalizations, and deliver the right care where and when it is needed most.
Ultimately, this data-driven approach enhances care coordination, improves patient outcomes, and supports the overall goals of value-based care: better health, better care experiences, and lower costs.
John Nash, Vice President, Strategic Initiatives at Redpoint Global
For success in VBC performance models, health systems and payers must transform unfit data into a complete, unified view of each patient. Analytics and predictive modeling to identify high-risk patients require complete and accurate patient data that is reflective of their health status at the moment of care. A complete picture of a patient’s data, including clinical, behavioral, digital, and social data, can help organizations anticipate risk and better allocate resources to deliver hyper-personalized engagement to guide patients to take action in their care journey.
Despite significant investments in data technology, most health organizations and plans still struggle with poor data quality, unresolved patient identities, and fragmented care journeys. These issues severely limit the effectiveness of patient engagement campaigns. Aligning IT environments with VBC goals requires updating “unfit” data from disparate sources- health records, claims, demographic, etc.- to create a unified view of each patient. When data is truly ready for use, it’s not just stored, it’s trusted, connected, and actionable. Organizations should invest in data solutions that transform data so it is right and fit for purpose, creating a foundation where analytics and AI can finally deliver the hyper-personalized, outcome-oriented care that value-based models demand.
Deb Jones, Senior Director, Insights Strategy at Tendo
Data analytics and predictive modeling are really the driving force behind proactive, personalized care in a value-based world. They shift the focus from reacting to illness to anticipating it—helping teams identify patients most at risk for complications, readmissions, or gaps in care before those issues surface. When done well, these tools do more than highlight risk; they provide context. By bringing together clinical data, behavioral insights, and social determinants, they paint a full picture of a patient’s needs.
That perspective helps care teams coordinate more effectively, target interventions, and focus resources where they’ll make the biggest difference. In the end, this results in better outcomes, smarter use of resources, and more meaningful patient impact.
Melissa Tyler, Vice President of Advisory Services at Lightbeam Health Solutions
Data analytics and predictive modeling work hand in hand in value-based care by analyzing historical and current performance trends to reveal risk patterns within a population. Predictive models then build on these insights to forecast which patients are most likely to deteriorate, enabling care teams to intervene early and get ahead of patient-management risks before they escalate. Together, these capabilities drive proactive, targeted care planning that improves outcomes and strengthens performance under value-based reimbursement models.
Kempton Presley, CEO at AdhereHealth
Data analytics and predictive modeling are indispensable in value-based care. They help identify who is most at risk—whether for poor medication adherence, an avoidable hospitalization, or an unmanaged social determinant of health. Advanced models can flag those members early, prioritize outreach, and surface the next best actions that care teams can take to prevent decline. But identifying risk isn’t the same as addressing it.
The real work happens when a person connects with the patient to understand why that risk exists—maybe it’s transportation, food insecurity, or medication affordability. Predictive insights are only as powerful as the human conversations that follow them. Medication adherence, in particular, is a linchpin for keeping members healthy and out of the hospital. Combining data-driven predictions with empathetic, person-centered outreach allows health plans to close gaps faster and deliver on the true intent of value-based care: better outcomes and a better experience.
Chandra Osborn, Chief Experience Officer at AdhereHealth
Analytics solutions today are incredibly sophisticated—they can track, report, and even predict outcomes with remarkable precision. But what they often lack is behavioral science. We can use advanced analytics and machine learning to risk-stratify, predict non-adherence, and prioritize outreach, yet those models are still incomplete if they don’t account for how people actually think, feel, and behave. The next step is to design predictive models that work with human behavior, not around it. That means embedding behavioral science into algorithms so they reflect real-world decision-making—why someone might delay filling a prescription, ignore a call, or disengage from care.
Many of the toughest challenges in value-based care, like medication adherence, are fundamentally human problems rooted in psychology and social determinants of health. AI can tell us who needs help and when, but behavioral science tells us how to reach them in ways that motivate change. The future of healthcare IT isn’t just more data—it’s smarter empathy, built into the models that drive action.
Sandhya Ravi, Principal Product Manager at AGS Health
In a value-based care setting, data analytics and predictive analytics are critical in identifying high-risk patients and optimizing their care plans. Predictive models can be built using clinical data, claims data, and even social determinants of health, which can help in identifying patients who are likely to experience complications, hospitalizations, or higher costs in the near future. This allows us to move from a reactive to a proactive approach.
Once those high-risk patients are identified, data analytics can help in stratifying the patients into risk tiers and personalize their care plans. For e.g., scheduling more frequent follow-ups, setting up remote monitoring, etc. Predictive analytics can also flag medication non-adherence or potential readmissions, so interventions can be planned before an issue occurs.
This not only helps improve patient outcomes but also helps in reducing avoidable hospital visits and the overall cost of care.
Shay Perera, Co-Founder & CTO at Navina
Analytics and predictive models are most powerful in value-based care when they stop being scoreboards and start being compasses. A risk score on its own doesn’t change outcomes. What matters is whether it’s grounded in the full longitudinal record—claims, labs, diagnoses, medications, prior admissions, even unstructured notes—and tied to a concrete next step in the care pathway.
The models that add the most value are those that answer specific questions: who might get worse in the coming months, whose chronic conditions aren’t staying on track, who may be headed for an avoidable ER visit, and what intervention is realistic in this setting. The real value comes from the move from generic ‘high risk’ lists to patient-specific recommendations that clinicians can actually act on at the point of care.
Such great answers! Huge thank you to all of you who took the time out of your day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.
What role do you think data analytics and predictive modeling play in identifying high-risk patients and optimizing care plans in a value-based care setting? Let us know over on social media, we’d love to hear from all of you!
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