By Jay Chowdappa, MD, co-founder and CEO, Zynix AI · Updated October 1, 2026
Key takeaways
AI identifies rising-risk patients by watching for changes between visits, such as climbing lab values, missed appointments, new prescriptions, emergency visits and social barriers, across clinical, claims and behavioral data. The value comes from acting early: outreach and a booked visit before the patient deteriorates, with clinicians handling anything that needs clinical judgment.
- Rising-risk patients are not yet the sickest, which is why earlier outreach can still change their course.
- Static risk scores built on last year's claims miss trends that only show up in recent labs, prescriptions and visit history.
- Dynamic risk models combine clinical, claims, pharmacy and social needs data and refresh as new data arrives.
- Detection has to trigger a specific next step with an owner, such as an outreach call, a booked visit or a care manager review.
- Agents handle outreach, scheduling and reminders, while clinicians handle symptoms, medications and care plan decisions.
The Rising-Risk Population
In value-based care, the highest-cost patients often aren’t the ones who are already critically ill — they’re the ones who are about to become critically ill. These rising-risk patients represent the greatest opportunity for intervention, but traditional care models lack the tools to identify them reliably before deterioration occurs.
Beyond Static Risk Scores
Traditional risk stratification assigns patients a score based on historical data — diagnoses, utilization patterns, demographics. But these static scores miss the dynamic signals that indicate a patient is trending toward a crisis. A diabetic patient whose HbA1c is slowly climbing, who has missed two recent appointments, and who just filled a prescription for a corticosteroid is flashing warning signs that a static risk score won’t capture.
AI-Powered Dynamic Risk Detection
AI models can continuously analyze streams of clinical, claims, and behavioral data to detect these emerging risk patterns. By processing lab trends, medication changes, appointment adherence, ED utilization, and social determinants of health, AI creates a dynamic risk picture that updates as new data arrives.
From Detection to Action
Identifying rising-risk patients is only valuable if it triggers timely intervention. Zynix AI’s rising-risk outreach agent automatically initiates outreach to identified patients, scheduling follow-up appointments and routing new symptoms or barriers to the care team under the care plan’s escalation rules, so already-overburdened care managers spend their time on the patients who need them.
The Value-Based Care Impact
Organizations that implement AI-powered rising-risk detection aim for reductions in avoidable hospitalizations, emergency department visits, and total cost of care. More importantly, they aim for improvements in patient outcomes that define success in value-based care.
Signals that mark a patient as rising risk
Rising risk shows up as a change in trajectory rather than a single high score. The most useful signals are the ones that move between visits and can be acted on this month.
- Clinical trends: lab values drifting the wrong way, such as a climbing HbA1c or falling kidney function, and new diagnoses that change a care plan.
- Utilization: a recent emergency department visit, an observation stay or a hospital discharge reported through ADT feeds.
- Medications: new high-risk prescriptions, gaps in refills, or several new prescribers in a short period.
- Engagement: missed appointments, an overdue annual wellness visit, or no primary care contact for an extended period.
- Social needs: transportation, food, housing or cost barriers captured during outreach or screening.
Who acts on a rising-risk flag
A flag is only useful when someone owns the next step. The work splits cleanly between what agents can do at scale and what needs a clinician.
Agents can reach out, run a structured check-in, book a visit, send reminders and capture barriers such as transportation. New symptoms, medication concerns and anything else that needs clinical judgment go to a care manager or the on-call clinician by rule, and agents never diagnose, triage clinically or recommend treatment. Care plans for chronic and preventive care keep each patient's tasks in sequence, so a flag ends in a completed visit or a documented reason it did not happen.
Frequently asked questions
What is a rising-risk patient?
A rising-risk patient is someone whose health and costs are trending upward but who is not yet among the highest-risk, highest-cost patients. Typical signs include worsening lab values, new chronic diagnoses, missed appointments, recent emergency visits and social barriers. Because they have not deteriorated yet, timely outreach and follow-up can still change their trajectory, which makes them a priority population in value-based care.
How does AI identify rising-risk patients?
It combines data a single report rarely brings together: claims history, EHR diagnoses and lab results, ADT notifications of emergency visits and admissions, pharmacy fills, appointment history and social needs screening. Models look for changes over time rather than a fixed score, and rank patients so care teams see who needs attention first. Risk and care gap analytics that feed worklists are one way to put this into practice.
What should happen after a patient is flagged as rising risk?
The flag should create a task with an owner and a deadline. Usually that means outreach to check in and book a primary care or care management visit, a clinician's review of medications and recent utilization, and help with barriers such as transportation. Track whether the visit happened and whether the patient's signals improved, not just whether a call was made.
How is rising-risk work different from high-risk care management?
High-risk care management focuses on a small group of patients who already have frequent admissions and complex needs, usually with intensive nurse involvement. Rising-risk work covers a larger group earlier in their trajectory, so it relies on lighter-touch, scalable steps: regular check-ins, timely visits, medication follow-up and help with barriers, with escalation to a care manager when a patient's signals worsen.
Related reading
- How AI Transforms Value-Based Care: Population Health, Risk Stratification, and Care Gap Closure
- Predictive Analytics in Healthcare AI
- How To Automate Care Management With AI Agents
About the author
Jay Chowdappa, MD is co-founder and CEO of Zynix AI. He is a physician, and ACOs led by him generated $300M+ in shared savings before he started Zynix AI to help care teams follow through on what their data already shows.