Using AI To Identify Rising-Risk Patients Before They Deteriorate

AI-driven dashboards help care teams see rising-risk patients before they deteriorate.

The Rising-Risk Blind Spot In Value-Based Care

Every value-based care organization can point to its highest-risk patients. The member has already had multiple admissions this year. The frail patient on complex polypharmacy. The dialysis patient whose name everyone on the care team knows.

The harder group to see is the one directly behind them: rising-risk patients. These are people who are not yet your highest-cost members but are on a short path to becoming them. They may have a new diagnosis, a subtle pattern of missed primary care visits, or a recent spike in urgent care use. On paper, they look “stable.” In reality, they are one event away from a serious deterioration.

Traditional risk stratification frameworks focus on historical spend and chronic conditions. They do a reasonable job of identifying today’s high-risk population, but they often miss those whose risk is changing rapidly. In shared-savings programs and Medicare Advantage contracts, that blind spot shows up as preventable emergency department visits, avoidable admissions, and unplanned readmissions that eat into margin and quality performance.

To close this gap, ACOs and provider groups need to move from static risk tiers to continuously updated views of who is rising in risk, why it is happening, and what the care team should do next. That is where AI-driven prediction becomes practical, not theoretical.

Why Retrospective Risk Scores Are Not Enough

Most value-based organizations already work with some mix of HCC-based risk scores, retrospective analytics, and registry rules. These tools are necessary, but they were not designed to answer a simple operational question: “Which patients are about to get much sicker or much more expensive in the next 3 to 6 months?”

HCC and RAF scores are optimized for accurate risk adjustment, not for early clinical intervention. They are typically based on past-year claims and diagnosis coding, which means they lag behind what is happening in the clinic this month. A patient who deteriorated last quarter will look high-risk next year, not when the first warning signs appear.

Many care management teams also rely on simple registry rules in the EHR, such as “A1c greater than 9” or “two or more ED visits in the last six months.” These rules are transparent, but they are blunt. They do not account for combinations of factors, trends over time, or clues hiding in unstructured documentation.

The result is a workflow where care managers are either flooded with long lists that do not feel meaningful or left reacting to crises that could have been anticipated. Retrospective risk scores are important for contracting and reporting. They are not enough to guide day-to-day outreach.

What A True Rising-Risk Patients AI Model Looks At

A true rising-risk model is built around a simple idea: combine many weak signals into one strong, actionable prediction about who is likely to deteriorate soon. That requires a broader and more dynamic view of each patient than claims alone can provide.

Typical data domains include:

• Recent utilization patterns, such as ED and urgent care visits, observation stays, and missed primary care appointments.

• Clinical indicators from the EHR, including new diagnoses, abnormal labs, vital sign trends, and changes in problem lists.

• Pharmacy data, including new high-risk medications, rapid medication changes, or patterns that suggest poor adherence.

• Social and behavioral context, such as transportation issues, food insecurity, housing instability, and behavioral health conditions.

• Signals in unstructured notes, for example, a clinician's concern about decompensation, caregiver burnout, or self-reported instability.

No single data point determines the prediction. Instead, modern machine learning methods look at patterns across these domains. A patient who misses a primary care visit, starts using the ED more often, shows a rising A1c, and has new documentation of financial strain may be scored as a rising risk even if their historic spend has been modest.

Well-designed rising-risk models are also calibrated to the realities of value-based contracts. They can be tuned by population, such as MSSP ACO, Medicare Advantage, or commercial risk, and can be updated frequently enough to reflect what happened last week, not last year.

From Scores To Actionable Patient Lists

A prediction score is only useful if it changes what the care team does on Monday morning. That means moving from raw model outputs to clear, prioritized patient lists that fit into existing workflows.

At a practical level, this often looks like a small number of program tiers. For example:

• Tier 1: Immediate outreach. Patients whose rising-risk score and recent events suggest they are likely to have a serious event in the next 30 to 60 days.

• Tier 2: Proactive engagement. Patients whose risk is trending up but who have not yet crossed a critical threshold.

• Tier 3: Monitor. Patients with a modest elevation in risk may need lighter-touch digital engagement or watchful waiting.

For each tier, care managers need simple rules that define what happens next. Who owns outreach? What type of touch is expected? How quickly must it occur? When those rules are clear, the model’s output becomes a daily worklist instead of a confusing dashboard.

Avoiding alert fatigue is critical. That means setting thresholds so that rising-risk alerts are meaningful, limiting list size to what the team can realistically act on, and periodically reviewing which alerts actually led to changed outcomes. Over time, programs can adjust thresholds and tiers as they learn what works for their population.

Embedding Predictions Into Care Team Workflows

Rising-risk intelligence has the most impact when it shows up in the same tools that care teams already use, not in a separate analytics environment that requires extra logins and manual exports.

In a mature workflow, a care manager starts the day in a care management platform or task view that already includes a prioritized list of rising-risk patients, refreshed overnight. For each patient, the system displays a concise explanation of why they were flagged, recent key events, open gaps, and recommended next steps.

AI agents can extend this workflow from insight to action:

• A Zynix care coordination agent, such as ZynCare, can draft personalized outreach messages or call scripts based on the patient’s conditions, recent events, and social context.

• Scheduling agents such as ZynSchedule can propose appointment times, coordinate transportation if available, and close the loop on follow-up visits.

• Documentation and scribe tools can automatically capture the context of outreach, update problem lists, and surface any new risk-relevant information back into the analytics layer.

When these pieces are connected, the rising-risk model does not just generate a score. It powers a closed-loop system where insights trigger timely outreach, tasks are completed, documentation is updated, and results flow back into the model for continuous learning.

Measuring Impact On Outcomes And Total Cost Of Care

Value-based leaders will rightly ask, “How do we know our rising-risk program is working?” The answer begins with a clear measurement that links model outputs to clinical and financial outcomes.

Common metrics include:

• Emergency department visits per thousand members, especially for ambulatory-sensitive conditions.

• Unplanned inpatient admissions and 30-day readmissions.

• Total cost of care measures, such as PMPM or spend per attributed beneficiary.

• Care management productivity, such as outreach completion rates and time from flag to first contact.

• Quality and experience measures that may be affected by better proactive care.

Organizations often start with pilot cohorts, such as a subset of high-impact conditions or a specific payer contract, and compare outcomes for patients enrolled in the rising-risk program versus similar patients who were not. Over time, more advanced teams use quasi-experimental designs to understand incremental impact after controlling for other interventions.

Analytics platforms need to make this evaluation repeatable. Ideally, care teams can see not just a global trend but also which rising-risk tiers, outreach modes, and program designs are producing the greatest improvements.

Governance, Explainability, And Clinical Trust

No rising-risk model will succeed without trust from clinical, compliance, and operational leaders. Governance cannot be an afterthought. It should be part of the initial design.

Key elements of a sound governance framework include:

• Clear documentation of model purpose, input data, and intended use cases.

• Regular performance and fairness reviews across age, race, ethnicity, geography, and payer type, where data permits.

• Human-in-the-loop decision making, where clinicians and care managers retain authority to accept, modify, or defer recommended actions.

• Accessible explanations that show which factors contributed most to a given patient’s score, without overwhelming users with technical detail.

• A structured process for raising and resolving questions or concerns from front-line staff.

When clinicians understand why a patient is being flagged and feel empowered to provide feedback, adoption increases. When compliance teams see a clear audit trail of data sources, model versions, and policy decisions, they are more comfortable supporting scale-up.

Implementation Playbook For Value-Based Organizations

Moving from concept to a live, rising-risk program can feel daunting, but the path is repeatable. Most successful organizations follow a stepwise approach rather than trying to solve everything at once.

Step 1: Align On Objectives And Population

Start by defining the business and clinical questions. Are you focused on reducing avoidable ED visits, lowering readmissions, supporting medically complex seniors, or stabilizing a specific contract? Clarify the population and outcomes before debating model details.

Step 2: Assess Data Readiness

Inventory the data sources you can reliably use today, including claims, EHR, labs, pharmacy, and SDOH. Address obvious data quality gaps, such as missing identifiers or inconsistent encounter types, and define a minimum viable data set for the first model.

Step 3: Design Workflows With Care Teams

Bring care managers, nurses, and physicians into the design process early. Map current workflows, identify points where a rising-risk signal would have changed decisions, and agree on how new lists or alerts will be handled. Decide upfront who owns outreach, documentation, and escalation.

Step 4: Launch A Controlled Pilot

Begin with a focused cohort and a limited set of program tiers. Provide hands-on training, shadow early users, and capture qualitative feedback. Use this period to refine alert thresholds, messaging templates, and escalation pathways.

Step 5: Measure, Iterate, And Scale

After a defined pilot period, review outcome measures alongside user feedback. Keep what works, sunset what does not, and adjust thresholds and workflows. Once you see a consistent impact, extend the program to additional contracts or populations in a disciplined way.

How Zynix Supports Rising-Risk Programs

Zynix is designed to help value-based organizations move from retrospective reporting to proactive intervention on rising-risk patients. Each product plays a specific role in that journey.

• ZynPredict serves as the predictive engine, combining clinical, claims, utilization, and social data to generate rising-risk scores that update as new events occur.

• ZynAnalytics gives leadership and program owners a clear view of performance, from population-level trends to the impact of individual interventions and outreach strategies.

• ZynCare uses AI agents to operationalize outreach, drafting messages, call scripts, and documentation so care teams can focus on human connection instead of manual paperwork.

• ZynSchedule closes the loop by coordinating appointments, reminders, and follow-up tasks for patients who need visits or diagnostics as a result of rising-risk flags.

Together, these tools support a closed-loop rising-risk program: identify who is likely to deteriorate, understand why, reach out in a timely and personalized way, and continuously monitor the impact on quality and total cost of care.

For ACOs, Medicare Advantage plans, and provider groups that are serious about value-based performance, this combination turns rising-risk prediction from a slide in a strategy deck into a daily operational capability.

Key Topics And Keywords Covered

• Focus keyword: rising-risk patients

• Predictive analytics for value-based care

• AI-driven risk stratification and early intervention

• Population health management and care management workflows

• ZynPredict, ZynAnalytics, ZynCare, ZynSchedule for rising-risk programs

  
  

Connect with a Zynix value-based care strategist to see how ZynPredict, ZynAnalytics, and our AI agents can help your teams identify deterioration sooner, intervene more efficiently, and strengthen financial performance across your ACO or Medicare Advantage contracts.

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