Anticipating Risk: The Real Impact of Predictive Analytics in AI Programs

AI in Care Management Program use has grown in healthcare as organizations need ways to spot patient problems early. Predictive analytics reviews patterns in medical records, billing data, and patient behavior to find who needs help next. This shifts care from fixing problems after they happen to preventing them before they start. Value-based contracts impose financial penalties when patients use emergency rooms or return to hospitals after discharge. These financial pressures make predictive tools essential for finding high-risk patients early and taking action before they get sicker.

How Predictive Models Identify Risk

The AI in Care Management Program examines multiple data sources at once. EHRs contain medical history and current diagnoses. Claims reveal which services patients used recently. Pharmacy records show whether patients picked up their medications. Social determinant data covers housing and food access. Lab results track health markers over weeks and months.

The software processes these inputs to score risk levels:

• Hospital admission likelihood in the next 30 or 90 days
• Chronic disease worsening probability
• Medication skipping risk
• Emergency department visit chance
• Missing preventive care potential

Risk Stratification for Resource Allocation

Risk scores by themselves do not improve patient outcomes. Organizations must use these predictions to direct their resources strategically. Stratification sorts patient groups into levels based on risk and specific care needs.

High-risk patients

High-risk individuals get intensive case management with regular contact and coordination across doctors. These patients usually have complicated conditions that require specialist appointments, home health services, and ongoing medication support.

Rising-risk patients 

These patients get targeted help before their health deteriorates. Care managers contact them when the software spots warning signs like weight changes, skipped refills, or missed tests. This early intervention often prevents costly hospital stays.

Low-risk patients 

Such individuals receive automated wellness programs with text reminders, educational materials, and self-care tools. Automating care for this group frees staff time to focus on patients who need hands-on support.

The Care Management Value Chain

The Care Management Value Chain describes how AI supports each care management stage. Identification uses risk modeling to find patients who will likely cost more later. Stratification ranks which patients need attention first. Engagement tools handle outreach and communication automatically. Management systems recommend what care managers should do next.

Each stage depends on the one before it. Finding the right patients does not help without ranking them properly. A good ranking fails without reaching patients effectively. Reaching patients means nothing if the recommendations do not work. The chain breaks when any part fails.

Predicting Specific Clinical Events

Basic risk scores give limited direction for intervention. Predictive models now target particular events that need specific responses.

• Hospital readmission models look at discharge data to find patients at risk of coming back within 30 days. The software checks diagnosis, past admissions, medication complexity, and available family support. Based on these predictions, hospitals schedule follow-up visits, review medications, and arrange calls before patients leave.
• Disease progression models watch health markers and symptoms for chronic illnesses. When diabetes patients show rising blood sugar trends, the system triggers medication adjustment alerts. Similarly, heart failure patients with rapid weight gain are contacted about fluid buildup before hospitalization becomes necessary.
• Medication adherence predictions find patients likely to stop taking prescriptions. The system identifies risk by looking at pharmacy claims showing refill gaps combined with past behavior. Interventions can then include simpler medication schedules, copay assistance, or automated reminders.

Operational Benefits Beyond Clinical Care

Predictive analytics 

It improves operations across healthcare organizations beyond direct patient care. Capacity planning models forecast patient volumes to set appropriate staffing levels. Emergency departments use these forecasts to adjust nurse schedules during expected busy periods.

Resource allocation

Resource allocation improves when organizations can anticipate which services patients will need. Predictive models help facilities plan equipment purchases, arrange specialist contracts, and schedule home health services based on expected demand rather than relying only on past patterns.

Quality metric tracking

Tracking shifts from retrospective to prospective. Organizations can identify patients likely to miss quality measures before reporting periods close. This warning gives care teams time to address gaps in screenings, vaccinations, and chronic disease monitoring while scores can still improve.

Measuring Impact

AI in Care Management spending must show measurable results to justify investment. Important metrics include:

• Hospital admission and readmission reduction
• Emergency department visit decreases
• Medication adherence improvement
• Preventive care gap closure rates
• Care manager productivity gains
• Total care cost trends

Organizations should compare these numbers for patients receiving predictive analytics guidance versus those who do not. This comparison matters especially for value-based contracts where better outcomes translate directly to shared savings or bonus payments.

Patient outcomes provide the real measure of success. Lower hospitalization rates mean patients stayed healthier at home. Better medication adherence shows improved chronic disease control. Closed screening gaps lead to finding diseases at earlier, more treatable stages.

About Persivia

Persivia builds AI-powered care management platforms focused on predictive analytics. The platform reviews patient data over time to create risk scores, rank interventions, and automate care management work across the Care Management Value Chain. It finds high-risk groups through disease assessments and population models. Care managers get clear information about which patients need attention and which actions will work best.

All You Need To Know About The CMS Lead Model

The CMS Lead Model starts in January 2027 and runs for 10 years through December 2036. Medicare built this program to replace ACO Reach and open doors for providers who got shut out of earlier models. Rural clinics, small practices, and doctors treating complex patients can now join. The old requirements that blocked them are gone.

What makes LEAD different is simple. The financial targets stay fixed for all 10 years. Previous programs changed these targets every few years, which made it hard for providers to plan or invest in better care systems. LEAD also pays upfront money to help smaller organizations build the infrastructure they need.

How LEAD Works Differently

LEAD keeps the same financial benchmarks through 2036. Older accountable care programs had a problem where successful ACOs got penalized. When they improved care and saved money, CMS would lower its targets the next year. That meant good performance actually hurt future earnings.

LEAD fixes this by offering:

• Monthly payments to cover patient care costs instead of waiting for year-end savings
• Two risk levels so organizations can choose what fits their size and experience
• Lower patient minimums so smaller practices qualify
• Direct payments for building care coordination systems in rural areas

Providers pick between 100% global risk or 50% professional risk. Global risk means taking on all Medicare costs for patients. Professional risk covers only outpatient and physician services. Smaller groups usually start with professional risk to learn the model before taking on full financial responsibility.

Who Gets Covered Under LEAD

The CMS Lead Model targets patients who struggle to manage their health alone. This covers homebound individuals, people with both Medicare and Medicaid coverage, and those in areas where healthcare is hard to access.

CMS built LEAD after reviewing why ACO Reach struggled with certain patient groups. Standard methods for assigning patients to ACOs did not work well for people with multiple chronic conditions. LEAD uses better risk scoring to account for how sick patients are when setting payment rates.

Some states are testing a dual-eligible pilot within LEAD. These patients have Medicare and Medicaid, but the two programs usually do not talk to each other. That creates gaps in care. The pilot aligns payments so providers get rewarded for coordinating both coverage types.

Payment Options in the Model

ACOs in LEAD get paid monthly per patient instead of billing for each service. This is called capitation. The amount depends on how sick the patients are and what services they need.

Two main payment paths are available:

• Primary care capitation that covers prevention, checkups, and managing ongoing health issues
• Total care capitation that includes hospital stays, specialist visits, and all Medicare Part A and B services

There is also something called CARA, which stands for Coordinated ACO Risk Arrangements. This lets ACOs partner with specialists on specific care episodes. The first one covers fall prevention. More episodes will be added later. CARA uses a digital system to make contracts between ACOs and specialists easier to set up.

Eligibility Requirements

Current ACO Reach participants can move directly into LEAD. New applications open in March 2026 through the CMS application portal.

Organizations that can apply:

• Medicare providers who have not joined an ACO before
• Federally Qualified Health Centers and Rural Health Clinics
• Independent doctor practices in rural or underserved locations
• Provider groups where many patients have both Medicare and Medicaid

The accountable care program lowered the bar for entry. Smaller practices that could not meet old patient volume requirements can now qualify under the new minimums.

What Patients Get From LEAD

LEAD lets providers build programs around what their patients actually need. Funds go toward managing diabetes and heart disease, stopping falls before they happen, and making sure different doctors share information.

Starting in 2029, ACOs can help patients pay for Part D prescription drug premiums. Many people skip medications because they cannot afford the copays. Subsidizing these costs helps patients take their medicines as prescribed.

Patients still choose any Medicare provider they want. LEAD does not create restricted networks or limit where people can get care. The model adds telehealth services and care navigators to help patients without taking away existing options.

The Bigger Picture

Medicare wants all beneficiaries connected to an accountable care relationship by 2030. Right now, about 14.3 million people get care through an ACO. LEAD expands this to groups who could not participate before.

The 10-year commitment matters because building better care systems takes time. Hiring care coordinators, buying data analytics tools, and training staff require upfront investment. Shorter programs did not give providers enough time to see returns on that spending. Many successful ACOs left earlier models when their benchmarks got cut after performing well.

LEAD bets that stable, long-term benchmarks will keep high-performing organizations in the program and encourage new ones to join.

Managing LEAD Participation With Better Technology

ACOs joining LEAD need platforms that handle data tracking, patient risk assessment, and quality reporting. Persivia offers platforms specifically built for organizations managing the total cost of care under value-based payment models.

These platforms track which patients are assigned to your ACO and calculate how sick each person is. They flag which patients need extra help and send quality data to CMS. Your staff spends time on patient care, not filling out spreadsheets. Persivia handles the technical work for ACOs at every stage of LEAD participation with analytics, care coordination tools, and quality tracking. 

2026 Strategies for Growth in Accountable Care Organizations

Accountable Care Organizations face major changes in 2026 as CMS implements new financial methodologies and program requirements. Organizations pursuing growth must adapt to tighter risk adjustment caps, updated benchmarking approaches, and increased quality emphasis. CMS aims to have 100% of traditional Medicare beneficiaries in accountable care relationships by 2030, creating opportunities for expansion for prepared organizations.

Transition to Two-Sided Risk Models

ACOs operating under one-sided risk arrangements face pressure to accept downside risk. The MSSP now limits initial one-sided participation to five years for new agreement periods starting in 2027. Organizations must evaluate readiness for enhanced tracks where savings potential increases alongside loss exposure. ACOs with mature care management programs and proven cost control capabilities are better positioned for this transition.

Strengthen Primary Care Networks

Strong primary care foundations drive ACO performance across all models. Organizations expand by recruiting primary care physicians in underserved markets and supporting existing providers with care coordination infrastructure. Primary care attribution determines beneficiary assignment, making physician relationships critical for population growth.

Invest in Care Coordination Infrastructure

Growing Accountable Care Organizations need software that connects hospitals, specialists, rehabilitation facilities, and community services. The software pulls patient data from all these places. Care teams with complete patient information coordinate better than those working from incomplete records.

Optimize Quality Performance Strategies

Quality scores directly affect shared savings percentages and track eligibility. Organizations prioritize measures yielding maximum impact, including patient experience surveys, preventive service delivery, and chronic disease control metrics. Real-time quality tracking allows intervention during patient encounters rather than retrospective gap closure.

Critical Quality Domains

• Patient and caregiver experience measures
• Care coordination and safety measures
• Preventive screenings and vaccines
• Chronic disease management for high-risk patients

Plan for Model Uncertainty Beyond 2026

ACO REACH ends in 2026 unless CMS extends it. Organizations currently in REACH should look at joining MSSP or other programs. Having contracts with multiple value-based programs means organizations stay viable even when one program ends or changes.

Prepare CMS LEAD Model Transition

The CMS LEAD Model represents CMS’s next phase of accountable care following the scheduled end of ACO REACH after 2026. While detailed financial parameters are still forthcoming, CMS has signaled that LEAD will apply tighter guardrails around risk adjustment, benchmarking stability, and coding intensity to support long-term sustainability. Organizations planning to transition into LEAD must evaluate how these controls may affect benchmark revenue, beneficiary growth, and performance strategy as CMS standardizes expectations across advanced risk models.

Key CMS LEAD Transition Considerations

• Stronger limits on risk score growth tied to historical baselines
• Refined coding intensity controls, with adjustments for high-need populations
• Risk adjustment constraints for newly aligned beneficiaries
• Elimination of growth-based exemptions as CMS enforces consistent scale and accountability

Takeaway

Persivia helps Accountable Care Organizations manage transitions between programs. The platforms pull data from hospitals, clinics, and other care locations. Organizations see their quality scores, which patients are assigned to them, and projected savings or losses under MSSP, REACH, and commercial ACO contracts. Organizations using Persivia access real-time data supporting decisions about risk model selection, provider network development, and quality improvement priorities.

Redesigning Care Delivery With Clinical Decision Support CDS Systems

Physicians spend up to 6 hours daily on administrative tasks that pull focus from patient care. Clinical Decision Support CDS Systems address this problem by delivering patient insights directly within existing workflows. These systems aggregate data from multiple sources, identify care opportunities, and enable action without requiring clinicians to switch between applications. Organizations implementing integrated CDS reduce documentation time while improving quality measure performance.

Moving From Alerts to Actionable Workflows

Traditional Clinical Decision Support CDS Systems generate alerts that create additional work for clinicians. Staff receive notifications about care gaps or quality measures, but must manually navigate to different systems to address them. Effective CDS provides insights alongside the ability to act on them immediately. Clinicians review recommendations and document interventions within their current application rather than opening separate platforms.

Bidirectional EHR Integration Enables Action

CDS platforms require bidirectional connections with electronic health record systems. Read-only integrations show information but force manual data entry in the EHR. Bidirectional systems read patient data and write completed actions back to the medical record. Physicians can order tests, update problem lists, or document interventions through the CDS interface with changes automatically reflected in the EHR.

Essential Integration Capabilities

• Real-time data synchronization with EHR systems
• Write-back functionality for completed actions
• Single sign-on eliminating separate logins
• Embedded views within EHR workflows

Reducing Cognitive Load Through Closed-Loop Systems

Physicians make hundreds of clinical decisions daily. Each alert requiring separate action adds cognitive burden. 

• Open-loop CDS tells clinicians what needs attention but leaves execution to them.
• Closed-loop systems present opportunities and provide mechanisms to address them immediately. 

This approach reduces the mental overhead of tracking incomplete tasks across multiple systems.

Aggregating Data From Disparate Sources

Patient information exists across EHRs, claims databases, health information exchanges, laboratories, and pharmacies. CDS platforms consolidate these sources into unified patient views. Clinicians access comprehensive medication histories, recent utilization patterns, quality measure statuses, and risk scores without querying individual systems. Moreover, data aggregation happens continuously in the background rather than requiring manual refreshes.

Prioritizing Interventions at the Point of Care

Not every alert carries equal urgency. CDS systems rank opportunities based on clinical priority, quality measure impact, and timing requirements. High-risk patients with overdue interventions appear prominently. Routine screenings for stable patients receive lower priority. This stratification helps clinicians focus their limited time on actions producing the greatest impact.

Typical Prioritization Factors

• Patient risk scores and recent utilization
• Quality measure deadlines and program requirements
• Clinical urgency based on lab values or diagnoses
• Previous intervention attempts and patient engagement

Supporting Multiple Quality Programs Simultaneously

Healthcare organizations participate in various quality reporting programs, including HEDIS, MIPS, ACO measures, and STAR ratings. CDS platforms track requirements across all programs using the same underlying patient data. Further, clinicians see which interventions satisfy multiple quality measures, allowing efficient gap closure during single patient encounters.

Measuring Impact on Clinical Efficiency

Organizations evaluate CDS effectiveness through documentation time, quality score improvements, and staff satisfaction metrics. Physicians using integrated CDS report reduced time spent on administrative tasks. Quality measure closure rates increase when systems enable action during patient visits rather than requiring separate outreach. These efficiency gains allow practices to see more patients or allocate time to complex care management.

Takeaway

Persivia's CareTrak® platform connects bidirectionally with over 80 EHR systems, enabling clinicians to view patient insights and take action without switching applications. The system aggregates data from clinical, claims, and laboratory sources while providing write-back capabilities that close the loop between insight and intervention. Healthcare organizations using this platform reduce administrative burden on physicians while improving quality measure performance across HEDIS, MIPS, and ACO programs.