Closing the Loop on Value Based Care With Predictive Risk Modeling
- POSTED ON
- August 18, 2025
- BY Natali E.
- POSTED ON
- August 18, 2025
- BY Cristina H.
Healthcare is moving away from fee for service toward value based care (VBC), emphasizing outcomes over volume.
The global value based care market was valued at $12.2 billion in 2025, with projections reaching $437.5 billion by 2032, growing at about 17% annually. Participation by providers is also growing significantly. The Centers for Medicare & Medicaid Services (CMS) reports a 25% increase from 2023 to 2024.
Central to value-based care (VBC) is the shifting from just treating illness to preventing it. Doing so effectively requires predictive risk modeling to close the loop.
Risk Stratification
Successful VBC hinges on identifying patients at risk: those most likely to experience complications, readmission, or rising costs. Yet many healthcare teams still rely on traditional analytics workflows that require IT teams and data specialists. This can take time, which often makes early intervention difficult.
Risk stratification helps providers to itemize and prioritize high-risk cohorts, align care pathways, and channel resources more effectively.
Predictive Models Drive Proactive Intervention
Predictive risk modeling combines clinical, operational, and other data to forecast adverse outcomes, and the results can be powerful.
For example, predictive healthcare analytics targeting at-risk patients can help reduce hospital readmission rates by up to 47%. A Cornell University study showed that focusing on medication regimens along with preventative care reduces hospitalization risk by more than a third.
Predictive models built on claims data is also helping identify the highest-cost patients. In one study, 0.16% of users were responsible for 9% of costs. Value-based care and targeted approaches to managing health for these cohorts yielded an estimated savings of $7.3 million per year across just 500 patients.
By moving from reactive case management to prevention, healthcare organizations are dramatically reducing costs and improving patient outcomes.
Using NoCode Conversational Analytics to Empower Care Teams
Traditional analytics pipelines often require specialized skills and long turnaround times. This slows down decision-making and makes it hard to respond quickly to patient needs.
No-code analytics platforms are changing that.
Intuitive Data Analytics (IDA) empowers care teams to ask questions about their data in natural language and get answers in real-time based on data. Whether it’s a care manager trying to identify diabetic patients at high risk of readmission or modeling the impact of a preventive outreach campaign, you no longer need to send queries of to IT or learn SQL.
Everything happens through an intuitive, conversational interface.
You can filter patient populations by risk level, location, comorbidities, or social determinants of health. You can explore care gaps, simulate what-if scenarios, and adjust strategies based on new trends, all within a single workspace without coding. The result? Speed and flexibility that matches the pace of frontline care. Instead of waiting weeks for a report, you can get answers instantly and dig as deeply into the data as you need.
Real-World Use Cases
Predictive analytics in VBC is not just theoretical. Let’s take a look at some real-world use cases.
Risk-Based Discharge Planning
One of the clearest opportunities for predictive analytics is reducing preventable readmissions, a key metric in most value-based contracts. At Allina Health, risk-based discharge planning tied to predictive analytics helped reduce 30-day readmissions by 27%.
Care teams received timely insights about which patients were most likely to be readmitted, enabling targeted follow-up and support. These efforts generated $3.7 million in variable cost savings, with even greater impact among the highest-risk subpopulations.
Managing Chronic Diseases
Chronic conditions like diabetes remain among the most resource-intensive challenges in healthcare. In fact, between 10% and 25% of all unplanned hospital readmissions are linked to diabetes.
Chronic conditions like diabetes remain among the most resource-intensive challenges in healthcare. In fact, between 10% and 25% of all unplanned hospital readmissions are linked to diabetes.
Optimizing Care Coordination for ACOs
Accountable Care Organizations (ACOs) depend on precise coordination to manage populations efficiently. Predictive tools make it easier to identify high-cost, high-risk individuals before their needs escalate.
Instead of reacting to claims data months after the fact, ACOs can now act proactively by targeting outreach, scheduling preventive visits, and allocating resources where they’ll have the greatest impact. Medicare Shared Savings Program participants achieved $1.19 billion in net savings in one year alone, in part due to stronger care coordination supported by analytics.
Linking Predictive Insights to Performance Targets
For healthcare organizations operating under value-based contracts, predictive risk has become a core performance driver.
Predictive insights can directly support a wide range of quality measures, including HEDIS scores, STAR ratings, and MIPS outcomes. When care teams understand which patients are trending toward poor outcomes, or which services are likely to prevent complications, they can make more precise decisions that boost both clinical and financial performance.
Beyond reducing readmissions, organizations using predictive analytics report a 15% decrease in hospital-acquired infections (HAIs) and an 8% drop in unnecessary imaging. These aren’t small gains. This dramatically improves patient outcomes, lowers treatment costs, and produces higher quality ratings.
IDA’s flexibility makes it easier to track, measure, and iterate toward these goals. Instead of building static dashboards or chasing lagging indicators, healthcare leaders can use dynamic modeling to adjust course in real-time. For example, if an outreach strategy isn’t yielding results, you can test alternatives immediately without starting from scratch or pulling in IT. That ability to close the loop quickly is what separates reactive organizations from those leading the way in value-based care.
Human first analytics make this continuous loop seamless, removing delays and empowering cross functional teams to act decisively.
Making Preventive, Predictive Care the Standard in Healthcare
Predictive risk modeling is foundational for effective, value based healthcare. By combining intuitive analytics interfaces with powerful machine learning, care teams can prevent unnecessary treatment, drive better outcomes, and meet performance thresholds.
IDA can make it happen. Try IDA’s no- code platform and unlock data-driven decision making today.
Explore more from IDA
Intuitive Data Analytics Unveils Revolutionary Business Intelligence Features to Its No-Code BI Platform at the Ai4 Conference in Las Vegas, NV.
Want to see IDA in action?
Get started with digital adoption today.
