AI in Value-Based Care: Unlocking New Possibilities for Healthcare Payers

The transition from fee-for-service models to value-based care (VBC) represents a major shift in the healthcare industry, requiring payers and providers to prioritize quality outcomes and cost efficiency. According to recent research from Gartner, "AI in Value-Based Care: Part 1 Roadmap Planning for Healthcare CIOs," artificial intelligence (AI) is a critical enabler of this transformation.* AI offers a path to more proactive care, optimizing operations, and delivering more personalized member experiences. For health plan executives, understanding how to leverage AI effectively can mean the difference between keeping up with market demands or falling behind. Here we provide a look at some of the key insights from the Gartner report.

Why AI is Essential to Advanced Value-Based Care

Gartner identifies AI as the "foundational technology" for VBC, underscoring its potential to transform how healthcare payers and providers deliver care. Unlike traditional models, which often react to member health issues after they arise, AI allows organizations to anticipate and address risks proactively. This shift results in significant improvements across three pillars of VBC:

1. Identifying High-Risk Members
   AI leverages data from electronic health records (EHRs), social determinants of health (SDOH), claims, and more to pinpoint members at risk of costly or adverse health events. Tailored interventions can then be deployed early, ensuring better outcomes and lower costs.

2. Optimizing Resource Allocation
   By predicting member needs, AI helps payers allocate resources more effectively. For example, AI-enabled advanced analytics can identify trends, streamline workflows, and reduce inefficiencies, leading to cost reductions and a more seamless care experience.

3. Enhancing Member and Provider Experiences
   From automating administrative tasks to improving care coordination, AI removes friction points in the system. For members, this often translates to faster care delivery. For providers, it means more time to focus on clinical care rather than paperwork.

Prioritizing AI Use Cases for Maximum ROI

Not all AI initiatives are created equal, and with limited resources, healthcare executives must prioritize projects with the highest return on investment (ROI). Gartner highlights several high-impact use cases where AI delivers significant value:

Population Health Management
AI-powered analytics help identify care gaps and recommend interventions at scale. These solutions utilize advanced algorithms to analyze massive, diverse datasets - ranging from electronic health records and claims to SDOH data - to uncover opportunities for targeted, high-impact outreach.

By leveraging real-time data and integrating predictive risk models, organizations can more precisely segment populations, prioritize members with emerging or escalating health risks, and accelerate deployment of evidence-based interventions. This enables health plans to not only address existing gaps in care, but also to anticipate future needs, prevent avoidable complications, and enhance overall population health performance. For example, organizations using predictive analytics have reduced hospital readmissions by double digits, illustrating its power to drive proactive care. 

Utilization Management Automation
AI tools simplify complex processes by analyzing clinical data to flag inappropriate utilization patterns or highlight opportunities for intervention, ensuring resources are used where they are needed most. Leveraging machine learning algorithms and real-time data streams, these solutions can quickly detect anomalies in care delivery, such as overuse of specific services, gaps in prior authorization, or deviations from evidence-based guidelines. 

Automated workflows make it possible to promptly alert care management and utilization review teams, accelerate approvals for medically necessary services, and reduce the administrative burden typically associated with manual reviews. As a result, health plans can better align care delivery with value-based objectives - improving operational efficiency, reducing unnecessary spending, and supporting higher quality outcomes for members.

The ongoing integration of AI-driven utilization management also empowers payers to maintain compliance with regulatory standards and adapt to evolving industry requirements, further advancing a proactive and member-centric approach to healthcare management.

Predictive Models
Real-time, AI-informed predictive models serve as a cornerstone in value-based care by delivering clinically actionable insights that enhance the identification and stratification of rising-risk populations. These advanced models leverage continuously refreshed datasets - including clinical records, claims data, and social determinants of health - to proactively surface individuals whose health status is trending toward higher acuity.

By segmenting members based on nuanced risk signals, payers and providers can more accurately prioritize outreach, deploy resources where they’ll have the highest impact, and tailor interventions to mitigate avoidable adverse events. The integration of pre-adjudicated claims data and automated risk stratification enables organizations to identify emerging risk patterns sooner, closing care gaps faster and improving member outcomes while supporting overall cost containment and operational efficiency.

Social Determinants of Health (SDOH) Integration
Incorporating non-clinical data like housing stability or access to transportation into predictive models creates a more holistic view of member health and allows for targeted, equitable care strategies. SDOH data represents critical factors that influence medical outcomes but are often overlooked in traditional predictive analytics. By integrating SDOH insights alongside clinical indicators and claims information, payers and providers can better identify at-risk members whose health and care access are impacted by socioeconomic or environmental barriers.

This comprehensive approach enables the design of interventions that address root causes - for example, coordinating transportation services for members facing mobility challenges, or connecting individuals to community resources to address housing insecurity. As a result, care plans become more precise, personalized, and effective in reducing health disparities, elevating both quality outcomes and operational efficiency.

Bridging Silos and Expanding AI's Impact

One of AI's most transformational aspects is its ability to break down silos within healthcare organizations. Many VBC programs suffer from fragmented management, with teams working separately on quality measures, utilization management, and care coordination. AI provides a unifying lens by integrating data and workflows across departments, enabling cohesive strategies. For example, bridging population health insights with care delivery workflows ensures interventions happen at the right time and place.

Additionally, AI-driven solutions like intelligent prior authorization (PA) automate what has traditionally been a labor-intensive, frustrating process for payers and providers alike. Gartner notes that organizations implementing AI for PA have dramatically reduced processing times, improving access to care while lowering administrative costs.

Looking Ahead: Transformation Through AI

While many healthcare organizations have begun incorporating AI into their VBC strategies, the Gartner report emphasizes that success requires thoughtful integration into workflows. It’s not enough to invest in the technology; payers must build robust data infrastructure, foster interdepartmental collaboration, and stay ahead of regulatory changes. Furthermore, organizations need to adopt clear measurement frameworks to evaluate AI’s impact on outcomes like reduced length of stay (LOS), improved quality metrics, or savings.

A Final Note: Ensuring Responsible and Transparent AI

As AI becomes increasingly integral to value-based care, ethical considerations must remain at the forefront. The responsible use of AI requires transparency in algorithms, clear communication about decision-making processes, and a commitment to upholding member trust and privacy. Effective governance frameworks should ensure that AI supports - not replaces - clinical judgment and does not result in automatic denials of care. Instead, AI should be leveraged as an enabler that enhances access, equity, and the overall quality of healthcare delivery. By prioritizing ethical standards, health plans can foster greater confidence among members and providers, laying the groundwork for sustainable, trusted transformation in healthcare.

How Vital Data Technology Can Help

At Vital Data Technology, we understand the challenges payers face as they transition to value-based care models. Affinitē, our AI-driven analytics platform, enables payers to take a proactive approach to member health by offering real-time insights that improve care coordination, close gaps, and optimize resource allocation. With the ability to integrate clinical, claims, and SDoH data seamlessly, our solutions empower health plans to enhance outcomes while controlling costs.

Whether you’re looking to reduce care delays through intelligent PA automation or improve member engagement with predictive health analytics, Vital Data Technology is here to partner with you in achieving your goals. Together, we can create a more efficient, equitable, and effective healthcare system.

By positioning AI as the centerpiece of your VBC strategy, you can lead your organization toward better clinical outcomes and competitive advantage. Now is the time to step forward and drive meaningful transformation in healthcare. Contact us to learn more about how we can help your organization.

*  Gartner, Inc. | "AI in Value-Based Care: Part 1 Roadmap Planning for Healthcare CIOs" | By Roger Benn | Published June 27, 2025 | ID G00835449. Access limited to Gartner members.