As we work on building a brighter future for healthcare, one where people are the main focus, value-based care models and AI tools have been two very big topics. These two topics are wonderful examples of us striving to focus on what our patients need and trying to remove some of the workload burden from our providers. So with their goals so closely related to each other, where is the crossover? What role do analytics and AI-driven tools play in aligning payers and providers around value-based care models?
We reached out to our brilliant Healthcare IT Today Community to ask just that, and this is what they had to share.
Sharlie Smith, SVP, Product Management at CliniComp
Analytics and AI are no longer optional – they are essential for aligning payer and provider goals in value-based care. When designed well, and intrinsic to EHR systems, these tools uncover gaps in care, stratify risk, and measure performance in ways that empower care teams and optimize reimbursement models around outcomes, not volume.
Shelley Davis, MSN, RNC, CCM, VP of Clinical Strategy at Lightbeam Health Solutions
At the point of care, it’s all about the data. Both payers and providers are laser-focused on data integration and activation—how to collect, aggregate, and operationalize it effectively and responsibly. That’s why leveraging artificial intelligence (AI) tools that achieve quantifiable ROI and value is essential. So, it is critical to develop strategies that ensure AI and analytics not only deliver value at the system level but also support clinical teams in delivering high-quality, member-centered care.
By combining predictive analytics, automated workflows, and proven population health tools, care teams can seamlessly identify rising-risk members and intervene early to prevent costly life-altering adverse events, health crises, and avoidable admissions. Today’s most advanced care management platforms can now enable real-time communication between payers, providers, and members to optimize treatment plans and member outcomes while maximizing savings. Purpose-built AI-driven platforms and deviceless remote patient monitoring (RPM) can further empower value-based care (VBC) organizations by synthesizing claims, clinicals, SDOH, ePROs, and other data sources to stratify risk, monitor trends, and personalize care at speed and scale. Equally important, these tools empower members to become active participants—and even drivers—of their own healthcare journeys, fostering greater engagement, ownership, and better health outcomes.
Michael Poku, MD, MBA, Chief Medical Officer at Equality Health
Driving meaningful improvements in population health requires a steadfast commitment to value-based care—anchored in deeper, more strategic collaboration between payers and providers. This relationship must evolve beyond transactional exchanges toward a shared vision of proactive, data-informed care that is grounded in evidence, cultural humility, and the unique needs of local communities. Advanced value-based care (VBC) platforms are essential to this shift. By integrating data from EHRs, HIEs, and other sources, and leveraging predictive and prescriptive analytics along with other data science–driven tools, these technologies enable earlier identification of high-risk, high-need individuals and support coordinated, whole-person interventions that improve outcomes and reduce costs. Just as importantly, these VBC platforms establish a common infrastructure that empowers payers and providers to align around health equity—especially in communities that have been historically marginalized or excluded from equitable access to care.
Kevin Ritter, EVP of CareInMotion at Altera Digital Health
The inevitable shift to value-based care (VBC) necessitates a move from volume to value. Areas such as effective patient attribution, risk stratification, performance, and quality reporting play key roles in making VBC models successful. Analytics allows both payers and providers to have visibility across their entire patient populations, identify high-risk patients, effectively delegate risk, and direct patients to the appropriate care venues. AI has the potential to manage and make sense of the large and messy datasets that exist across the healthcare ecosystem, enabling payers and providers to have access to all necessary data to provide effective, quality care.
Rajeev Ronanki, CEO at Lyric
Healthcare is at its inflection point—and AI isn’t just helping us keep up, it’s propelling us forward. This isn’t about marginal process improvements. AI is giving us the power to reimagine the entire healthcare system—from fragmented and reactive to intelligent, transparent, and collaborative. By enabling real-time insights, creating intelligent value, and fostering trusted, zero-friction partnerships between payers and providers, we’re building a more connected ecosystem. And at the heart of it all, we’re making healthcare more human—by removing the noise and putting people back at the center.
David Hamilton, Chief Growth Officer at Zyter/TruCare
When a health plan and a provider group have a Value Based Care (VBC) relationship, new technologies will help enormously. VBC is a partnership between a health plan or payer and a provider group, so the more they can leverage technology to manage VBC processes, the more effective the partnership. Using these technologies is no longer a matter of gaining a competitive advantage — it’s a matter of remaining competitive. As an example, innovative health plans are starting to use AI to predict potential adverse health events in individuals or populations so that early intervention is more likely.
Many of these interventions can be aided by AI agents to help notify providers and members/patients of such potential or to help recruit members into plan-sponsored programs designed to ward off adverse health issues before health issues occur. Identifying adverse health trends in a population and predicting their continued growth and impact on population health enables early, proactive intervention that can address medical issues before they worsen – or occur in the first place. This is especially key for value-based care arrangements, where providers take on the medical cost risk of a defined population and have to act much like a health plan.
The following technologies are key: Health plan adoption of agentic and predictive AI will lessen the prior authorization burden considerably. Agentic AI automates manual steps in the process, enabling immediate authorization of the vast majority of requests. Complex requests that still must go to a health plan clinician for review will be aided by AI “co-pilots” that assist health plan reviewers by scanning massive amounts of clinical text to help determine the appropriateness of a requested medical service. All this results in more immediate approvals and faster turnaround times for complex approvals. By enabling the vast majority of prior auth requests to be approved instantly, we can enable immediate scheduling of the service the provider wants for their patient, and dramatically shorten approval times for complex requests that require review by a nurse clinician or physician at the health plan. Long-term – prior auth requests will be initiated automatically from electronic medical records (EMR) – so provider staff won’t need to “touch” the request at all.
Ahzam Afzal, Co-Founder and CEO at Puzzle Healthcare
Healthcare IT is starting to close long-standing gaps between payers and providers—especially when it comes to high-risk patients transitioning from hospital to home or post-acute settings. When a payer has claims visibility and a provider has clinical insight, but the two aren’t speaking the same language or working from the same timeline, patients get lost in the middle. That’s where the biggest avoidable costs—and the worst outcomes—show up.
We need more platforms that bring both sides into a shared ecosystem, enabling live visibility on risk, escalation needs, and gaps in care plans. In post-acute care, we’ve seen the biggest gains when payers and providers use real-time alerts—like admission discharge transfer feeds, discharge notifications, and medication reconciliation triggers—to collaborate during the most critical 72 hours post-discharge. This window is where AI-enabled interventions can catch a missed referral, flag a deteriorating patient, or prompt a home visit before it turns into a readmission.
Analytics also help conversations between providers and payers go from general incentives to aligned action. Rather than having a general goal to reduce readmissions, providers and payers can now identify which facilities are failing to follow up, which patients are missing transitional touchpoints, and which partners are driving better outcomes, allowing both parties to reward performance, correct variation, and build trust around a shared operating model.
Gayathri Narayan, VP & General Manager of ModMed Scribe at ModMed
Claims denials can eat up countless administrative hours and be incredibly burdensome for smaller practices trying to stay afloat – even more so since payer organizations began using AI to manage and streamline claims processing, which has resulted in increased denial rates. Billing teams can greatly reduce the amount of time spent on claims filing and appeals by deploying AI support from the start to flag any risks of denials and address missing information before submission, essentially leveling the playing field again for billing teams. Ultimately, using AI to cover billing workflow bases early can not only smooth the notoriously bumpy claims process, but it can also help avoid any potential delays in patient care, which plays a role in overall care costs for providers and their patients. As such, leveraging AI in billing can potentially lead to twofold benefits or more in terms of both ROI and quality of care.
Nadia Angelidou, Chief Statistician & VP, Data Science at Net Health
Advanced analytics—and the ML pipelines that power them—do far more than surface insights. They underpin the entire engineering of a value-based contract, acting as the shared language that lets payers and providers negotiate, monitor, and continuously refine incentives. Analytics and AI-driven tools start with real data, not slogans—setting budgets and quality goals that are fair, credible, and agreed on before a dollar is spent. This approach shifts from hindsight to foresight by refreshing risk scores in real time so care teams can help rising-risk patients today, not chase last year’s problems. Built-in governance and transparency ensure everyone sees exactly how performance is measured, avoiding ‘black-box’ debates. Finally, continuous learning adjusts targets as care patterns evolve to keep incentives balanced.
The takeaway: analytics are not an overlay—they are the operating system of value-based care. When built on governed, longitudinal data and sound statistical design, AI-driven tools convert payer and provider objectives into a unified, testable framework where financial risk, clinical outcomes, and equity can be optimized in tandem. Without that rigor, advanced analytics is just math in a slide deck.
Jen Goldsmith, President at Tendo
Analytics and AI are essential in translating the goals of value-based care into day-to-day operations for both payers and providers. These tools help identify high-risk patients, forecast healthcare utilization, and evaluate the quality and efficiency of care delivery. Importantly, they also support transparent, shared performance metrics that both sides can trust. AI-driven tools are increasingly being used to uncover patterns in care gaps, predict avoidable hospitalizations, and recommend interventions, making it easier for providers to improve outcomes while managing costs. When combined with bundled pricing or episode-based payment models, advanced analytics can also help payers and providers jointly identify opportunities for cost-effective, high-value care pathways.
Brian Laberge, Solution Engineer, Health Language at Wolters Kluwer Health
Analytics and AI-driven tools play a pivotal role in aligning payers and providers around value-based care by enabling transparent, real-time tracking of quality measures such as HEDIS scores, Star ratings, and care gap closures. These tools automate the aggregation and normalization of clinical and claims data, ensuring both parties operate from a shared, accurate dataset. By surfacing actionable insights—like missed screenings or medication adherence issues—AI empowers providers to proactively address care deficiencies, while giving payers confidence in performance-based reimbursement. This shared visibility into quality metrics fosters trust, supports compliance, and ensures that financial incentives are tightly coupled with patient outcomes.
Alan Stein, Chief Product & Strategy Officer at HealthEdge
AI is already playing a meaningful role in helping to align payers and providers around value-based care—and its impact is only growing. At its core, value-based care is about collaboration and aligning incentives to ensure patients receive the right care and achieve better outcomes. In this model, payers can’t simply act as transactional processors of claims. They need to be active participants, and one of the key reasons is the breadth of data they have access to. Payers often have a more comprehensive view of a member’s health journey than any single provider. This gives them a unique opportunity to leverage advanced analytics and AI tools to generate actionable insights.
These insights can be used to directly support members or shared with providers to foster a more open, data-driven partnership. In either case, using this information to improve care and outcomes benefits everyone involved in a value-based arrangement. AI and analytics can be applied across a wide range of use cases. Clinically, they can support diagnosis and predict outcomes. From a member engagement perspective, they can personalize communication and equip care managers with the right information at the right time. Operationally, they can enhance core administrative systems—enabling payers to configure complex value-based contracts and adapt to regulatory changes with minimal disruption. We’re only beginning to tap into the potential of AI in healthcare. So perhaps the better question is: Is there any area where AI and analytics won’t play a role?
Jeremy Friese, Founder and CEO at Humata Health
Any conversation right now about aligning payers and providers should include a discussion about prior authorization – a topic that’s getting so much attention because of how frequently it can push them apart at the expense of the patients they’re trying to serve.
From a payer perspective, prior authorization serves an important purpose of preventing unnecessary procedures, which in turn, curbs wasteful spending – both key goals of value-based care. But for providers, there are valid arguments that it increases administrative burden, pulls them away from patient care, contributes to burnout, and adds unnecessary delays to treatment. I know all too well about these challenges because I dealt with them for 20 years as a physician and health system executive. But I also recognize that prior authorization is ingrained in the healthcare system and will not be removed completely.
Since prior authorization isn’t going anywhere, we have a moral obligation to fix it, and AI-driven tools will be a big part of the solution. Frustrations and public outrage about prior authorization are hitting a boiling point, and fortunately, it’s happening at a time when we have the tools to fix it thanks to advancements in AI that didn’t exist just a few years ago. Prior authorization is a perfect use case for AI. It can automate tasks on both sides of the workflow – payer and provider – and it makes it easier for them to quickly understand medical necessity rules. As a result, it can make approvals in seconds, removing administrative roadblocks that can frequently delay patient care for weeks. This removes the burden for everyone and, most importantly, helps speed access to the care patients deserve.
There’s just one critical guideline we need to follow: AI should never be allowed to deny a prior authorization request. It can be used to quickly review the vast majority of requests that are approved. But anything that’s ultimately denied should still be handled by a human.
Matthew Oefinger, Chief Data and Technology Officer at Podimetrics
Analytics and AI tools have changed the healthcare landscape in incredible ways. We’re continuing to see new use cases emerge and growing opportunities for AI, and even more basic analytics, to drive meaningful improvements in patient care. These advancements show up in both direct patient interactions and optimized workflows and treatment pathways for providers. Chatbots are among the more common AI tools used for patient engagement. The information they share doesn’t need to be especially profound; the mere presence of an always-available resource enables engagement in ways that encourage stronger, more refined human interactions between patients and providers.
This deeper engagement gives providers more space to deliver holistic, preventive care, resulting in better outcomes for patients, greater efficiency for providers, and meaningful savings for payers. The premise of value-based care—the idea that we treat patients holistically within a fixed cost structure—can now be monitored and dynamically driven in unprecedented ways. At-risk populations are no longer defined solely by large-cohort actuarial models; instead, we’re seeing finer-grained cohorts and more adaptive AI-driven approaches for monitoring and controlling risk throughout a patient’s journey.
What great answers! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of their day to read this article! We could not do this without all of your support.
What role do you think analytics and AI-driven tools play in aligning payers and providers around value-based care models? Let us know over on social media, we’d love to hear from all of you!
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