He explores how Lumeris is redefining primary care through “Tom,” an agentic AI platform that enhances access, streamlines workflows, and empowers physicians with real-time insights. From managing chronic conditions to addressing workforce shortages, Dr. Carmouche outlines a future where technology enables scalable, patient-centered care.

It’s a powerful conversation on innovation, empathy, and the policy changes needed to truly transform healthcare delivery.

To learn more about Tom and Lumeris, contact us.

References:

https://medicalinnovationpod.com/

Spotify: https://open.spotify.com/episode/1NsXcKr5cGLFA60s01Jr6q?force_isolation=true

Apple Podcasts: https://podcasts.apple.com/us/podcast/the-medical-innovation-podcast/id1784368272?force_isolation=true

The post The Medical Innovation Podcast: Reimagining Primary Care with Agentic AI (Lumeris) appeared first on Lumeris.

Unlocking Hidden Value in Healthcare Data 

Healthcare generates more data than any other sector—yet less than 3% is effectively used.² Inefficient systems incapable of processing multi-modal data force clinicians to manually sift through charts and records, slowing care and straining resources. Agentic AI, powered by secure and scalable infrastructure, harmonizes disparate data streams into usable insights. This shift frees clinicians from paperwork and channels the hidden value of data directly into patient care. 

Modernizing Core Infrastructure for Flexibility and Agility 

Legacy IT systems cannot keep pace with agentic AI. By 2025, 94% of organizations will operate across multiple cloud environments, while 79% will revert some services back on-premises for control—underscoring the importance of hybrid, adaptable infrastructure³. Elastic computing, low-latency connectivity, and scalable storage are essential. Deloitte projects that AI-driven workloads will increase by at least 20% across every IT environment in the next year⁴. To support this demand, IT leaders must modernize infrastructure today to ensure reliability tomorrow. 

Protecting Patient Trust with Enterprise-Grade Security 

AI adoption in healthcare depends on trust. HIPAA compliance and data protection must be embedded into every system. A recent framework for HIPAA-compliant agentic AI highlights the role of attribute-based access control, PHI sanitization, and immutable audit trails as safeguards against breaches⁵.  

Beyond compliance, enterprise-grade protections such as zero-trust architectures, advanced encryption (at rest and in transit), and continuous threat monitoring are essential to defend against increasingly sophisticated cyberattacks targeting healthcare organizations. Embedding these principles into infrastructure design not only reduces risk and prevents costly breaches but also ensures that AI systems can be scaled confidently across clinical and operational domains.  

When security is treated as the foundation of innovation rather than a barrier, health systems gain a critical advantage: the confidence of patients, providers, and regulators. Trust is the currency that enables safe, scalable AI adoption, and it is earned through uncompromising commitment to security at every layer of infrastructure. 

Scaling for Resilience 

Healthcare demand fluctuates dramatically—from seasonal surges to pandemic spikes that can strain every corner of the system. For CIOs, CMIos, CTOs, and other IT and IT-adjacent leaders, the ability to scale is not just about handling more data or ensuring adoption by more users but rather ensuring that critical services remain available when patients and clinicians need them most.  

Elastic infrastructure as a safety net: Scalable, cloud-based infrastructure allows health systems to expand or contract dynamically, minimizing downtime while maintaining continuity of care. For IT leaders, scalability is not optional; it is the foundation of resilience. 

AI-specific scaling requirements: Agentic AI workloads often require rapid bursts of compute power for tasks such as analyzing multi-modal patient data, generating treatment insights, or orchestrating best next action and other multi-step workflows across systems. Without resilient scaling, these models can stall and create delays in clinical decision-making. 

Resilience beyond volume: Scalability underpins disaster recovery and business continuity planning. Health systems with elastic, distributed infrastructure can reroute workloads and maintain performance even when local systems are compromised by cyberattacks, natural disasters, or hardware failures.  

Strategic imperative: As IDC emphasizes without scalable infrastructure, even the best AI use cases fail to achieve mission-critical impact⁶. Resilience is not an “add-on”; it’s the foundation that allows Agentic AI to operate reliably under pressure, delivering consistent value across clinical and operational settings.  

In short, scalability ensures resilience, and resilience is what transforms agentic AI from an experimental tool into a trusted partner in healthcare delivery. 

Demonstrating ROI Through High-Impact Use Cases 

AI succeeds when it solves real problems quickly. By starting with targeted high-value workflows such as revenue cycle optimization, patient scheduling, or clinical documentation, IT leaders can demonstrate immediate impact. Early wins generate financial savings while also building organizational confidence to expand AI adoption. Research underscores this “start small, scale fast” approach. Modular, platform-based AI allows organizations to deploy discrete agents in focused areas, then expand iteratively. This modularity creates scalability, resilience, and adaptability, since individual agents can be updated without disrupting the entire system¹. 

By choosing practical, high-ROI use cases first, health systems prove value, reduce resistance, and establish the foundation for broader AI success. 

Driving Adoption with Workflow Integration and Observability 

Clinician adoption determines AI success. Even the most advanced models will fail if they disrupt established care processes or require clinicians to leave their core tools. AI solutions must be embedded seamlessly into EHRs and operational systems, enhancing workflows rather than disrupting them. Modular standards-based integration increases adoption likelihood and long-term improvement¹. Yet adoption alone is insufficient.  

However, adoption without reliability is fragile. With 78% of global organizations already relying on AI, infrastructure observability, continuous monitoring of GPUs, latency, and throughput, is critical to maintainperformance¹.  

Equally important is the fusion of observability with governance. Monitoring by itself highlights problems, but governance frameworks ensure accountability, auditability, and alignment with clinical and ethical standards. Together, workflow integration, observability, and governance create a feedback loop: clinicians adopt and use AI confidently, IT teams maintain high performance, and executives see measurable return on investment. 

Combining workflow integration with observability and governance ensures resilience, adaptability, and sustained competitive advantage for health systems embracing Agentic AI.  

Conclusion 

By 2028, 33% of enterprise software applications will incorporate agentic AI, up from less than 1% in 2024, allowing 15% of daily work decisions to be made autonomously.⁷  This can transform healthcare operations and patient care, but only if it is supported by infrastructure that is modern, secure, scalable, and observable. For health system IT leaders, infrastructure is no longer an afterthought; it is the strategic enabler of success. The question is no longer whether to modernize, but whether your organization is ready now. The future of AI leadership in healthcare will be defined not by those who experiment first, but by those who build the strongest foundations. 

References 

1. TechRadar. (2025). The need for robust AI infrastructure. TechRadar Pro. https://www.techradar.com/pro/the-need-for-robust-ai-infrastructure 

2. GE HealthCare. (2024, June). How agentic AI systems can solve the three most pressing problems in healthcare today. https://www.gehealthcare.com/insights/article/how-agentic-ai-systems-can-solve-the-three-most-pressing-problems-in-healthcare-today 

3. PwC. (2025). Google Cloud and PwC: Healthcare AI insights. PwC. https://www.pwc.com/us/en/technology/alliances/google-cloud/healthcare-ai-agents-solutions.html 

4. Deloitte. (2024). Growing demand for AI computing: The impact of AI-driven workloads on infrastructure. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/emerging-technologies/growing-demand-ai-computing.html 

5. Neupane, A., et al. (2025). A framework for HIPAA-compliant agentic AI systems. arXiv preprint. https://arxiv.org/abs/2504.17669

6. IDC & AMD. (2025). Infrastructure challenges for scaling AI: IDC Spotlight Report. AMD. https://www.amd.com/content/dam/amd/en/documents/solutions/data-center/insights/enterprise-ai-insights-infrastructure-challenges-scale-ai-idc-spotlight.pdf 

7. Lovejoy, K. (2025, September 11). Agentic AI: The reality behind the hype. Kyndryl. https://www.kyndryl.com/us/en/about-us/news/2025/09/agentic-ai-fact-vs-fiction 

The post Infrastructure Priorities for Agentic AI Success for Healthcare IT Leaders appeared first on Lumeris.

As AI becomes more engrained in healthcare operations, health system leaders must evaluate solutions with the same rigor applied to clinical and financial decisions. A Health System’s Guide to Evaluating AI Solutions provides a primary care–first framework for assessing AI based on clinical relevance, workflow integration, scalability, and governance.

Developed by Lumeris clinical and strategy leaders, the guide helps executives make confident, defensible AI decisions that support long-term care transformation. It offers practical guidance for moving from experimentation to enterprise impact.

Key Learning Points

• How to evaluate AI solutions using a structured framework aligned to primary care delivery and health system strategy
• The core criteria leaders should apply when assessing AI vendors, including clinical impact, operational fit, scalability, and governance
• What separates scalable, enterprise-ready AI from point solutions that fail to deliver sustained value
• How thoughtfully deployed AI can help strengthen primary care capacity and help improve outcomes
• How to evaluate AI vendors with a useful takeaway checklist that has been thoughtfully curated with experts in the space

The post eBook: A Health System’s Guide to Evaluating AI Solutions appeared first on Lumeris.

By transforming automation from a set of disconnected tools into a unified strategic capability, Tom from Lumeris makes this balance achievable. Designed as an extension of the care team, Tom transforms automation from isolated task execution into a strategic capability that supports intelligent, coordinated action. By seamlessly integrating into existing workflows, Tom helps primary care teams take the best next action, enhancing efficiency, strengthening patient relationships, and delivering measurable results across the enterprise.

Keep reading to learn how Tom, built for healthcare, integrates seamlessly into existing workflows, preserving clinician trust while delivering consistent, enterprise-scale results.

The Strategic Inflection Point for Digital and Technology Leaders

After years of layering tools atop legacy systems, the industry is finally recognizing that complexity is not innovation, it’s a barrier. The most forward-thinking organizations and leaders are moving beyond incremental technology upgrades toward unified, intelligent systems that anticipate needs, learn continuously, and act with precision. This is a marked shift from fragmented point solutions to unified, intelligent systems that drive the best next action for care.

Organizations that adopt an “automation-first mindset,” embedding automation into every clinical and operational initiative, achieve faster, more scalable results.¹

For digital and technology leaders, this shift represents more than modernization; it redefines operational leadership. It fundamentally transforms how care is delivered and sustained. Tom supports this evolution by uniting EHRs, scheduling, and care management systems under a single, secure framework. The result is simplified infrastructure, reduced redundancy, and a more agile, connected primary care network capable of continuous improvement.

Healthcare’s next wave of transformation won’t be driven by more technology. It will be driven by smarter connection. Leaders who treat automation as a strategic capability, not just a tool, will open the door for a new kind of efficiency. One that amplifies human potential instead of replacing it. Leaders redefining healthcare aren’t asking what can be automated, they’re asking how automation can collaborate.

From Task Automation to Intelligent, Team-Based Best Next Action

Traditional automation executes fixed tasks and focuses on process completion and even labor replacement. Limited to doing what it’s been told. But today’s healthcare challenges demand something more nuanced, more strategic, and far more human.

Tom represents that evolution was built for it. It moves beyond static task automation to agentic intelligence, automation that understands context, anticipates need, and delivers the best next action. Instead of automating for efficiency alone, it automates for real impact and outcomes, taking informed action for every care team member, patient, and unique situation.

Chief Intelligence Officers are embedding this kind of operational intelligence directly into workflows, blending human expertise with intelligent systems that act autonomously within trusted parameters.²

Tom embodies this collaboration. It works alongside care teams, not in place of them, to proactively surface the best next action needed to close gaps in care, help manage patients with chronic conditions, and provide clinical guidance for the care team to evaluate. Tom has prevention and wellness agents that are proactively scanning all available data to determine what services each patient may need, such as breast cancer, colorectal, or cervical screenings. Tom also supports care coordination, like annual wellness visit scheduling, pre-visit check-in calls, as well as chronic condition management. Tom can proactively determine if a diabetic patient is due for an eye or foot exam, capture self-reported blood sugar readings, and support other conditions like hypertension and depression. Tom doesn’t stop there; medication adherence is another area where the agentic AI is proactive. The best part is that this doesn’t happen in a vacuum, Tom makes call summaries and transcripts available and can even put forth clinical recommendations. By automating routine tasks that the care team simply does not have time to do across an entire panel, Tom enables clinicians to focus on what matters most: building stronger patient relationships, exercising clinical judgment, and delivering higher-quality outcomes.

The new era of automation isn’t about replacing human intelligence, it’s about augmenting it at scale. Leaders who design automation to think with their teams, not just for them, will redefine what performance and compassion look like in modern healthcare.

The future of healthcare innovation isn’t going to come from more disparate tools and point solutions, it’s going to come from technology that feels effortless, human, and built around how care really happens.

Improving Access and Clinician Efficiency Without Adding Complexity

The future of digital transformation depends on simplifying workflows and removing barriers, not adding layers of technology for the sake of adding technology. For most health systems, the problem isn’t a lack of tools, it’s that too many of them operate in silos, creating friction, fatigue, and inefficiency. Clinician burnout and administrative overload drain resources and morale, even as digital investments increase, and prevent physicians and care teams from doing their best work. Embedded automation, such as ambient documentation and intelligent workflow support, is already demonstrating measurable impact.³

Tom builds on this foundation and greatly enhances this model by integrating directly into existing primary care workflows. It automates patient outreach, follow-up scheduling, and referral coordination within the tools clinicians already use. Tom doesn’t operate in the background; it works alongside the care team, ensuring every actionable step is surfaced, completed, and documented without disrupting clinical flow.

For clinicians, that means fewer clicks and less cognitive load. For technology leaders, it means simplified infrastructure and measurable ROI. The result is greater access, higher throughput, and a more connected patient experience, all while reducing administrative friction, reducing cognitive load and IT overhead.

The real measure of digital transformation isn’t how much technology you add, it’s how much easier you make it for clinicians to care for people. When automation truly supports the flow of care, everyone benefits. The patient, the clinician, and the system as a whole. And as healthcare grows more connected and intelligent, one principle must remain constant: trust has to be built in from the start.

Security, Governance, and Trust by Design

Every advancement in healthcare depends on trust, and that trust must be earned through transparency, compliance, and control. Digital executives increasingly recognize that success in automation requires explainable, auditable systems with built-in governance.² For digital and technology leaders, this isn’t just a compliance requirement, it’s a leadership imperative.

Tom is built to meet that standard. With SOC-2 and HIPAA-compliant infrastructure, full audit trails, and configurable human oversight, Tom delivers intelligent automation without compromising safety. This means organizations can scale autonomous agentic intelligence confidently, knowing it operates within trusted, ethical boundaries. Supported by Lumeris’ long-standing commitment to ethical data stewardship and value-based care, Tom provides the clarity and confidence needed to scale responsibly. It gives leaders the visibility and governance they need to drive innovation responsibly.

Trust is the currency of digital transformation. The leaders who build automation with transparency and accountability at its core won’t just deploy smarter systems — they’ll earn the confidence of every clinician and patient they serve.

With trust as the foundation, the next step is connection — because intelligent automation can only scale when every system speaks the same language.

Integration and Interoperability as a Strategic Advantage

For CDOs, CTOs, CDIOs, and CAIOs, and innovation executives, interoperability is about breaking down silos to drive system-wide intelligence and turning integration into a source of competitive advantage.

As care delivery becomes increasingly digital and rarely unified, interoperability has become the cornerstone of sustainable transformation and the new measure of maturity. Fragmented vendor ecosystems lead to redundant workflows, data silos, and operational blind spots that slow innovation and inflate costs.

Tom was designed with interoperability at its core, using standards-based APIs that connect seamlessly across EHRs and existing systems, creating an intelligent layer of automation across the enterprise. By harmonizing data and unifying workflows, Tom prevents technology sprawl, reduces redundancy, and enables leaders to modernize confidently while maintaining operational continuity. With Tom, interoperability isn’t a technical checkbox; it’s a leadership lever for agility, scale, and sustainable transformation.

Interoperability is about coordination, clarity, and competitive edge. Leaders who make integration a strategic priority unlock the full power of automation, turning fragmented systems into a unified engine for better care and smarter growth.

Integration lays the foundation — but impact is proven through results that matter to clinicians, patients, and the bottom line.

Measuring What Matters: Real ROI and Impact

Success is no longer measured by the number of automations deployed, it’s measured by the outcomes they create. Healthcare leaders are increasingly focused on measuring automation’s tangible impact. Experts recommend beginning with high-volume administrative workflows to demonstrate early wins and build organizational trust.¹

Tom can deliver measurable improvements in panel size increase, primary care access, physician capacity, care coordination efficiency, and more. Its self-learning engine compounds these gains, continuously optimizing processes. Rather than treating ROI as an afterthought, Tom and the Lumeris team make it part of the lifecycle. This enables technology and operations leaders to connect automation investments to the outcomes that matter most: clinician time, patient access, and overall system resilience.

ROI in primary care agentic AI is all about creating capacity that moves the mission forward. The leaders who measure impact in both human and financial terms will set the new standard for sustainable, intelligent transformation.

As these results take hold across the organization and health systems in general, a new kind of intelligence is emerging, one that helps people and technology work in rhythm to drive meaningful change.

The Path Forward: Building a Foundation for Agentic Operations

Next-generation automation is the foundation for a more agile, data-driven, and sustainable primary care model. Automation is no longer a project; it’s a mindset. Leading organizations are embedding automation into every initiative, transforming how care is delivered, documented, and supported. It’s more than implementing automation that operates disparately to accomplish singular tasks and doesn’t connect data and actions, implementing autonomous agentic AI into primary care is the driver that will differentiate your health system, free up access for patients, and expand physician and care team capacity in a meaningful way.

Success is as much about culture as it is about code, requiring leadership alignment, cross-functional collaboration, and phased adoption.¹ The systems that thrive will be those where digital leaders champion both the technical and human dimensions of transformation.

Tom supports this shift by serving as the operational backbone for transformation. By embedding automation, analytics, and governance into one cohesive system, it enables health systems to evolve from managing workflows to managing outcomes.

Backed by Lumeris’ expertise in value-based care and primary care transformation, Tom helps leaders achieve sustainable transformation, aligning technology strategy with measurable performance and better patient outcomes.

Conversation Starters for Digital Leaders

The shift toward agentic AI marks a new phase of digital transformation, one where automation acts intelligently, adapts in real time, and supports care teams at every level. To capture its full potential, leaders must foster alignment across clinical, operational, and technology functions. These questions are designed to spark meaningful dialogue, guide strategy, and identify the next opportunities to scale agentic AI across primary care. Here are a few conversation starters:

• How are we measuring the impact of automation on clinician experience and access?
• Are we treating AI as a product or as a system capability?
• Where can agentic automation relieve our heaviest administrative burdens today?
• What data or governance gaps do we need to close to safely scale agentic AI?

Learn more about Tom by Lumeris, and how agentic AI can become an extension of your primary care team.

Schedule a Tom Demo

References

1. Duffy, R. (2025, September 25). The automation-first mindset in healthcare: Key considerations for the AI era. HIT Consultant. https://hitconsultant.net/2025/09/25/the-automation-first-mindset-in-healthcare/
2. Becker’s Hospital Review. (2025, October). What’s the next tech-focused role in the health system C-suite? Becker’s Healthcare. https://www.beckershospitalreview.com/healthcare-information-technology/
3. Becker’s Hospital Review. (2025, October). The tech shaping healthcare in 2025, per 62 leaders. Becker’s Healthcare. https://www.beckershospitalreview.com/healthcare-information-technology/

The post Why Agentic AI Will Redefine How Primary Care Works appeared first on Lumeris.

Yet within these pressures lies a powerful opportunity. Primary care transformation, supported by agentic AI, is an effective and often overlooked way to align financial sustainability with clinical excellence. Unlike generative AI, which provides insights, agentic AI acts inside workflows to expand capacity, reduce avoidable utilization, and improve outcomes.⁵ This shift enables healthcare leaders to simultaneously strengthen financial performance and elevate patient care.

Healthcare leaders are presented with new ideas every day for how to grow revenue, improve margins, gain market share, and manage costs. The path forward is not a choice between financial strength and clinical excellence. Primary care transformation shows that improving financial outcomes is inseparable from improving clinical outcomes, and the most transformative solutions achieve a balance of both.

Primary care transformation is a smart, viable strategy that forward-thinking executives are already beginning to explore. While many organizations are experimenting with AI, generative AI will only advance your priorities so far. The future belongs to primary care transformation enabled by agentic AI.

This brief will explore why investing in primary care transformation is a high-impact strategy for healthcare leaders seeking sustainable revenue growth, stronger margins, expanded market share, and tighter cost control.

Revenue Growth Through Stronger Patient Engagement

Revenue capture remains a top opportunity for healthcare leaders, however many health systems are losing potential patient revenue to competitors. Leakage and fragmented engagement continue to erode financial performance. For CEOs and CFOs, this represents not only lost revenue but also a missed chance to strengthen long-term patient relationships that build loyalty and utilization.

Unlike traditional AI, which surfaces insights for leaders to interpret, agentic AI acts autonomously within workflows. It engages patients directly with timely, personalized outreach, closes gaps in preventive care, and supports continuity between visits. This ensures revenue growth is rooted in long-term patient value rather than isolated encounters.

Agentic AI brings together data across the enterprise including clinical records, pharmacy activity, claims, and social factors to create a complete view of the patient. With this foundation, it can tailor engagement so that it feels relevant and human instead of transactional or generic. Research shows that personalized outreach increases adherence to preventive care and boosts satisfaction, both of which directly strengthen downstream revenue.

With these capabilities, health systems can anticipate patient needs, close care gaps, and expand capacity and staffing for profitable services. For executives operating in an environment where one in four healthcare CFOs report that operating margins have fallen short of goals over the past three years,⁶ primary care transformation powered by agentic AI provides a scalable path to sustainable revenue growth and margin stability.

Margin Protection and Expansion

Margin protection and expansion are consistent priorities for healthcare executives. Traditional models often tolerate inefficiencies as long as billing offsets costs, but this cycle is increasingly unsustainable as reimbursement tightens and costs rise. For CEOs and CFOs, breaking this cycle is essential to building financial resilience. Preventive primary care reduces avoidable admissions and readmissions, while coordinated care lowers duplication and unnecessary utilization. When combined with agentic AI, these benefits scale even further.

Agentic AI can streamline both clinical and administrative workflows by automating documentation, eliminating redundancies, and intelligently routing patients to the most appropriate and cost-effective services. This reduces workforce strain, improves operational efficiency, and ensures resources are aligned with revenue-generating activities.

For CFOs, the impact is clear: higher-margin revenue opportunities emerge without the proportional increase in costs that typically erode returns. Executives are already recognizing this shift. Surveys show CFOs increasingly view AI not merely as a tool for efficiency but as a driver of sustainable growth and profitability.⁷ For health system leaders under pressure to do more with less, margin expansion through agentic AI represents a practical, scalable solution.

Market Share and Competitive Differentiation Through Patient Care

Market share is one of the most visible measures of CEO and CFO performance, directly tied to organizational growth and leadership stability. Once lost, it is extraordinarily difficult to recover, making proactive strategies essential to success. Primary care serves as the decisive differentiator in competitive markets, particularly in metropolitan areas where multiple health systems compete for the same patient base. Expanding access, improving coordination, and embedding agentic AI into primary care delivery allows executives to move beyond traditional referral management toward durable patient loyalty. To attract and retain patients and grow market share, health systems need to create a competitive advantage that rivals cannot easily match. While many healthcare leaders are using generative AI as a solution to drive efficiency and improve market share it is health systems that use Agentic AI systems that will be able to grow market share through improved patient care.

Market share in today’s environment is shaped primarily by two levers: reducing leakage and increasing panel size. Leakage, where patients seeking care outside the system erodes both revenue and loyalty. By strengthening referral pathways, improving care coordination, and ensuring a seamless patient experience, health systems can improve keepage, retaining more care in-network. Higher patient satisfaction further reinforces this cycle, driving durable loyalty and protecting market share.

Panel size is equally important. With clinician shortages and mounting patient demand, most systems are constrained in their ability to expand. Agentic AI supports transformation here by automating routine workflows and coordinating care across the continuum. By freeing provider capacity, reducing administrative burden, and supporting chronic disease management, health systems can safely increase panel size without overwhelming clinicians. More patients can be served, and each receives more consistent, proactive care strengthening both access and loyalty.

For healthcare leaders, the result is sustained revenue streams, stronger patient loyalty, and long-term financial positioning. Systems that embrace primary care transformation with agentic AI will be positioned to capture and grow market share, even in crowded and highly competitive markets. With agentic AI solutions health systems can deliver accessible, patient-centered primary care supported by intelligent technology that guides the best next action for the patient that won’t delay care which drives worsening outcomes.⁸

Operating Cost Management: Efficiency as a Driver of Growth

Operating costs remain one of the most stubborn challenges for health system executives. Inefficient, siloed profit centers and heavy administrative overhead drain resources that could otherwise be invested in patient care and strategic growth. For healthcare leaders, these inefficiencies erode value and make it difficult to sustain margin stability. Primary care transformation offers a coordinated model that cuts across silos, optimizes resource allocation, and eliminates duplication. When supported by agentic AI, these gains expand significantly.

Agentic AI acts inside workflows to streamline care coordination, support chronic disease management, and reduce unnecessary duplication of services. By automating follow-ups, triaging patients to the most appropriate care, and ensuring continuity between visits, agentic AI frees provider time and reduces administrative burden. This additional capacity allows leaders to expand panel size, serve more patients without proportional cost increases, and lower reliance on high-cost, avoidable interventions.

Equally important, AI-driven care coordination reduces unnecessary utilization and ensures patients receive the right care at the right time. These models lower the total cost of care while maintaining quality. In rural markets, where shortages are especially severe,⁴ such efficiencies are not only essential to financial resilience but also critical to ensuring equitable access to care.

Executive Call to Action

Primary care transformation supported by agentic AI can give healthcare executives control over aspects of the four levers that matter most: revenue growth through stronger patient relationships, margin expansion through efficiency and prevention, market share gains through differentiation, and operating cost reductions through automation and coordination. Together, these outcomes create greater capacity to serve more patients, improve resource utilization, and build organizations that can thrive in uncertain markets.

For healthcare leaders, the imperative is clear. Primary care transformation, enabled by agentic AI, proves that revenue growth, margin stability, market share expansion, and cost control are not competing objectives but mutually reinforcing ones. The systems that act decisively will secure sustainable growth, enhance financial resilience, and position themselves as market leaders. Those that wait will face the risk of being outpaced by competitors who make primary care the strategic engine of both clinical and financial performance.

Agentic AI also creates a future where proactive engagement, personalized care, and scalable capacity are not aspirational goals but operational realities. By embedding intelligence into daily workflows, health systems can reduce readmissions, increase patient satisfaction, and strengthen long-term loyalty. For healthcare leaders, this means not only balancing financial stability with clinical excellence but advancing both at the same time.

The question for today’s executives is no longer whether to transform primary care, but how quickly they can bring this transformation to scale.

Schedule a Tom Demo

References

1. The American Journal of Medicine. (n.d.). Physician burnout leading to workforce attrition. https://www.amjmed.com
2. AAMC. (n.d.). Aging population increasing demand for physicians. https://www.aamc.org
3. ODPHP. (n.d.). Delayed care resulting in worse health outcomes. https://odphp.health.gov/healthypeople/priority-areas/social-determinants-health/literature-summaries/access-primary-care
4. National Library of Medicine. (2023). A shortage of doctors in rural America. PubMed Central. https://pmc.ncbi.nlm.nih.gov/articles/PMC1071163/
5. PwC. (2024). Agentic AI: The new frontier in generative AI—An executive playbook. https://www.pwc.com/m1/en/publications/documents/2024/agentic-ai-the-new-frontier-in-genai-an-executive-playbook.pdf
6. Deloitte. (2024). Healthcare CFOs embrace comprehensive approach to profitability. https://www.deloitte.com/us/en/insights/industry/health-care/health-care-cfos-embrace-comprehensive-approach-to-profitability.html
7. Salesforce. (2024). CFOs invest in AI for growth. https://www.salesforce.com/news/stories/cfos-invest-ai-for-growth/
8. Office of Disease Prevention and Health Promotion. (n.d.). Access to primary care. Healthy People 2030. U.S. Department of Health and Human Services. Retrieved from https://odphp.health.gov/healthypeople/priority-areas/social-determinants-health/literature-summaries/access-primary-care

The post Primary Care Transformation: A Strategic Lever for Revenue, Margin, Market Share, and Cost Control  appeared first on Lumeris.

In healthcare, in the messy, overbooked, “why is my 9 am. already 20 minutes behind?” reality of care delivery, the real revolution isn’t about replacement. It’s about relationships. Between clinicians and algorithms. Between data and decisions. Between what machines can compute and what humans care about. The future is human plus AI. And that equation adds up to something more symbiotic than sci-fi.

Tom™, our AI-powered Primary Care as a Service (PCaaS) platform, already behaves less like a passive analytics engine and more like a colleague who quietly handles the work 24/7. It monitors panels, follows established guardrails and guidelines to execute best next actions, coordinates outreach, and escalates when something looks off. But the magic isn’t that Tom acts; it’s how Tom fits into the human team.

From AI Coexistence to AI Collaboration

We’ve already seen the first act. AI reads X-rays. It drafts clinical notes. It reminds you when a patient might be overdue for a check-up. Useful, yes, but still a bit mechanical. What’s coming next is deeper integration, where human expertise and autonomous systems work together in real time, swapping signals like a well-rehearsed band. 

Imagine a primary care ecosystem where AI systems anticipate care gaps, flag nuanced risks, and surface context not because they know more than clinicians, but because they see differently. The physician, meanwhile, brings interpretation, empathy, and nuance to the irreplaceable human parts of medicine. Together, the care teams and AI systems form a feedback loop that’s greater than the sum of its algorithms. 

A normal Tuesday morning might look like this: while the clinician finishes an encounter, Tom has already cleaned up the panel, teed up chronic care outreach, flagged a few high-risk patients for same-week touchpoints, and drafted follow-up tasks for the team. No manual bottlenecks preventing autonomous tasks from being executed seamlessly, and within scope. Just a quiet sense that the day is oddly…manageable.

Designing for Care Delivery Symbiosis, Not Supervision

Too often, we talk about “AI oversight” as if the goal were to babysit technology that’s perpetually misbehaving. But what if we designed systems to collaborate naturally with humans, not to be micromanaged by them? 

In a symbiotic healthcare model, both sides adapt. AI learns from human corrections, tone, and priorities. Clinicians, in turn, leverage AI’s strengths and stamina without surrendering judgment. One navigates while the other keeps everyone safe and comfortable. 

On the surface, the care team doesn’t see a swarm of agents. They see a coherent workflow: a queue of prioritized tasks, pre-assembled lists of who needs what, and outreach that just…happened, without someone staying past 7 p.m. to click “send.” The choreography is invisible; the relief is not.

The AI Workflow Frontier

A truly symbiotic system doesn’t just “support” workflows; it redesigns them. The future clinical encounter could look less like filling out templates and more like a dynamic dialogue among patient, clinician, and AI assistant. 

Picture this: as a patient speaks, the AI synthesizes real-time insights from medical history, and environmental data. By the time the conversation ends, a personalized care plan is already taking shape – evidence-based, human-approved, and ready for action. That’s the kind of workflow where everyone wins, and no one burns out.

The New Rules of Engagement

If we want human-AI collaboration to feel natural instead of nerve-wracking, we need more than technology and trust. We need operating principles, a social contract for how this all works in practice. Here are a few principles that are emerging in AI-native primary care environments: 

1. No unreviewable decisions in high-risk care. 

While all AI-driven decisions must remain reviewable from a governance perspective, high-risk actions require explicit human review for execution. Once a clinical or ethical threshold is reached, these high-stakes decisions must be over-rideable and clearly traceable to both the data and the clinicians responsible for them. 

2. Humans own values; AI owns variance. 

Clinicians define goals, trade-offs, and what “good” looks like in their population. AI hunts for outliers, risks, care gaps, and missed opportunities across millions of data points. It’s not there to decide what matters, only to make sure we don’t miss it. 

3. Every AI action leaves a breadcrumb. 

When Tom schedules an appointment, triggers outreach, or flags a concern, it leaves a clear, human-readable explanation. A short, defensible “because” that clinicians can glance at and either accept or reject without an existential crisis. 

4. Clinicians can always say no, and the system learns from it. 

Overrides are not treated as disobedience. They are treated as feedback. If clinicians keep rejecting the same type of suggestion, the system should adapt. In a healthy ecosystem, “no” is not the end of the conversation; it’s the tuning mechanism.

The Punchline: AI Enabled Care Is Still About People

The irony of AI in healthcare, is the more advanced the technology becomes, it actually depends on the human element to succeed – requiring care teams to evaluate the technology, depending on them to show compassion and use technology responsibly, and ultimately putting their trust in AI. 

At Lumeris, we see this future not as fantasy but as a design challenge how to make technology empathetic enough to earn its place in the clinical circle of trust. In that vision, the best AI doesn’t just make care smarter; it makes it more human. 

After all, the end goal isn’t to automate healthcare it’s to elevate it. And if that means AI plays second fiddle in the world’s most important duet, that’s a tune worth practicing.

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Extending high quality primary care to all Americans will require massive transformation of the prevailing care model and redesign of payment models to support it. Modern technology, specifically generative Al, now allows us to clearly envision a world where primary care is readily accessible to all – affordable, highly effective, proactive, personalized, and empathetic.

In this future vision, primary care transforms from a transactional series of office visits into a continuous ambient monitoring service where the “doctor’s office” is primarily a digital command center. In-person care becomes a high-precision logistical event, deployed only when physically necessary. “Going to the doctor” is replaced by “being with the doctor.” Al provides surveillance, novel financing provides incentives for prevention and innovation, and the human care team provides empathy and strategy.

Patient panels of 2,000 become 5,000 or greater. Valuable human capacity is used wisely. Patients get 24/7 access, convenience, and highly personalized care. Health outcomes improve and costs lower. This vision is no longer science fiction – this future is now.

The Agentic AI Unlock: Introducing Tom™

In early 2025, Lumeris introduced Tom, an Al operating system for primary care, and a new category of software – Primary Care as a Service (PCaaS). Built on over a decade of operating experience as a multi-population risk manager for millions of patients, working in partnership with many of America’s flagship health systems and large medical groups, Lumeris was an early adopter of generative Al using natural language to automate care processes.

The vision for Tom is to augment primary care physicians (PCPs) and their care teams by introducing the platform as an agentic Al “member of the care team” – charged with calculating for each patient in a primary care setting the “Best Next Action” to improve their health outcomes. Instead of handing a “gaps in care list” to a human, Tom autonomously takes the action – proactively outreaching for a cancer screening or vaccination, scheduling a post-hospital discharge follow-up visit, checking in on a patient with hypertension and diabetes to collect biometric data and ensure medication adherence, assessing symptoms, and hundreds of other clinically-relevant services common to primary care.

Tom also interacts with the primary care team – summarizing patient interactions and surfacing novel insights to the care team, providing “contextual” clinical decision support to providers at the point of care, and prioritizing patients that might need the practice’s attention. Strategic aggregators of primary care, namely health systems, quickly saw Tom as the tool that could finally allow them to imagine larger panels per PCP, drive better access, improve care model performance, declutter lower-acuity care from specialists while repatriating care back into primary care, and preserve continuity of downstream care within their own health systems.

Tom allows users to more clearly imagine the future of primary care.

Primary Care 2030: From Vision to Reality

The actor Robert Young’s portrayal of Marcus Welby, MD in the ABC television drama from 1969 to 1976 created the image for what became the cultural shorthand for the idealized, benevolent, all-knowing family doctor. Dr. Welby became the gold standard against which real-life primary care physicians were measured for decades. The nostalgia stems from a specific set of behaviors and a model of care that has become increasingly rare in modern medicine.

The image of Marcus Welby, MD persists as a powerful symbol of human connection in medicine. In an era of 15-minute appointment slots, electronic health records, and fragmented specialist care, Welby represents the desire to be known by one’s doctor, not just treated. Tom represents the modern technology-enabled opportunity to transform today’s sub-optimal experience of “going to the doctor” to “being with the doctor” – with many of the characteristics of a time that, until now, seemed forever lost in the past.

Features of “Nostalgic PCP”

Unrestricted Availability (The House Call)

A most enduring visual of Dr. Welby walking up a patient’s front path with black leather medical bag in hand, seemingly at any hour of the day or night.

The “Whole Person” Approach

Where the physical symptoms were treated within the context of the patient’s entire life cycle, and where Dr. Welby acted as doctor, therapist, social worker, and even father figure.

Holistic Care

Dr. Welby didn’t refer patients to specialists for every minor issue and managed most of the care himself.

Benevolent Paternalism

The character exuded a calm, authoritative demeanor, sat down and listened without looking at his watch (or computer screen), and offered reassurance that he would personally see the problem through to the end.

The Patient Experience: “Always-On” Care

Let’s envision the future with Tom. In the new model, the patient does not “go” to the doctor for a check-up, the doctor’s intelligence is constantly with them. The experience is characterized by frictionless access and pre-emptive intervention.

The Digital Front Door

The patient’s primary interface is a voice-enabled Al health assistant, Tom, via mobile phone or smart home device. This Al handles 80% of needs including care coordination, medication refills, appointment scheduling, and basic health coaching. Interactions are personalized to channel preferences (text vs. voice), language, and time of day.

Ambient Monitoring

Wearables and smart home sensors (smart mirrors, toilets, bedding, floor mats) continuously stream data. Tom analyzes trends in heart rate variability, sleep patterns, gait, or changes in the urine to predict illness before symptoms become acute.

The “Exception” Visit

When a physical exam is truly needed (for a biopsy, vaccination, or complex palpation), it does not happen in a generic crowded waiting room or medical clinic.

Instead, it can occur in a micro-clinic (500-sq-ft, high-tech neighborhood hub) or via a paramedicine-staffed mobile medical unit that comes to the patient’s driveway to avoid unnecessary ER trips for conditions like dehydration, CHF exacerbations, or minor infections.

The Role of AI: Tom as the “Infinite Resident”

Tom and its Al capability serves as the “cortex” of the practice, handling the cognitive load that currently contributes to physician burnout. It acts not just as a tool, but also as an always present, always “on” resident of sorts. Key responsibilities of Tom include:

Continuous Care Coordination & Risk Stratification

Algorithms audit the incoming stream of patient data 24/7. Tom “red flags” a patient whose weight gain and oxygen levels suggest early heart failure, escalating them to the top of a physician’s list explicitly before a crisis develops.

Automated Administrative Architecture

Tom summarizes patient interactions, proactively schedules appointments for specialty consultations, diagnostic tests, and ancillary services, refills medications, and interfaces with electronic health record-integrated tools (UM, ambient scribes, revenue cycle.)

Contextual Clinical Decision Support

In advance of an encounter, either asynchronous or synchronous (virtual or in-person), Tom prompts the human provider with “Best Clinical Guidance” based on what is known about the patient from a complete patient record and contemporaneous patient-derived insights, guiding care that is based on the latest global research. Not only does this act as a real-time safety net against error, it allows for “top of license” performance by each clinician on the care team.

The Role of the Primary Care Provider, Enabled and Augmented by Tom

With Tom handling data gathering and routine synthesis, human roles shift toward high-level strategy, empathy, trust, and complex patients and procedures. In this new world, the PCP manages a much larger panel of patients (potentially 5,000+ instead of 2,000) but only interacts with the top 5% of higher-risk or most complex cases. Their job is no longer data entry; it is synthesizing complex information and making high-stakes judgment calls.

The workspace is very different, more akin to a modern tech-forward command center. The PCP is continuously fed asynchronous tasks which Tom has teed up for rapid review and sign off with dynamic prioritization raising the more pressing items to the top. As the regulatory environment and scope of practice laws evolve, Tom will assume more autonomous action. Inside of these encounters, Tom summarizes patient interactions, displays any patient-generated data, provides clinical decision support prompts where able, and stands ready to execute any new orders from the physician.

Patients requiring the physician’s time are coordinated as well with telemedicine being the likely patient communication channel, with or without human facilitation at the patient’s side. The physician coordinates logistics with a practice coordinator to dispatch a mobile unit to the home or route a patient to a micro clinic site that is convenient, if palpation, physical examination, or diagnostics are likely required for accurate and efficient diagnosis.

Optimal Financing Model: “Subscription + Outcomes”

The traditional fee-for-service (FFS) model is incompatible with this vision because it incentivizes volume (in-person, E/M-billable clinical encounters) rather than value (healthier patients). The optimal payment model is a hybrid direct primary care (DPC) and capitation structure.

There are several ways that the subscription model could now come to life. Because Tom-enabled primary care can be provided at a predictable and affordable cost, insurance products can universally include it as a benefit, and employers or self-pay consumers can fund primary care through HSA/FSA benefits or other tax-incentivized policies – combined with catastrophic “wrapper” insurance coverage.

Alternatively, traditional carriers could contract fully insured lives using a PMPM payment model (in lieu of FFS for a defined set of services), even within PPO networks, using regular, Tom-facilitated, patient-defined attribution in lieu of claims-driven logic (where Tom confirms with patients quarterly that their PCP is still Dr. Smith.”)

Health systems have additional ways to monetize more effective and efficient primary care with larger patient panels per PCP.

In Summary: The Future Is Now

Delivering high quality primary care to all Americans is now within our reach, thanks to the significant advances in generative Al. As with most transformations, technology itself is an important piece, but technology will fail to achieve its full potential in the absence of its adoption and deployment by humans. The art of the possible is only limited by our imagination and our willingness to evolve. Patients, healthcare providers, and purchasers all stand to gain by this new vision for primary care – but patients, rightfully, have the most to gain. Consider these three patients and how a Tom-enabled primary care practice would benefit them:

Health-Conscious Executive

For a patient focused on wellness and prevention, Al acts as a proactive coach, synthesizing data from their wearables to help optimize their routine and provide personalized insights. Care is remote and convenient allowing him to balance and protect his work and personal schedules.

Elderly Patient with Chronic Conditions

For an elderly patient with complex needs, Al provides a lifeline of continuous monitoring and support, connecting them to their care team and ensuring their conditions are managed effectively and proactively – mostly from the comfort of their own home.

Single Mother with a Behavioral Health Diagnosis

For a busy parent, Al offers accessible, on-demand mental health support, allowing them to check in with their care team and access resources discreetly and conveniently. The patient doesn’t have to sacrifice work or family for health.

Ai-Enabled Primary Care is a Reality

With Tom, Al enabled primary care is now a reality. Leading health systems across the country are embracing this incredible technology to solve one of the most vexing challenges in American health care – access to primary care, and all the benefits which accrue from that access. The future of primary care is indeed now.

Schedule a Tom Demo

References

Bhuyan, S. S., Sateesh, V., Mukul, N., Galvankar, A., Mahmood, A., Nauman, M., … & Samuel, J. (2025). Generative artificial intelligence use in healthcare: Opportunities for clinical excellence and administrative efficiency. Journal of Medical Systems, 49(1). https://doi.org/10.1007/s10916-024-02136-1

Daniel, H., Bornstein, S. S., & Kane, G. C. (2018). Addressing social determinants to improve patient care and promote health equity: An American College of Physicians position paper. Annals of Internal Medicine, 168(8), 577–578. https://doi.org/10.7326/m17-2441

Holman, H. R. (2020). The relation of the chronic disease epidemic to the health care crisis. ACR Open Rheumatology, 2(3), 167–173. https://doi.org/10.1002/acr2.11114

Jabbarpour, Y., Jetty, A., Byun, H., & Siddiqi, A. (2025). The cost of neglect: How chronic underinvestment in primary care is failing US patients. The health of US primary care: 2025 scorecard report. Milbank Memorial Fund. https://www.milbank.org/publications/the-health-of-us-primary-care-2025-scorecard-report/

Jones, C. H., & Dolsten, M. (2024). Healthcare on the brink: Navigating the challenges of an aging society in the United States. npj Aging, 10(1). https://doi.org/10.1038/s41514-024-00148-2

Lim, S. M., Li, P., Braat, S., Aung, Y. H., Fu, S., Johnson, D. F., & Lim, W. K. (2025). Validation of a multi-channel ambient sensor to measure vital signs in patients within the ward and at home. Sensors, 25(4), 1149. https://doi.org/10.3390/s25041149

McCarthy, M. (2015). Report predicts US physician shortage could exceed 90,000 by 2025. BMJ, 350, h1296. https://doi.org/10.1136/bmj.h1296

Milbank Memorial Fund. (2025). Building bridges to value: Infrastructure essentials for community health centers. https://www.milbank.org/publications/building-bridges-to-value/

Physician-Focused Payment Model Technical Advisory Committee (PTAC). (2025). Reducing barriers to participation in value-based care models. U.S. Department of Health and Human Services. https://aspe.hhs.gov/ptac-meetings

Starfield, B., Shi, L., & Macinko, J. (2005). Contribution of primary care to health systems and health. The Milbank Quarterly, 83(3), 457–502. https://doi.org/10.1111/j.1468-0009.2005.00409.x

Sun, X., & Li, X. (2023). Editorial: Aging and chronic disease: Public health challenge and education reform. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1175898

Taylor & Francis. (2025). AI in primary care — a general practitioner’s bucket list. European Journal of General Practice, 31(1). https://doi.org/10.1080/13814788.2025.2567462

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Rural Health Transformation: A Performance-Driven Program

The program places a clear emphasis on speed to deployment, speed to value, and the use of technology innovation to deliver measurable improvements in access, quality, and cost. Importantly, CMS has also emphasized strong stewardship of federal dollars, with expectations that states demonstrate transparency, accountability, and protection against fraud, waste, and abuse as RHT funds are deployed.

The intent is explicit. RHT is a performance-driven program, not a grant program designed to fund isolated pilots. It is a time-limited execution-focused investment meant to catalyze durable system change: expanding access, modernizing infrastructure, and using technology innovation to deliver quantifiable improvements in outcomes and cost. States are expected to demonstrate tangible progress against defined goals to retain and unlock future RHT funding, making rapid execution and measurable outcomes essential.

States must not only execute quickly, but also track, report, and validate outcomes to retain funding – making measurement, auditability, and responsible stewardship of funds aligned with core program requirements.

All 50 states received initial RHT awards at the end of last year, generally ranging from approximately $150 million to more than $280 million for the first year of implementation, with additional funding tied to performance and execution over time.

From Plans Execution: The Role of CHRA

To help states move quickly from planning to action, the Collaborative for Healthy Rural America (CHRA) launched as a national coalition purpose-built for rapid deployment, measurable impact, and long-term sustainability.

CHRA brings together Lumeris, Teladoc Health, Nuna, Deloitte, and Unite Us to provide proven, immediately deployable infrastructure that states can activate now – leveraging existing data platforms or initiatives, enabling real-time performance monitoring, auditability, and proactive identification of potential fraud, waste, or misuse of RHT-funded services, while keeping patients and local providers at the center of care.

The Top 10 Challenges States Must Solve

Based on CHRA’s engagement with rural health leaders in more than 30 states, the following challenges consistently emerge as the primary barriers to realizing the full promise of Rural Health Transformation – particularly when speed to value and accountability for outcomes are required.

These challenges reflect not just structural barriers, but the operational realities states must address to deliver results within CMS’s performance and accountability framework. These challenges are especially acute as states are required to demonstrate measurable progress on clinical outcomes, equity, and cost while maintaining rigorous oversight of RHT investments.

Access & Execution Challenges

1. Fragmented access and navigation

Challenge: Rural care is delivered across clinics, hospitals, EMS, telehealth platforms, schools and community organizations, often without coordination. Patients face multiple entry points and no clear front door.

How CHRA Helps: Delivers unified access models that connect disparate entry points into a single, navigable system anchored in local providers and designed to expand access quickly without disrupting existing care relationships.

2. Geography-driven barriers to care

Challenge: Distance, travel time and weather continue to dictate access to care, even as states invest in new programs.

How CHRA Helps: CHRA blends local care teams with virtual care, mobile services, and intelligent routing so geography no longer determines access or outcomes.

3. Moving from planning to execution at scale

Challenge: Many states have strong Rural Health Transformation plans but face challenges translating them into consistent, statewide execution.

How CHRA Helps: CHRA delivers immediately deployable, deployment-tested models that allow states to move from plan approval to execution quickly and scale statewide while producing early, measurable results tied to RHT goals.

Data, Technology, & Accountability Challenges

4. Siloed investments that fail to connect

Challenge: Investments in technology or programs often stand alone and fail to integrate into day-to-day care delivery.

How CHRA Helps: Provides shared infrastructure that connects RHT-funded investments into a single operating model centered on patients and local providers.

5. Data without day-to-day insight

Challenge: States have data but often lack real-time, actionable insights that support frontline decisions and performance accountability. This gap also limits states’ ability to detect anomalies, monitor utilization patterns, and ensure appropriate use of RHT-funded services.

How CHRA Helps: CHRA turns existing data into actionable intelligence that supports care delivery, performance monitoring, CMS-aligned reporting and proactive identification of anomalies and patterns associated with potential fraud, waste, or misuse of program resources through transparent, auditable workflows.

6. Technology fragmentation and infrastructure risk

Challenge: States cannot mandate EHR changes, yet interoperability gaps and uneven IT readiness persist.

How CHRA Helps: CHRA overlays existing systems to promote interoperability, within a secure infrastructure – avoiding rip-and-replace while accelerating value. 

Sustainability, Workforce & Outcome Challenges

7. Unsustainable primary care economics

Challenge: Fee-for-service models often fails to support primary care in rural markets, threatening long-term access.

How CHRA Helps: CHRA enables primary-care-led models, including Primary Care as a Service, that align payment, access, and outcomes and generate quantifiable return on investment to support sustainability beyond the RHT funding period.

These models are explicitly designed to improve performance on rural-relevant clinical metrics, including maternal health, chronic disease management for COPD and CHF, preventive care, and longitudinal primary care access.

8. Avoidable emergency department use and transfers

Challenge: Emergency departments are frequently used as substitutes for primary care, driving cost and unnecessary transfers. 

How CHRA Hhelps: CHRA integrates triage, EMS treat-in-place, and virtual specialty support to keep care local whenever appropriate. 

These capabilities support measurable reductions in avoidable ED utilization and readmissions, particularly for patients with chronic conditions such as CHF and COPD. 

9. Gaps in maternal and behavioral health continuity

Challenge: Loss of obstetric services and fragmented behavioral health follow-up create critical gaps in care. 

How CHRA Helps: CHRA supports coordinated maternal and behavioral health pathways that combine local care teams with virtual specialty support and closed-loop follow-up, enabling measurable improvements in maternal health outcomes, postpartum follow-up, and behavioral health continuity in rural communities. 

10. Workforce strain and unmet social needs

Challenge: Rural systems face staffing shortages, burnout, and limited visibility into social needs outcomes.

How CHRA Helps: CHRA enables team-based care, workflow automation, and closed-loop social services integration, extending capacity without replacing local providers.

Why Execution and Integration Matter

As John Fryer, chief growth and corporate development officer and one of the leaders organizing CHRA noted, “Rural Health Transformation is not about funding more programs. It is about delivering results quickly and building systems that work together. States that succeed will be the ones that turn RHT investment into measurable access, value, and sustainability—while keeping patients and local providers at the center of care.”

From Transformation to Sustainability

CHRA’s approach is intentionally designed to help states demonstrate progress early, measure results continuously, and sustain expanded access long after initial RHT dollars are deployed. This includes a commitment to tracking the metrics that matter most in rural communities: maternal health, chronic disease outcomes (including COPD and CHF), avoidable utilization, access, and equity—while maintaining transparency and accountability for every RHT dollar invested.

RHT gives states both the resources and the responsibility to rethink rural care delivery. The challenge is execution. The opportunity is to deploy proven infrastructure that delivers expanded access, measurable outcomes, responsible stewardship of public funds, and sustainable return on investment – well beyond the RHT funding window .

Learn More

Learn more about the Collaborative for Healthy Rural America and how it is supporting states’ Rural Health Transformation efforts at https://www.lumeris.com/chra/, or contact us at connect@lumeris.com

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As AI becomes more engrained in healthcare operations, health system leaders must evaluate solutions with the same rigor applied to clinical and financial decisions. A Health System’s Guide to Evaluating AI Solutions provides a primary care–first framework for assessing AI based on clinical relevance, workflow integration, scalability, and governance.

Developed by Lumeris clinical and strategy leaders, the guide helps executives make confident, defensible AI decisions that support long-term care transformation. It offers practical guidance for moving from experimentation to enterprise impact.

Key Learning Points

• How to evaluate AI solutions using a structured framework aligned to primary care delivery and health system strategy
• The core criteria leaders should apply when assessing AI vendors, including clinical impact, operational fit, scalability, and governance
• What separates scalable, enterprise-ready AI from point solutions that fail to deliver sustained value
• How thoughtfully deployed AI can help strengthen primary care capacity and help improve outcomes
• How to evaluate AI vendors with a useful takeaway checklist that has been thoughtfully curated with experts in the space

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Why Trust Is the Make-or-Break Factor

AI can accurately flag patients at high risk of readmission, but if the care team doesn’t trust the recommendation, it’s just another alert in an already crowded inbox. Trust is what turns AI from ‘interesting’ into ‘indispensable.’

So, what builds trust? At Lumeris, trust is not a single feature; it’s a system of principles and practices that must be embedded throughout the AI lifecycle. This approach ensures our solutions, like Primary Care as a Service (PCaaS) and Tom®, are not only innovative but also responsible. Here are the foundational factors that matter most for adoption in clinical and operational settings.

1. Patient Safety Above All

Healthcare is not a sandbox. You can’t ‘move fast and break things’ when lives are on the line. AI must operate within validated clinical guardrails, provide evidence-based recommendations, and include fail-safes for high-risk decisions.

In PCaaS, when Tom suggests a medication refill, it doesn’t just pull from a formulary. It checks for contraindications, recent lab results, and flags anything that needs a clinician’s review. Safety is not a feature it’s the foundation.

2. Transparency and Explainability

If AI feels like a black box, clinicians will treat it like a black hole – something to be avoided at all costs. Clinicians and administrators need clarity, not complexity. AI recommendations should come with interpretable rationale summaries, traceable decision pathways, and clear communication of limitations. When Tom recommends a follow-up for a diabetic patient, it doesn’t just say “Schedule an appointment.” It explains: “HbA1c trending upward over 3 months; guideline recommends review.” That’s transparency clinicians can act on.

3. Equity and Bias Mitigation

Bias in healthcare AI isn’t theoretical, it’s real, and it hurts outcomes. Fairness is non-negotiable. Diverse datasets, bias assessments, and ongoing monitoring ensure AI serves all populations equitably. External reviews and continuous validation reinforce this commitment. Our risk models in PCaaS are validated on diverse datasets, so a care coordinator in rural Ohio and one in urban St. Louis can trust that recommendations aren’t skewed by geography or demographics.

4. Accountability and Governance

Trust needs a safety net. Defined roles, multidisciplinary governance, and incident response protocols ensure that when AI influences care, accountability is clear and actionable. Every AI-driven action in PCaaS is logged with context; who saw it, who acted on it, and why. If something goes wrong, we don’t play “guess who?” Accountability is built in.

5. Compliance and Regulatory Alignment

Healthcare AI without compliance is like a car without brakes: it might go fast, but it is not safe. Legitimacy matters. AI must align with HIPAA, FDA, and emerging standards. Documentation and auditability aren’t just regulatory requirements; they are trust enablers. PCaaS integrates HIPAA and FDA-aligned protocols, so when Tom handles patient data, it does so under strict privacy and security standards.

6. Continuous Improvement

Trust isn’t a one-and-done deal. It is earned every day. Real-world performance monitoring, feedback loops from clinicians and patients, and periodic revalidation ensure AI evolves responsibly alongside clinical practice. We monitor Tom in real time for performance drift. If a model drifts, we catch it before it impacts care. And yes, clinicians can flag questionable outputs directly, because feedback loops aren’t optional; they’re essential.

Setting the Foundation of Trust for the Future of Care

Bottom line, AI is the engine of the future of care, and trust is its steering wheel. By embedding these foundational factors– patient safety, transparency, equity, accountability, compliance, and continuous improvement– we make adoption possible and practical. At Lumeris, PCaaS and Tom aren’t just AI solutions; they’re trust-first innovations designed for the realities of clinical and operational workflows. Because in healthcare, doing good and doing no harm isn’t just a promise, it’s the job description.

Schedule a Tom Demo

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