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Generative AI in Value-Based Care and Risk-Bearing Organizations

By Anil Patil

Value-based and risk-bearing primary care practices are under rising expectations to deliver outcome-based care at lower cost while managing increasingly complex populations and patients with multiple chronic conditions. Unlike fee-for-service environments, success for these organizations hinges on proactive, coordinated, and data-driven care delivery. Yet clinicians and care teams remain overwhelmed by administrative work, fragmented data, and reactive operational and technology workflows. Generative AI (GenAI) is emerging as a powerful enabler in the care models—not by replacing clinicians or admin staff, but by optimizing their ability to identify risk, close care gaps, engage patients, and act at the right moment. When applied thoughtfully, GenAI can help value-based and risk bearing practices align clinical outcomes with economic performance.

Reducing the “Administrative Burden”: Freeing Time for the Human Touch
Excessive administrative overhead has become one of the biggest roadblocks in value-based and risk-sharing models, in particular. Clinicians are expected to manage chronic disease, coordinate care, have optimal documentation, meet quality metrics, and engage patients—all while maintaining productivity. This “administrative burden” erodes time for patient interaction and contributes to clinician burnout.

GenAI offers an opportunity to redesign clinical and operational workflows so that critical information is surfaced automatically and at every touchpoint of patient care. Rather than having clinicians and staff hunt for insights across EMR and practice management screens, worklists, and dashboards, GenAI synthesizes data into usable context and makes it available at the right time and place in the service model.

Ambient clinical documentation and charting are one of the highest impacting examples. By capturing conversations during patient interaction and generating structured clinical notes, GenAI reduces after-hours documentation and allows clinicians to focus on listening, “see and touch the patient,” and focus on clinical decisions. Importantly, a “human-in-the-loop” approach ensures clinicians maintain oversight for accuracy and liability.

Previsit intelligence is another high-impact use case at the point of care. GenAI can process longitudinal patient records before an encounter, summarizing active problems, overdue screenings, lab results, medication changes, and historic care plans including recent emergency department visits and hospitalizations. For a clinician, this means treating patients with a clear understanding of both clinical needs and quality opportunities—with minimal or no manual patient record searches.

In risk-based care models, prior authorization approvals are time sensitive and directly affect care delivery and clinical outcomes. GenAI accelerates approvals by synthesizing supporting clinical context, drafting requests, and reducing back and forth with the payers, helping patients get timely access to needed services.

In claims management, GenAI processes patient charts to identify documentation gaps, align coding, and create more complete claim narratives. This ensures first-pass accuracy, reduces preventable denials, speeds reimbursement, and eases administrative workload for both clinicians and revenue cycle teams. Together, GenAI applications do not replace clinicians; they reclaim time—helping care teams focus on patient-centric, high-value care.

Applications in High-Value Clinical Use Cases

AI Copilot for Complete Care
In risk-bearing models, care orchestration is critical for success. GenAI copilots support this by summarizing a patient’s 360-degree view, highlighting risks, and surfacing relevant insights during care delivery. Instead of static alerts and to-do lists, clinicians and staff receive contextual, patient-specific prescriptive and predictive insights that align with patient needs and care objectives.

AI to Find Gaps in Care
Timely care gap closure is central to success in shared-risk models. GenAI excels at identifying gaps by analyzing both structured data and unstructured clinical notes across EMRs, care management platforms, registries, and interoperable sources. It can identify overdue screenings, missing lab monitoring, or incomplete follow-up that traditional rules-based algorithms often miss.

Example: GenAI can identify patients with documented diabetes care plans in free text notes but missing evidence of annual retinal exams and HbA1c monitoring. Care team receives prioritized outreach lists, improving both clinical outcomes and quality performance.

Redesigning Workflows With GenAI
GenAI redesigns clinical and administrative workflows by embedding gap identification, prioritization, and actions directly into clinical workflows. Care managers receive summarized task lists, clinicians see gap-focused prompts during encounters, and staff are guided on outreach and scheduling—reducing fragmentation across roles.

AI in Clinical Decision-Making
While GenAI does not make clinical decisions, it enhances decision-making by using guidelines, patient records, and risk factors. This is especially valuable in providing complex care to high-cost, high-need members.

AI Algorithms for Diagnosis and Prognosis
Advanced AI algorithms provide early insights of disease progression and risk. In value-based settings, this enables proactive interventions—before conditions worsen and costs escalate.

AI in Chronic Disease Management
Chronic and critical conditions account for the majority of cost and utilization in risk-bearing models. GenAI facilitates longitudinal care coordination of the following:

• Diabetes: Monitoring lab trends, adherence, and preventive care;
• COPD: Identifying exacerbation risk and missed follow-ups;
• Hypertension and heart disease: Tracking control metrics and medication adherence; and
• High-risk pregnancies: Coordinating care plans and monitoring.

By continuously analyzing patient records, GenAI helps care teams intervene earlier and more consistently.

AI for Effective Patient Engagement
Engaged patients lead to improved health outcomes and closed gaps. GenAI enables personalized outreach—tailoring messages to language, literacy level, and clinical context. Patients receive education and clearer explanations of why care is needed, improving adherence and trust.

An Actionable Plan for Smart AI Adoption
Effective GenAI adoption requires a deliberate, outcome-driven approach. Organizations should start with high-impact, low-disruption use cases, such as clinical documentation support or care gap identification, where benefits are quickly measurable. Seamless integration across care systems is critical; GenAI should operate within existing EMRs and care management platforms to leverage longitudinal patient data. Finally, insights must drive action. Identified care gaps should automatically trigger required workflows ensuring a closed-loop process that translates insights into sustained clinical and performance improvements.

Ensuring Accuracy, Privacy, and Trust
Trust is foundational in health care AI adoption. Organizations must implement human-in-the-loop models, ensuring clinicians and subject matter experts review and validate AI-generated outputs before they influence care delivery.

AI governance frameworks are essential to define appropriate use cases, ownership, and corrective actions. These frameworks should address data governance, model efficacy, and performance monitoring.

Algorithmic transparency also matters. Clinicians and leaders must influence how insights are generated and what data sources are used. Clear audit trails and explainability help maintain compliance, protect patient privacy, and reinforce confidence in AI-supported care.

Conclusion
GenAI boosts care delivery and efficiency, acting as an accelerator rather than a shortcut. In value-based and risk-bearing care organizations, GenAI enables clinicians and staff to work smarter, engage patients more effectively, and provide patient-centric care proactively. The future of care is not automated; it is augmented—combining human wisdom with intelligent systems to deliver better outcomes and lower care cost.

— Anil Patil is a physician turned digital health leader and medical informaticist with deep expertise in leveraging technology to advance wellness, population health, and patient-centered care. He is experienced across mobile care, risk-based models, health plans, primary care, and community health technology ecosystems.