The Clinical Team as Conductor, AI as Orchestra

Beyond Interpretation: The Case for Agentic AI

Traditional healthcare IT systems are passive. They display results, send alerts, and generate reports, but they don't take action. The burden of coordination—scheduling follow-ups, ensuring referrals happen, verifying patients fill prescriptions, confirming lifestyle changes are implemented—falls on already overwhelmed clinical staff and patients navigating complex systems.

Our vision for agentic AI changes this paradigm. Instead of simply flagging that a patient's HbA1c indicates diabetes, agentic AI will initiate and manage the entire care cascade that should follow. It will schedule appointments, coordinate referrals, ensure prescriptions are filled, monitor adherence, and adjust interventions based on outcomes—all while keeping the clinical team informed and in control.

The Care Coordination Crisis

Consider what typically happens when lab results indicate a new diagnosis of diabetes. The clinician must schedule follow-up appointments, refer to diabetes education, prescribe medications, order additional tests, coordinate with endocrinology if needed, and ensure the patient understands and follows through with each step. Each handoff represents a potential failure point where patients can fall through the cracks.

Research shows that up to 50% of referrals never result in completed appointments. Medication non-adherence rates exceed 30% for chronic conditions. Follow-up testing is missed or delayed. The result? Preventable complications, emergency department visits, and hospitalizations that proper care coordination could have avoided.

How Agentic AI Will Transform Care Coordination

We envision agentic AI as an intelligent care coordinator that never sleeps, never forgets, and never gets overwhelmed. When lab results require action, the AI won't just alert—it will act. Here's how we see this transformation working in practice:

Immediate Action Initiation: When results indicate diabetes, the AI will immediately begin coordinating care. It will check the patient's insurance coverage for diabetes medications and supplies, identify in-network endocrinologists with availability, and schedule appointments that fit the patient's preferences and transportation needs.

Intelligent Scheduling: The AI won't just book appointments—it will optimize them. It will coordinate multiple visits to minimize travel, schedule lab draws to align with fasting requirements, and ensure follow-up appointments happen at clinically appropriate intervals. If a patient misses an appointment, the AI will automatically reschedule and address barriers.

Prescription Management: Beyond sending prescriptions to pharmacies, agentic AI will monitor whether they're filled, identify cost or coverage issues, and proactively address them. If a patient doesn't fill a critical medication, the AI will explore alternatives, connect them with assistance programs, or alert the clinical team to intervene.

Proactive Monitoring: The AI will continuously monitor for expected follow-up actions. Did the patient complete their diabetes education? Have they checked their blood sugar as recommended? Are follow-up labs scheduled? When gaps appear, the AI will take action to close them.

Coordinating Complex Care Cascades

Where we believe agentic AI will truly shine is in managing complex conditions that require coordination across multiple providers and interventions. Consider a patient whose lab results reveal chronic kidney disease alongside diabetes:

The AI will orchestrate a complex ballet of care: nephrology referral with appropriate prerequisite testing completed first, medication adjustments to protect kidney function, dietary counseling specific to both conditions, blood pressure monitoring with automated device integration, and regular lab monitoring with trend analysis. Each element will be tracked, managed, and optimized continuously.

The AI will ensure that all providers have the information they need when they need it. It will prepare visit summaries, ensure test results are available before appointments, and coordinate care plans across specialties. No more patients arriving at specialists without crucial information or conflicting recommendations from different providers.

Addressing Social Determinants Through Intelligent Coordination

Our vision for agentic AI recognizes that successful care coordination extends beyond clinical services. When lab results indicate poorly controlled diabetes, the AI will investigate why. Is it medication access? Transportation barriers? Food insecurity affecting dietary control?

Based on its findings, the AI will take appropriate action: connecting patients with transportation services for appointments, identifying food assistance programs for those with food insecurity, coordinating with social services for housing or utility assistance that impacts health, and adjusting care plans to accommodate life circumstances.

This holistic coordination will ensure that care plans aren't just clinically sound but actually feasible for each patient's unique situation.

Closing the Loop on Test Results

One of healthcare's most dangerous gaps is the failure to act on abnormal results. Studies show that 7-10% of abnormal lab results lack appropriate follow-up. We envision agentic AI eliminating this gap entirely.

The AI will track every result requiring action and ensure completion: abnormal results will be acknowledged by clinicians within defined timeframes, patients will be notified appropriately based on urgency and health literacy, follow-up testing or appointments will be scheduled automatically, and completion will be verified with automatic escalation if delays occur.

For critical results, the AI will be able to initiate immediate action—paging on-call providers, arranging urgent appointments, or even coordinating emergency transportation when warranted.

Personalized Communication and Engagement

We see agentic AI transforming how patients engage with their care by personalizing every interaction. The AI will learn each patient's preferences: preferred communication channels (text, phone, email, app), optimal contact times, language and health literacy levels, and motivational approaches that resonate.

When coordinating care, the AI will adapt its approach accordingly. A tech-savvy patient might receive app notifications with detailed information, while another might get simple text reminders with phone call follow-ups. The AI will even coordinate with family members or caregivers when authorized, ensuring support systems are engaged.

Preventing Readmissions Through Proactive Coordination

Hospital readmissions often result from failed care transitions. We believe agentic AI will dramatically reduce these failures by taking control of post-discharge coordination. When discharge labs indicate risk factors, the AI will spring into action.

It will ensure follow-up appointments are scheduled before discharge, verify medications are filled and understood, coordinate home health services when needed, monitor for early warning signs through connected devices, and escalate concerns before they become emergencies.

This proactive approach has the potential to dramatically reduce readmission rates, particularly for conditions like heart failure and diabetes where lab monitoring is crucial.

Integration with the Broader Healthcare Ecosystem

The agentic AI we're building won't operate in isolation—it will integrate seamlessly with existing healthcare infrastructure. It will pull data from electronic health records, laboratory information systems, pharmacy systems, and insurance platforms. More importantly, it will push coordinated action back into these systems.

The AI will update care plans in the EHR, document actions taken, communicate with care team members through their preferred channels, and maintain audit trails for quality improvement. This integration will ensure that agentic AI enhances rather than disrupts existing workflows.

Measuring Impact Through Continuous Improvement

Unlike traditional care coordination efforts that rely on manual tracking, the agentic AI we envision will continuously measure its own effectiveness. It will track metrics like time from result to action, appointment completion rates, medication adherence, clinical outcome improvements, and patient satisfaction scores.

This data will drive continuous improvement. The AI will learn which coordination strategies work best for different patient populations, which barriers most commonly impede care, and which interventions drive the best outcomes. These insights will improve not just individual patient care but system-wide coordination strategies.

The Clinical Team as Conductor, AI as Orchestra

It's crucial to understand that in our vision, agentic AI doesn't replace clinical judgment—it executes it at scale. Clinicians define the care pathways, set the parameters, and make the critical decisions. The AI ensures these decisions are implemented completely, consistently, and efficiently.

Think of clinicians as conductors who interpret the music and guide the performance, while agentic AI serves as a tireless orchestra that executes every note perfectly. The conductor's expertise and interpretation remain essential; the AI simply ensures flawless execution.

Privacy, Security, and Ethical Considerations

As we build agentic AI to take more active roles in care coordination, robust privacy and security measures are paramount. Adia's agentic AI systems are being designed with privacy by design, ensuring that all actions will be authorized, documented, and auditable. Patients will maintain control over their data and can adjust AI permissions at any time.

Ethical considerations are embedded throughout our development process. The AI is being programmed to respect patient autonomy, support but not replace clinical decision-making, and ensure equitable access to coordination services regardless of technical literacy or socioeconomic status.

The Future of Autonomous Care Coordination

We're building toward a future where no patient falls through the cracks, where every lab result triggers appropriate action, and where care coordination happens seamlessly in the background. Agentic AI will make this future possible by transforming passive data into active care.

As these systems evolve, we envision AI agents that can predict coordination needs before problems arise, negotiate with insurance companies to expedite approvals, coordinate care across entire populations while maintaining personalization, and even collaborate with other AI agents to optimize resource utilization.

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Conclusion

The shift from passive to agentic AI in healthcare represents more than technological evolution—it's a fundamental reimagining of how care will be delivered. By automating the complex logistics of care coordination while preserving clinical autonomy and patient choice, agentic AI will address one of healthcare's most persistent challenges.

At Adia, we believe that every lab result should not just inform but activate appropriate care. We're building agentic AI to ensure that the insights generated from laboratory testing translate into coordinated action that improves outcomes. As we continue to develop these capabilities, we're moving closer to a healthcare system where perfect care coordination is not an aspiration but an expectation.

This is our vision for the future of AI-powered care—a future where healthcare isn't just about knowing what to do, but ensuring it gets done. With agentic AI coordinating care based on lab results, we're working to make that future a reality.