Executive Summary:
For decades, we have thrown more data at healthcare’s problems. We digitized records, built AI models, and automated workflows. And yet, the healthcare players remain trapped in an endless loop of clicks, approvals, and guesswork—a system that runs on friction instead of flow.
And now Agentic AI is turning eyeballs with a new promise— offloading the weight of low-value decisions, so healthcare professionals can focus on what truly matters—saving lives, innovating treatments, and delivering care that feels human again.
John, 52, arrives at the ER with crushing chest pain. Hours earlier, his smartwatch flagged abnormal heart rhythms—data that never reached his doctor. Now, every second counts. The ER orders urgent tests—ECG, bloodwork, imaging—but treatment stalls, waiting on prior authorization. The payer, overwhelmed with requests, needs more data and stuck in bureaucratic loops. Diagnostics lack access to John’s full history, delaying interpretations. Imaging results sit in disconnected systems. Pharma has life-saving medication but can’t dispense it without approval.
Everyone—providers, payers, diagnostics, pharma—is ready to act but paralyzed by fragmented data and red tape. The cruel irony? The technology to prevent this crisis existed on John’s wrist, but the system failed to connect the dots. This isn’t an isolated case—it’s healthcare today. The problem isn’t a lack of information; it’s that the data is too slow, too scattered, and too broken to save lives when it matters most.
Three major pain points drive this crisis:
- Cognitive Overload: By 2025, healthcare will generate over 60 zettabytes of data annually, but only 3% of it is effectively used. Clinicians are drowning in data scattered across systems, forcing them to manually sift through records during critical, time-limited consultations. Medical knowledge doubles every 73 days, especially in cardiology, oncology, and neurology, overwhelming even the most experienced providers.
- Care Plan Orchestration: Simple processes like scheduling tests or authorizing medications require manual coordination. Providers send imaging requests and surgical consults via fragmented systems, while diagnostic teams lack patient histories to make informed decisions quickly. Each delay compounds the risk to the patient.
- System Fragmentation: Data from wearables, EMRs, diagnostics, and payers sit in silos, unable to communicate effectively. Even life-saving information—like wearable heart data—can’t flow seamlessly between stakeholders, slowing down treatment when every second counts.
These challenges demand more than incremental fixes. Does technology have a solution?
Analysts and industry leaders are hailing Agentic AI as the culmination of everything technology has been moving toward—an evolution that promises to tackle deep-rooted challenges in complex systems like healthcare. But can it truly solve the inefficiencies that have plagued patient care for decades?
From RPA to Copilots to Agents: AI’s Evolution and Contribution to Healthcare
Traditional automation tools have fallen short in healthcare’s complex landscape. Robotic Process Automation (RPA) can execute repetitive tasks flawlessly—like automatically submitting prior authorization requests—but it’s rigid and rule-based. If something unexpected happens, like missing patient data or a new form introduced by an insurer, RPA hits a wall. It can’t adjust to variables outside of its predefined rules.
Large Language Models (LLMs) can process vast amounts of data and generate insights. For instance, an LLM could analyze medical records and identify potential issues based on historical patterns. But LLMs can’t act. They can’t automatically trigger a test order or send an alert to a clinician—they need to be paired with another system or human intervention to execute tasks.
Agentic AI bridges this gap. It combines the automation power of RPA with the reasoning capabilities of LLMs, adding autonomy and adaptability. These agents act on data, learn from outcomes, and adjust workflows dynamically to meet goals, all without constant human oversight.
Let’s break this down with a real-world example.
Scenario: John, 52, has a smartwatch that detects an irregular heartbeat.
| What RPA Could Do: | What Generative AI Could Do: | What Agentic AI Does: |
| An RPA bot might be programmed to send a notification to John’s phone or upload the data to his electronic health record (EHR). But if John doesn’t respond or the data doesn’t trigger a preset threshold, the bot does nothing. It lacks the flexibility to assess the situation beyond its basic rules. | A generative AI model could analyze John’s heart data alongside his medical history and flag it as potentially dangerous. It might even generate a detailed report for his doctor, explaining the anomaly. But it stops there. It can’t order tests, notify providers, or handle follow-ups. | Agentic AI takes it several steps further. It: Detects the irregular heartbeat from John’s smartwatch. Cross-references the data with his medical history in the EHR. Orders an ECG and necessary blood tests automatically through the hospital system. Initiates a prior authorization request with John’s insurer, ensuring no treatment delays. Notifies the cardiologist of urgent findings and schedules an appointment. Alerts the pharmacy to prepare necessary medications in case intervention is needed. |
Where Agentic AI Lands First: Use Cases Across Healthcare
This workflow, however, is just the tip of the iceberg. What it can achieve depends on how creatively and strategically we implement it, all while respecting regulatory guardrails and patient safety standards.
Agentic AI can integrate with everything from Electronic Health Records (EHRs) and Electronic Medical Records (EMRs) to payer systems, diagnostics platforms, and even pharma supply chains. The possibilities stretch across internal operational efficiencies and external partner collaborations, transforming how care is delivered, financed, and managed.
| 1. Providers: Optimizing Clinical Workflows and Decision-Making Dynamic Treatment Plans: AI agents can continuously analyze real-time patient data from EHRs, adjust treatment plans based on new lab results, and notify clinicians of critical changes. This reduces manual data reviews and prevents diagnostic errors. Care Coordination: From scheduling follow-ups to coordinating between radiology, pathology, and pharmacy, Agentic AI orchestrates seamless, interdisciplinary collaboration. Regulatory Compliance: By ensuring all patient data handling aligns with HIPAA and GDPR requirements, Agentic AI reduces compliance risks while maintaining transparency and patient trust. |
| 2. Payers: Streamlining Claims and Authorization Processes Automated Claims Adjudication: AI agents can review claims, verify them against policy coverage and clinical guidelines, and make decisions in real-time, dramatically reducing turnaround times. Fraud Detection: By analyzing vast datasets across claims, Agentic AI can identify suspicious patterns and flag potential fraud, improving financial outcomes. Member Engagement: AI-driven chatbots and virtual agents enhance customer interactions, offering personalized policy recommendations and real-time claim updates. |
| 3. Life Sciences: Accelerating Drug Development and Clinical Trials Clinical Trial Automation: AI agents manage trial recruitment, monitor patient adherence, analyze trial data, and adapt protocols in real-time to optimize outcomes. Regulatory Submissions: The AI automates regulatory documentation for bodies like the FDA, ensuring that data submissions are consistent, accurate, and timely. Pharmacovigilance: By continuously monitoring post-market data, Agentic AI can detect adverse drug reactions early, ensuring patient safety and compliance. |
| 4. MedTech: Enhancing Device Performance and Patient Monitoring Predictive Maintenance: AI agents monitor device performance, predict failures, and schedule maintenance automatically, minimizing downtime. Remote Monitoring & Alerts: Devices connected to Agentic AI can track patient vitals, detect anomalies, and alert healthcare providers instantly. Data Interoperability: Ensuring data from devices integrates seamlessly with EHR systems while adhering to interoperability standards like FHIR (Fast Healthcare Interoperability Resources) |
Implementing Agentic AI: A Phased Approach to Trust and Automation
Should you accept full AI autonomy on day one? Absolutely not. Agentic AI isn’t an overnight transformation—it’s a strategic evolution. Gartner defines AI agency as a spectrum, meaning AI doesn’t jump to full autonomy immediately. Instead, it progresses incrementally, ensuring trust, control, and operational alignment at every stage.
The path to AI adoption follows three phases:
- Augmentation (Human-in-the-Loop, AI as an Assistant)
AI assists but does not act independently. It automates repetitive tasks, speeds up information retrieval, and provides recommendations, but a human makes the final decision. For instance, in claims processing, AI pre-fills claims, highlights inconsistencies, and ranks urgency, but a human still approves every claim. This phase builds trust by proving AI’s reliability without taking control away from users.
- Automation (AI as an Operator, Humans as Supervisors)
AI begins to take over high-confidence decisions while flagging edge cases for human review. For example, in claims processing, AI might handle 70-80% of cases end-to-end, while humans intervene only in complex or ambiguous scenarios. At this stage, AI continuously learns from human feedback, refining its decision-making and improving accuracy over time.
- Autonomy (AI as an Independent Actor, Humans as Observers)
AI operates without human intervention, managing workflows end-to-end while escalating only rare or high-risk situations. In this stage, the user interface disappears—there’s no need for a manual review of every process. Instead, humans oversee the system at a macro level, ensuring compliance, governance, and optimization.
Each phase ensures measurable improvements—eliminating human errors, eliminating AI errors, reducing processing time, and scaling efficiency—while still keeping humans in control until AI has proven its reliability.
The Future of Healthcare with Agentic AI: Intelligent, Autonomous, and Frictionless
John’s story reveals healthcare’s biggest weakness: fragmented data and slow workflows that harm patient outcomes. But Agentic AI changes the game. Instead of rigid APIs and manual integrations, agents will communicate directly with each other, ensuring real-time data sharing across providers, payers, diagnostics, and life sciences.
For CXOs, this means lower costs, faster decisions, and better care. Data integration projects that once took months will happen on the fly, freeing teams to focus on outcomes, not infrastructure. Next-generation healthcare applications will be AI-native, automating administrative tasks and providing doctors, nurses, and executives with timely, actionable insights.
At PalTech, we see healthcare evolving into a connected, efficient, and autonomous ecosystem, driven by Agentic AI. The old system of siloed information and endless red tape will give way to transparent and human-centric care—where technology finally does what it should: support people, not hold them back. Until then, stories like John’s will remain common. With Agentic AI, a frictionless, intelligent future is within reach.
