Agentic artificial intelligence (AI) gains popularity swiftly. This technology refers to AI systems designed to act autonomously towards specific goals. Agentic AI can plan, decide, take actions, and use tools without constant human prompting. Its goal-oriented behavior and decision-making capabilities make it valuable in different industries, involving healthcare. In this blog, we explore agentic AI in healthcare by focusing on real-world use cases. 

What is Agentic AI in Healthcare?

Agentic AI in healthcare means autonomous or semi-autonomous AI systems concentrating on clinical or operational goals. Agentic AI includes AI agents which are software systems completing tasks like reasoning, planning, and making decisions for users or other systems.  

Unlike traditional AI systems that focus primarily on prediction or content generation, AI agents in healthcare can act, coordinate, and adapt within real-world clinical and operational workflows. They can interpret data from electronic health records (EHRs) and other healthcare systems, assess potential risks, and prioritize appropriate actions. Also, they can alert healthcare professionals, plan care, and monitor patient outcomes.  

GenAI in healthcare is found among the popular subjects, but this technology and AI agents are different. While GenAI primarily supports content generation tasks such as summaries, notes, and explanations with limited autonomy, agentic AI operates with higher autonomy by actively managing and coordinating care processes. In short, GenAI assists physicians while agentic AI orchestrates health workflows. 

What are the Benefits of Agentic AI in Healthcare? 

Agentic AI does not only analyze and generate outputs, so it offers important benefits in healthcare. This technology actively helps run healthcare operations. The following situations are the key benefits of agentic AI in healthcare: 

• Fast and Safe Decision Support: Delays and incomplete information in healthcare environments can increase the risk of adverse outcomes. AI agents in healthcare can prevent this by improving decision speed and safety. Healthcare professionals can receive prioritized and synthesized information instead of raw data via agentic AI. Moreover, this technology sends alerts and recommendations in a timely manner, so potential harm can be mitigated before it escalates.  

• Reduced Burnout Among Healthcare Professionals: Agentic AI in healthcare can address burnout by minimizing clinical and administrative burdens. It can autonomously and continuously manage administrative loads, frequent interruptions, and fragmented workflows within healthcare facilities. This enables healthcare professionals to regain time, focus, and motivation.  

• Better Patient Outcomes: By enabling more coordinated and personalized care, the technology supports improved patient outcomes. Specifically, it can identify risks earlier and avoid missing follow-ups, leading to fewer complications and enhanced recovery. 

Real-World Use Cases of Agentic AI in Healthcare 

Agentic AI shows its benefits with real-world applications. Common AI agent applications in healthcare include clinical decision support, patient monitoring, personalized care, and operation automation. 

Clinical Decision Support and Diagnostics 

Agentic AI continuously evaluates patient data, interprets clinical information, and identifies appropriate next steps. Rather than passively presenting health data, it actively supports clinical decision-making. 

An agent used for early sepsis detection in hospitals is a good example. It detects subtle deterioration patterns across time via continuous monitoring of lab results, medications, and clinical notes. Particularly, it flags rising sepsis risk, warns the care team, suggests guideline-aligned actions, and tracks whether actions were taken. Consequently, it significantly accelerates recognition, helps reduce mortality, and supports more consistent adherence to sepsis protocols.

Patient Monitoring and Virtual Care 

AI agents can work both inside and outside of healthcare facilities. Agentic AI-driven remote monitoring for heart failure patients is a concrete example. These patients wear devices tracking weight, blood pressure, and heart rate. Then, an agent reviews daily information and detects early signs of fluid retention. Moreover, the system can send personalized notifications like ‘‘take your medicines’’.  

The above example shows that agentic AI acts according to patient data autonomously instead of waiting for clinician review. This reveals earlier intervention, fewer hospital admissions, and better patient engagement. 

Personalized Treatment 

Agentic AI in healthcare uses patients’ needs, preferences, and responses as a base to dynamically adapt treatments. For example, an agentic AI system can be designed for oncology treatment pathway management.  

Cancer patients have multi-month care plans involving labs, imaging, chemotherapy, and follow-ups. An agent can track these plans and make adjustments based on personal health data and treatment efficacy. Furthermore, it can coordinate across oncology, radiology, and pharmacy. This improves patient experience during complex care journeys. 

Workflow Automation

AI agents arrange multiple-stage clinical and administrative workflows, eliminating manual coordination. For instance, an agent can help a clinician who orders a specialized medical imaging study for efficient prior authorization management. The system can gather required documentation and submit prior authorization to the insurance company. After that, it can monitor response status, handle requests for additional information, and update the clinician and patient automatically.  

The example for prior authorization management underscores that agentic AI can reduce administrative burden significantly by operating across different systems like EHRs and payer portals. This protects patients because care delays can be mitigated. 

Considerations for Agentic AI Adoption in Healthcare

The real-world examples show that traditional AI is primarily limited to generating predictions and warnings, whereas agentic AI in healthcare manages end-to-end processes. While traditional AI often requires manual intervention, AI agents are designed to operate autonomously. Although agentic AI brings several benefits to healthcare, there are still critical challenges that need to be addressed. 

Data Quality and Interoperability 

Poor data quality might cause incorrect actions in agentic AI systems. In other words, decisions and actions by AI agents are as reliable as the healthcare data feeding them. Consequently, strong data validation and continuous monitoring are essential before autonomous action is allowed. 

In addition to data quality, interoperability between diverse health systems affects agentic AI adoption. For example, an agent cannot improve care coordination if it cannot seamlessly access data across EHRs, labs, imaging systems, and payer platforms. Fragmented data causes incomplete understanding of patient status. Because of this, robust interoperability standards and integration across clinical and administrative systems must be prioritized for effective AI deployment. 

Patient Safety 

Healthcare providers, professionals, and patients may have concerns about agentic AI’s ability to initiate actions. Key safety concerns about this technology involve over-automation, alert fatigue, unintended interactions between systems, and failure to recognize edge cases. Clearly defined human-in-the-loop controls are crucial to eliminate safety concerns. Although agentic AI in healthcare is autonomous in execution, it is not autonomous in authority. 

Transparency and Explainability 

Healthcare decisions must be understandable, defensible, and auditable; otherwise, physicians may be cautious about relying on digital systems. Also, patients want to know how decisions by AI affect their care. In this respect, AI agents in healthcare must clearly communicate what they are doing, why they are doing it, and which information they use.

Final Words 

Agentic AI moves healthcare forward through a goal-driven and coordinated approach. By enabling autonomous execution within unambiguously identified clinical and ethical boundaries, this technology can improve health decision-making, reduce clinician burden, and support patient-centric care.  

As Tiga Healthcare Technologies, we demonstrate the benefits of AI in healthcare with our AI & Analytics solutions. By clicking here, you can explore how our AI-powered solutions transform health data into actionable insights. 

Let’s shape the future together with artificial intelligence in healthcare, as always!