Worldwide attention was mostly focused on Generative AI and how it automates digital tasks, but a change of a much bigger scale is happening in healthcare, which, apparently, is being ignored by most people. The one who is the IBM Corporation Customer Success Manager, Arjun Warrier, who manages a customer portfolio of more than $10M ARR and is a Fortune 500 client, to bringing in the new technology: agentic AI. Agentic AI refers to autonomous systems, can understand their environment, logically work through complex healthcare procedures, and, if given a goal, can independently execute the necessary actions to achieve that goal.
With this new breed of systems, it is no longer just about the reactive paradigm of generative AI. Generative models will only respond when requested. On the contrary, agentic AI will take the initiative by observing data streams, predicting needs, changing tactics, and making sure regulations are followed during the entire process. In a situation where the timing of interventions can be crucial for the patient's survival, the switch from reactive to proactive AI can be deemed a significant technological advancement.
Beyond the Chatbot: What Agentic AI Actually Does
The arrival of external laboratory results from the pathology department at several hospitals set off a series of manual processes—verification of patient data, alerting of clinicians, modification of EHR, and initiation of treatment change. All these activities consume a lot of time and may cause errors. The AI-based system, with the support of human intervention, very rapidly eliminates this entire process. It identifies the newly autopsied tissues, connects them to the patient's history, and then, through the doctor's preferred communication, it "calls" the doctor of choice; the patient then receives a record with a well-prepared history, and it also "drops" the out-of-normal-range results for urgent review by medical staff. He performs all these tasks repeatedly without depending on any signals, and he is always independent.
Warrier's customers have witnessed this through practice: processing of more than 50,000 patient records with full compliance to FDA and DEA audits, 95% renewal rates, and 15–25% gains in efficiency of operations in busy hospitals.
The Three Pillars of Healthcare Agentic AI
- Contextual Awareness and Learning: In contrast to rules-based automation, which is rigid, agentic AI is sensitive to clinical, functional and historical context. In directing laboratory information or ordering prescriptions, it will take into account patient status or provider needs, departmental loads, and regulatory limits. This adaptive context-awareness allows making decisions which are really adaptive.
- Autonomous Decision-Making Within Defined Boundaries: There is calibrated autonomy in agentic AI. It can rearrange regular drugs or send urgent results automatically, but bombards human specialists in cases on the edge, such as irregular drug interactions. It is very powerful because it ensures the maximisation of efficiency and conservation of clinical safety.
- Continuous Improvement Through Feedback Loops: Through outcome learning, agentic AI becomes more effective as the agentic AI learns, reducing delays and bottlenecks in communication. These learning cycles form the key to the 1525 per cent efficiency realisations that were realised in the implementations of Warier.
Real-World Applications Reshaping Healthcare Delivery
- Treatment Protocol Optimisation: The agentic AI is capable of delving into the past is of patient care and then grouping the results. In such areas as oncology, where the level of acuity is high, the AI can be used to figure out dosing patterns that lead to positive outcomes and thereby recommend changes to care teams. Also, this AI can be a continuous clinical optimisation tool that is always present.
- Regulatory Compliance Automation: The healthcare organisations are under tough requirements by agencies like the FDA and DEA. The agentic systems are open to keep on validating actions, automatically generate documentation and keep a full audit trail. One of the implementations made by Warrier cut compliance overheads by approximately a third and enhanced performance in the audit.
- Predictive Resource Allocation: Through patient flow and operational patterns, agentic AI can optimise the schedules of the staff, redistribute equipment, and detect bottlenecks at the point of disruption of care. During previous positions at Axway, the implementations led by Warrier provided a 30% reduction in the efficiency of patient-care workflow by predictive operational modelling.
Innovation Without Compromising Compliance
One of the most important benefits of agentic AI, especially in a controlled setting such as healthcare, is that it allows compliance to become part of its operations. Each action is checked against regulatory schemes, real-time documentation is produced, and audit trails are made automatically.
This, according to Warrier, squares a long-standing industry conflict: that innovation retards regulatory readiness. The dynamic is overturned by agentic AI through allowing organisations to innovate more quickly since compliance is part of the basic functioning of the system, rather than a sort of side-note.
Challenges and Considerations
- Trust and Transparency: Healthcare providers should know the way decisions are made. The agentic AI systems must have clear logic and explainable behaviours, as well as clear lines of escalation.
- Integration Complexity: The heterogeneous aspect of healthcare IT requires advanced integration levels. The implementations of the enterprise in Warrior have reduced the integration costs by up to 30 per cent using modernisation and unifying data.
- Ethical Guardrails: Autonomous systems pose the concerns of responsibility, preventing bias, and human control. It must have efficient governance structures, not merely technical solutions.
What Healthcare Leaders Need to Know
Warrier advises health-system executives to approach agentic AI strategically:
- Begin with workflows with high volume and rules-based, e.g. lab results processing or prescription order refills.
- Scale independence is progressively scaled, and there is good control over when systems are active and when they get out of hand.
- Measure results that are important, such as turnaround time, reduction of error, compliance measures, and patient satisfaction.
- Put integration architecture first, since access to clean interoperable data is the base of intelligent autonomy.
The Path Forward
Shifting from a generative AI to an agentic AI is not merely a technological upgrade; instead, it is a revolution that changes the whole structure of the hierarchy in the healthcare system. It is the businesses that apply agentic AI in their workflow who will be the first to gain and later to see the increasingly evident year-by-year competitive advantages, especially considering the lack of personnel and the growing complexity of operations.
Warrier agrees with the view that the effect is already noticeable: Agentic AI has stopped being seen as just an instrument for saving time and resources; on the contrary, it overhauls the entire level of capability of the healthcare companies. The difference in digitisation between the leaders and the followers is becoming greater, and agentic AI is driving the gap further.
About Arjun Warrier
The Customer Success Manager at IBM Corporation is Arjun Warrier. He is managing a group of Fortune 500 healthcare enterprise clients with an ARR of more than 10 million dollars, which is considered one of the best portfolios in the company. Warrier has been working with IBM for over 18 years and has also gained experience in the same field from Software AG, Axway, and Infosys. His work was mainly focused on the AI-based healthcare integration and the modernisation of the enterprise. Warrier is an IEEE Senior Member, a researcher with more than 18 peer-reviewed papers, and an international conference speaker, having been invited by such institutions as Manchester Metropolitan University and Science Tech Xplore.
His talent is covered in One India, News Nation TV, and the Free Press Journal, and he analyses topics related to the future of autonomous and agentic AI in healthcare in the major media, such as The Globe and Mail.
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