Agentic AI use cases in the healthcare industry
Agentic AI is a large shift from simple task automation to advanced, goal-driven systems that reason and plan. Unlike just generative AI, agentic AI can coordinate end-to-end processes (patient triage, care orchestration, resource allocation, medication management, and other routine tasks) with minimal human intervention.
By reducing mental overload and fragmentation, agentic AI connects people and events across departments, thus enabling faster delivery and fewer handoff errors.
Agentic AI is evolving and emerging as the next big thing shaping applied technology in the healthcare industry. Agentic AI – agents within intelligent frameworks – can reason, autonomously resolve healthcare challenges, and make informed decisions with limited human oversight.
But wait, don’t confuse it with AI agents – we’re talking about technology reaching beyond isolated functions.
What is agentic AI?
Agentic AI generally refers to systems that establish and pursue complex goals with minimal human guidance. Agentic AI, if according to more technical definitions, can achieve those objectives by reasoning and planning, often within changing settings.
But isn’t that basically the same as an AI agent? Not really.
AI agents are specialized, task-oriented solutions that follow specific instructions to handle isolated functions. In practice, they’re diligent, reliable receptionists that handle patient queries, scheduling, reminders, and more.
Agentic AI, in comparison, includes solutions that pursue broader goals without needing detailed prompting. Some experts even think they could even coordinate cohesive plans across multiple specialized departments, from scheduling necessary testing to monitoring patient progress.
| AI agents | Agentic AI | |
| Appointment management | A chatbot can schedule a follow-up after receiving patient prompt | The system can detect abnormal results, identify urgency, notify specialists, and sends prep instructions |
| Patient triage | The algorithm can provide basic advice and suggest further options | The system can monitor vital signs, flag deterioration, alert specialists, and update EMR records |
| Medication reminders | An agent can send daily reminders | The system can track adherence trends, adjust reminders, alert clinicians, and suggest an intervention |
| Clinical documentation | An agent can transcribe human speech into appropriate EMR fields | The system can analyze doctor-patient conversations, extract findings, suggest diagnoses and treatments, and update the program |
Learn more:
- AI agents for healthcare to transform patient care
- Agentic engineering: new trend worth watching
- AI agents for smarter hospital workflows
- AI agents for assisted patient care
How does agentic AI differ from generative AI?
General perspective
Generative AI is great at creating new content (text, images, audio, video) and editing if given clear directions. It’s like a sophisticated autocomplete program: you provide your question or request and receive a response (ChatGPT and DALL·E are the most popular tools).
Agentic AI, in contrast, is a solitary assistant that not only understands your instructions, but does much more. It can establish goals and actively pursue them – it doesn’t passively wait for prompts.
Which one would suit your needs the best?
To put it short, just imagine two kinds of helpers:
- The one is the creative assistant that waits for prompts
- The other is the self-directed doer that takes the initiative
Technical perspective
| Generative AI | Agentic AI | |
| System purpose | To generate new content by responding to instructions | To achieve set goals through planning, making decisions, and executing |
| System behavior | Reactive behavior (generates responses without considerations) | Proactive behavior (initiates actions and adapts to contexts) |
| Autonomy | Requires instructions to generate an output | Acts independently with minimal human input |
| Architecture | Generally monolithic (a single AI model) | Modular, multi-component (a blend of different AI modules) |
| Learning approach | Primarily uses supervised or self-supervised learning for training | Usually uses reinforcement learning, planning algorithms, or so-called tool-augmented reasoning |
| Interaction loop | One-shot or few-shot interaction | Ongoing loop |
| Temporal persistence | No state, no continuity between prompts unless added external memory | Both state and strategy, as well as memory by default |
| Output type | Content artifacts | Actions, decisions, state changes |
Now let us move to the practical stuff.
Agentic AI in healthcare: dealing with everyday routines
Agentic AI integration for smarter delivery: the trifecta of challenges
Mental overload, aftercare orchestration, as well as fragmentation continue disrupting healthcare workflows. That causes disjointed services and delays.
Just imagine someone arriving in the emergency room with shortness of breath and needing prompt attention. The nurse must assess vital signs, check for cardiac risks, and manually enter everything into the EMR system – all that while handling other alerts.
At the same time, the attending will coordinate with radiology to book an X-ray and cardiology to book an ECG. Without unification, those requests often require phone calls or emails.
Later, when results arrive, the pharmacist will cross-check medication history for potential drug interactions. But, typically, the records are scattered across different health systems.
Each handoff adds delays, mental load, and risks.
But how can professionals get those daily tasks done efficiently without working extra hours?
Agentic AI implementation changing the game
Agentic AI can address the disaster of fragmentation by coordinating these complicated healthcare workflows. By gathering the context, guiding tasks, and initiating further actions – ordering tests, notifying specialists – agentic AI can minimize the load.
Being goal-driven and adaptive, agentic systems continuously learn and refine existing strategies in real-time. They don’t support decisions – they ensure every step is connected and efficient.
Agentic AI for healthcare: use cases
Some scenarios demand solutions that not only analyze but plan, coordinate tasks across systems, and execute:
Patient triage
Agentic systems can assess and analyze the symptoms and history to prioritize patients based on condition. The algorithms can route the cases, trigger workflows, notify teams, and continuously re-evaluate priorities.
Patient admission & discharge
Agentic systems can detect the criteria for admission and discharge from incoming health metrics and referrals. The algorithms can schedule the tests, assign rooms, notify teams, and coordinate further plans and follow-ups – all without human hand-offs.
Decision support
Agentic systems can process patient records, results, guidelines, and notes to assist in diagnosis and treatment. Generating advice, highlighting risks that typically go unnoticed, suggesting actions, and adapting on demand – all without interrupting workflows.
Care-plan orchestration
Agentic systems can monitor the records (vital signs, labs, imaging) to reprioritize assigned tasks when needed. Dispatching orders, adjusting programs right away – no problem.
Claims processing
They can quickly analyze a document, validate coding, prepare claims for submission, and follow up statuses. That means fewer denials and much faster payout with minimal manual intervention.
Resource allocation
They can also forecast bed availability, predict staffing, reassign nurses or orderlies, and even trigger schedules. All that by integrating patient records without using paper-based spreadsheets.
Medical documentation & transcription
Talking about medical paperwork, they capture doctor-patient interaction and convert the speech into notes. They summarize completed visits, extract entities, suggest coding, and even flag missing patient information.
Medication management & reconciliation
Beyond generating medication lists, they might also manage other processes associated with medication tasks. These involve inventory querying, formulary checks, refill inquiring, interaction flagging, and more.
How we can help
By transitioning from isolated healthcare systems to cohesive agentic systems, you unlock smart healthcare automation. The transformation will manifest across operations and change daily delivery.
Let’s talk about how you can successfully implement Agentic AI – contact us.
Our services:
- AI development
- RPA services
- .NET development
- ASP.NET development
- Web development
- Mobile development
- Cloud services
- Custom product software development
Our expertise:
- Artificial intelligence
- Machine learning
- Deep learning
- In-context learning
- Prompt engineering, and more
- Computer vision
FAQ
Agentic systems can pursue a goal by planning, making decisions, and taking further actions across settings. Unlike other advanced tools, agentic systems actively interact, learn, adapt, and coordinate different actions with minimal human input.
Non-agentic AI typically responds to one single prompt or task – it analyzes the input and produces an output. Agentic AI, on the other hand, is an autonomous ecosystem – it sets a goal, plans actions to execute the plan, and adapts when needed.
- A reduced cognitive burden on clinicians
- A decreased administrative workload
- Quick adaptation to changing patient conditions
- Scalable handling of large patient populations
- Complex decision-making
- Smarter coordination, and more
- Patient triage
- Patient admission & discharge
- Decision support
- Care-plan orchestration
- Claims processing
- Resource allocation
- Medical documentation & transcription
- Medication management & reconciliation
It analyzes data sources including records, results, guidelines, and notes to support data-backed planning. Generating advice, highlighting risks, and handling other tasks, it helps to build evidence-based strategies.
Dive deeper:
It manages complex flows across teams (bed management, staff scheduling, patient flow, resource allocation). Forget bottlenecks and inefficiencies.
Dive deeper:
No transparency, incorrect or biased recommendations, clinical over-reliance, ethical concerns, and others. There’s also the challenge of integrating with existing healthcare systems and avoiding business disruption.
Agentic AI can be regulatory compliant, without doubt – but only when designed in accordance to standards. HIPAA, GDPR, HL7, FHIR, CDA, DICOM – all these and others are critical to ensure patient privacy and safety.