Agentic AI in the healthcare industry
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 |
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.
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 large model) | Modular, multi-component (different 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 |
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 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: key applications
Some scenarios demand solutions that not only analyze but plan, coordinate tasks across systems, and execute:
End-to-end 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.
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.
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.
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 automation. The transformation will manifest across operations and change daily delivery.
Talk to Abto Software about adopting agentic AI, and let’s get started.
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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.
AI agents aren’t always agentic systems, only those having autonomy, goal-oriented behavior, and adaptability. AI agents may or may not have agency
- 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
- Data privacy and security
- Data standardization
- Complicated interoperability across departments and tools
- Potential over-reliance
- Clinical validation and trust
- Legal liability and accountability