Benefits of agentic AI in healthcare workflows
Agentic AI is transforming healthcare by automating tasks, improving decision-making, and enhancing patient care. Here's a quick overview of its key benefits:
- Automation: Handles up to 80% of routine documentation, saving time and reducing costs.
- Efficiency: Cuts administrative spending by 15–30% and saves billions annually.
- Decision Support: Boosts diagnostic accuracy and personalizes treatment plans.
- Patient Experience: Enables 24/7 support, remote monitoring, and tailored care.
- Scalability: Addresses workforce shortages by optimizing workflows.
Quick Comparison: Traditional AI vs. Agentic AI
| Feature | Traditional AI | Agentic AI |
|---|---|---|
| Autonomy | Responds to input | Operates independently |
| Decision-Making | Fixed rules | Context-aware reasoning |
| Learning | Manual updates | Self-improves continuously |
| Data Handling | Single task focus | Multi-step workflows |
Agentic AI is already driving results, such as reducing call abandonment by 85% and automating 44% of inbound calls. With healthcare costs rising and staff shortages increasing, this technology offers practical solutions to improve efficiency and care quality.
Top 10 Agentic AI Use Cases in Healthcare
Automating Administrative Tasks
Agentic AI is transforming healthcare by streamlining administrative workflows, which are notoriously time-consuming and costly. Inefficient processes cost healthcare organizations a staggering $455 billion annually and consume up to 50% of providers' time - time that could be spent on patient care.
Smart Scheduling Systems
AI-driven scheduling tools are making workforce management in healthcare more efficient. Take Mercy, a 50-hospital system, as an example. In 2023, Mercy introduced AI-powered nursing management and saw impressive results:
- Saved $30.7 million in staffing costs
- Cut time spent on scheduling and staffing tasks by 20%
- Increased shift fill rates from 83% to 86%
- Reduced turnover by 8%
"During the pandemic, Mercy nurses departed for increased compensation, but pay wasn't the main driver. Their workload for what we were paying them was the catalyst. Clinicians are no longer tolerating pre-pandemic working conditions and they want more control over their schedules."
– Betty Jo Rocchio, Senior Vice President and Chief Nurse Executive at Mercy
Similarly, the Hong Kong Hospital Authority has been using an AI rostering engine since 1998 to create weekly and monthly schedules in minutes. This system factors in hospital regulations, staff preferences, and skill distribution, saving time and ensuring fairness.
Automated Medical Billing
AI is also simplifying medical billing, speeding up insurance claims processing while reducing errors. It can handle 91% of eligible claims and process them 46% faster than traditional methods. This automation significantly cuts costs, especially in call center operations, which often cost healthcare organizations around $14 million annually. For instance, Mayo Clinic is testing VoiceCare AI's agent, Joy, to manage tasks like insurance verification, prior authorization, claims processing, and appeals.
Data Integration Solutions
AI-powered tools are helping healthcare facilities integrate data more effectively, reclaiming valuable staff time. At the Medical University of South Carolina, AI agents save over 5,000 clinical staff hours each month. Meanwhile, the Ottawa Hospital has cut preoperative appointment times in half, potentially saving 80,000 staff hours annually with Nvidia's AI agent.
| Integration Area | Impact |
|---|---|
| Call Management | 85% drop in abandonment rates |
| Routine Calls | 44% of inbound calls automated |
| Staff Time | Appointment durations reduced by 50% |
In one case, a Ushur client resolved 36,000 member interactions autonomously in just two months. These systems often operate through self-service portals on healthcare websites, improving patient access while reducing staff workload.
"Agentic AI works best when the user is incredibly sophisticated, when the workflows that you're making better are incredibly repeatable, and when the ontology and knowledge that's required is fixed. In those situations, AI actually is very effective."
– Punit Soni, CEO of Suki
From scheduling to billing to data integration, agentic AI is proving to be a game-changer for healthcare operations. Its ability to handle repetitive tasks efficiently is freeing up resources and improving overall efficiency.
Supporting Medical Decisions
Agentic AI doesn't just handle administrative tasks - it also supports healthcare professionals with advanced tools for diagnostics and treatment planning, improving patient care outcomes.
Diagnosis Support Tools
AI-powered diagnostic tools are making a big impact on accuracy. For instance, a study using a GPT-4 multi-agent framework saw top differential accuracy jump from 0% to 71.3% (57/80 cases) and reach 80% (64/80 cases) when considering the final two differentials.
An AI-driven chatbot also delivered impressive results:
- 81.8% top-one diagnostic accuracy (327/400 cases)
- 85.0% top-two diagnostic accuracy (340/400 cases)
- 95.8% accuracy in specialist referrals
- A 47% reduction in diagnostic questions (16 on average compared to 29)
"The framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. The LLM-driven multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios." - Yu He Ke et al.
These advancements pave the way for more precise and personalized treatment strategies.
Custom Treatment Planning
Agentic AI is transforming treatment planning by tailoring care to individual patients. These systems analyze diverse data points - such as genetic information, medical history, lifestyle habits, and treatment responses - to create customized care protocols. For example, in oncology, AI reviews PSA levels, MRI results, biopsy findings, molecular tests, and biochemical markers. The insights are turned into actionable recommendations and integrated with electronic medical records (EMR).
In addition to personalized care, these systems enhance patient safety by identifying risks early.
Patient Risk Analysis
AI-powered monitoring systems are improving risk prevention by spotting subtle changes in patient conditions before they escalate. Using real-time monitoring, predictive analytics, and historical data, these tools enable early interventions. For example, prior authorization reviews are completed 40% faster.
"AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients. This allows healthcare professionals to focus mainly on complex cases and patient interaction." - National Center for Biotechnology Information
Wearables and AI-integrated monitors also play a role in preventing complications, reducing hospital readmissions, and lowering costs by continuously tracking and analyzing vital signs.
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Improving Patient Experience
Agentic AI is changing the way patients experience healthcare by providing personalized, 24/7 support. This approach not only improves accessibility but also helps deliver better results for patients.
24/7 Digital Health Assistants
AI-powered digital assistants are reshaping patient interactions with healthcare providers. These systems handle tasks like answering common questions, scheduling appointments, and offering medical guidance instantly. For example, Intermountain Health introduced an AI system that cut call abandonment rates by 85% and automated 44% of routine inbound calls.
Other examples include Babylon Health's diagnostic bot, which evaluates symptoms and suggests next steps, and Sensely's virtual nurse avatar "Molly", which gathers patient information through various communication platforms.
"Pre-op appointments can take up to two hours, and these are conducted with a healthcare professional. But a lot of [the information shared during these appointments] - where to park, when to arrive - is not diagnostic or clinical information. But the patient needs to know it so they can adhere to the right instructions - so they can be there on time, and so they don't have to cancel the surgery because they had any food that morning."
– Kimberly Powell, Vice President and General Manager of Healthcare at Nvidia
Remote Health Monitoring
AI-driven remote patient monitoring (RPM) systems are making a big impact on managing chronic diseases. These systems have been shown to reduce hospital readmissions by 30%, improve patient outcomes by 20%, and increase healthcare access by 40%.
In a 2020 study, an AI-based decision support system was able to optimize insulin doses for patients with type 1 diabetes.
"AI agents are reshaping chronic disease management by offering continuous monitoring, personalized treatment plans, and proactive interventions. These AI teammates enable seamless communication between patients and healthcare providers, streamlining care delivery and ensuring that patients receive timely, effective support in managing their chronic conditions."
– Dr. Jagreet Kaur Gill, Chief Research Officer and Head of AI and Quantum at Akira AI
Personal Care Management
AI systems work together to create tailored care plans by analyzing data from various sources, including clinical records, genetics, and imaging. This capability is especially important as healthcare knowledge now doubles every 73 days, yet only 3% of healthcare data is being used effectively.
"That's a pretty low-stakes use case, but there are lots of areas where AI can greatly improve that process. Think about the specific preferences certain providers have - you may have three dermatologists, but one of those dermatologists may not perform certain procedures. Knowing which dermatologist performs which procedure and looking for availability are all multiple steps that a person would be doing - but now the AI can do it almost instantaneously."
– Naimish Patel, Head of Healthcare at Red Cell Partners
Setup Guide and Best Practices
Data Security Requirements
Agentic AI systems demand strong safeguards to protect EHRs, PII, and ePHI.
"Organizations that can effectively harness these data streams while maintaining data security will be best positioned to thrive as healthcare continues morphing and becoming more patient-centric." - Vijayashree Natarajan, SVP and head of technology at Omega Healthcare
Here are some key security measures:
| Security Layer | Implementation Requirements | Purpose |
|---|---|---|
| Access Control | Micro-segmentation for applications and users | Blocks unauthorized access |
| Monitoring | Continuous system surveillance | Detects threats early |
| Data Governance | Dedicated AI oversight team | Ensures regulatory compliance |
| Audit Protocol | Regular vulnerability assessments | Preserves system integrity |
Once these measures are in place, you can move forward with integrating the system.
System Integration Steps
Integrating agentic AI systems requires careful planning to avoid disruptions and ensure smooth operation.
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Assessment Phase
- Review your current infrastructure.
- Pinpoint integration points and analyze data flows.
- Check compatibility with existing systems.
- Define clear metrics to measure success.
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Implementation Strategy
- Set milestones for the integration process.
- Roll out solutions in stages to manage risks.
- Use synthetic data for safe, controlled testing.
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Performance Monitoring
- Continuously track system performance.
- Set up automated alerts for potential issues.
- Keep an eye on possible security breaches.
Employee Training Guide
Investing in training can lead to a 218% increase in income per employee and boost profit margins by 24%.
Focus training on these three areas:
| Training Component | Focus Areas | Expected Outcomes |
|---|---|---|
| Technical Skills | Operating AI systems, data entry | Improved operational efficiency |
| Clinical Integration | Using decision support tools, optimizing workflows | Better patient outcomes |
| Security Protocols | Privacy compliance, data protection | Reduced risks |
To make training effective, include these elements:
- Create role-specific modules tailored to employee needs.
- Conduct regular assessments to ensure knowledge retention.
- Keep detailed documentation for reference.
- Offer mentorship programs to support ongoing learning.
The training program should highlight real-world applications while reinforcing the importance of human oversight in critical decisions.
Conclusion
Agentic AI is reshaping healthcare by streamlining workflows, reducing costs, and enhancing care quality. With the healthcare industry grappling with $455 billion in annual efficiency losses, AI solutions have proven to cut costs and automate processes effectively, delivering real operational improvements.
These achievements are building trust in the role of agentic AI.
"Trust begins with proof. AI tools need to be accurate, reliable and safe before they can support a physician's choices. Nearly as important, they must integrate seamlessly into clinical workflows, delivering meaningful insights at the right time without disrupting care delivery", - Jeff Jones, senior vice president of product development at UPMC Enterprises
The market for AI in healthcare is expected to grow rapidly. Projections estimate an increase from $14.6 billion in 2023 to $102.7 billion by 2028, with a compound annual growth rate of 47.6%. This surge is tied to agentic AI's clear benefits across key areas:
| Impact Area | Measurable Benefits | Business Outcome |
|---|---|---|
| Administrative Tasks | Up to 30% reduction in review times | Greater operational efficiency |
| Cost Management | 15–30% reduction in administrative spending | Lower operating costs |
| Resource Optimization | Up to 50% reduction in documentation time | Increased staff productivity |
Healthcare organizations are already seeing the advantages. For example, Ushur's AI agent successfully handled over 36,000 interactions independently. By integrating agentic AI with robust security measures and proper training, organizations can position themselves for success in the evolving digital healthcare landscape.