Agentic AI in healthcare: From automation to autonomy

Healthcare organizations are moving from task automation to intelligent, outcome-driven systems that act with context, speed, and compliance in mind.

 

In fact, generative AI is already supporting many companies with clinical documentation, call center scripts, and document processing. But these advancements still depend on human review before action can be taken.

 

That's where agentic AI can help – systems designed not just to recommend but to safely act.

 

Applied responsibly, agentic AI can:

 

• Reconcile claim discrepancies by cross-checking benefits and initiating resubmission

• Proactively identify patients at risk for missed follow-ups and schedule appointments while validating eligibility

• Open cases, review supporting data, and draft resolution plans in real time

   

The result: fewer handoffs, faster cycle times, and staff freed to focus on clinical care and human connection.

While many healthcare companies are interested in exploring agentic AI, it can be difficult to know where to start. The key is an approach that connects deep healthcare expertise, measurable value, and scalability to help payers, providers, and innovators move from pilots to production.

 

Here are some areas where agentic AI can add value in healthcare:

 

• Revenue cycle management: Unpredictable and inconsistent claims can cause revenue chaos for healthcare companies. Agentic AI can help. By predicting denials, correcting errors prior to submission, and resubmitting claims, staff can focus on the exceptions.

• Administrative workflows: Scheduling, billing, and prior authorization often cause frustration for healthcare members. Agentic AI can be used to triage requests, resolve routine inquiries, and pass complex cases to staff with full context. This can lead to 40% faster resolution times and an increase in net promoter scores

• Data and interoperability: Fragmentation across electronic medical records, claims, enterprise resource planning systems, and external registries makes compliance and gathering insights difficult. Harmonizing these data streams using agentic AI means high-risk patients can be flagged for proactive outreach. Better still, compliance can be strengthened to deliver a 30% improvement in population health insights

One area that deserves extra attention is how agentic AI can be used to transform the patient experience.

 

Patient satisfaction is intrinsically linked to how humanized (or not) their digital experience with healthcare companies is. They need empathy, compassion, and emotional support from the start.

 

One of the first interactions a patient or healthcare member has is often through some kind of contact center via phone, email, or live chat. This is a crucial moment for patients seeking assistance, clarification, and support. If virtual AI assistants and AI-powered patient relationship management systems are used carefully, patients get a well-designed and well-staffed contact center experience within a compassionate and responsive environment.

 

To achieve this, healthcare companies need to find a delicate balance between digital innovation and empathetic delivery. Each healthcare leader must use AI in the ways that work for their company and their patients, which is why the use of AI should never be a one-size-fits-all approach.

 

In whatever way you choose to use AI, adoption must be safe and ethical – especially in an industry where the stakes are life and death. This means responsible AI by design must be embedded into every use case, with safeguards for:

 

• Data privacy and compliance (HIPAA, GDPR, CMS interoperability rules)

• Accuracy and auditability, with testing pipelines and human oversight

• Bias detection and fairness, for equitable outcomes for vulnerable populations

• Ethics and transparency, with explainable AI frameworks

   

It's not just the right thing to do – it's essential to protect patients and partners.

Once you know where and how you want to apply AI, you need to think about the overarching operating model. For healthcare, there are three parts to the AI operating model that support clear business value and adoption at scale:

 

• Generative AI as the interface, for intuitive, natural conversations with staff, patients, and members

• Agentic AI as the execution engine, autonomously completing revenue cycles, administrative tasks, and patient-facing workflows

• Human expertise as the control layer, for compliance, ethics, and trust at every step

   

Healthcare companies that welcome this approach are setting themselves up for success.

Healthcare margins are razor-thin. Denials and write-offs are rising. Staff shortages continue to grow. Patients want seamless digital-first experiences. And regulatory scrutiny is intensifying.

 

AI is no longer a pilot project – it's becoming foundational infrastructure for healthcare operations.

 

Organizations that lead this shift to AI will share three traits:

 

• A commitment to responsible adoption at scale

• A vision to redesign operating models, not just overlay AI on legacy processes

• A focus on measurable outcomes: better margins, better experiences, better health

   

With AI at your side, it's possible to reimagine healthcare operations for efficiency, trust, transparency, and patient-centric care.

 

 

^{1. The Commonwealth Fund, High US Health Care Spending: Where Is It All Going?, 2023}