Agentic AI in Healthcare

This is often the custom part of an agentic AI solution. It coordinates workflow steps, tool calls, case context, and handoffs between specialized agents in multi-agent systems. You can use off-the-shelf platforms by vendors like AWS and Microsoft to set up orchestration, but healthcare-specific approval logic and exception paths often need tailoring.

Some integrations can use standard APIs or existing platform connectors, while others require engineering around EHR, RCM, CRM, scheduling, contact center, or payer systems. Custom work is usually needed to expose only the right actions to the agent, support data integration, normalize data between systems, handle failed calls, and log every read or update.

Healthcare organizations usually already have payer protocols, task routing rules, service policies, SLAs, and access permissions. Software engineers and business analysts turn these rules into executable workflow logic that the agent can follow consistently across cases.

You can integrate ready-made AI models via cloud services such as Azure OpenAI or Amazon Bedrock, or deploy certain models in a private cloud or on premises for extra security. Custom work will be needed to adapt prompts, data retrieval mechanisms, and model behavior to your workflows, documents, and terminology.

Useful content already exists in clinical records, payer manuals, provider directories, scheduling rules, FAQs, and internal policies. But before AI agents can rely on it, this content usually needs cleaning, strict access rules, and version control to keep outdated information out of recommendations — that’s where engineers come in.

In ScienceSoft’s experience, users are more likely to adopt AI when it appears where the work already happens: in an EHR panel, RCM queue, or CRM case page. In many cases, this means you can add a sidebar, dashboard, or embedded widget to an existing system instead of building a separate app. For patient-facing use cases, access is usually added through existing patient portals, mobile apps, telephony systems, or call center software.

Before agentic AI is connected to any systems, its actions should be defined as clearly as any user role or workflow rule. At ScienceSoft, we usually create allowed-action maps for each agent before implementation begins, with architects, security specialists, compliance officers, and clinical or operational leads validating the technical, security, and regulatory constraints. The map should describe the agent’s operating boundaries, including system access, data access, editable records, and approval points. For example, a prior authorization agent may collect supporting clinical data, create a task to obtain a missing document, and update the case status. But it should not be able to send a submission, approve an appeal, or message a patient. Those actions should remain with staff unless the organization explicitly approves automation for that step.

Secure AI agent frameworks do not give agents broad access to healthcare systems. If an agent connects to each healthcare system separately, its permissions, logs, and error handling can become scattered across multiple integrations. This makes it harder to control what the agent can do and harder to investigate issues later. A safer design is to route agent actions through a controlled integration layer. Instead of giving the agent broad system access, the layer exposes only workflow-specific actions, such as retrieving an encounter summary or updating referral status. As a result, every agent action follows the same access rules, logging format, and failure-handling process across connected systems.

PHI exposure in agentic AI is hard to control when your software only tracks where the agent retrieves patient data, but not where that data is copied, transformed, stored, or sent afterward. Similar to the permitted action maps, ScienceSoft recommends creating a PHI flow map for every agent workflow, showing how patient data moves through the AI pipeline, connected systems, and vendor-controlled environments. The map should be updated whenever a workflow step, connector, prompt, or logging rule changes.

Based on this map, the project team defines how the agent may use PHI at each touchpoint. Administrative safeguards assign responsibility for agent configuration, PHI-sharing approvals, incident response, and vendor access. Physical safeguards cover the workstations and environments where staff may view agent activity involving PHI. Technical safeguards enforce these rules in software through access control, encryption, audit logs, retention limits, and restrictions on PHI use in prompts, logs, embeddings, and support records.

Before rollout, PHI handling can be tested with misuse and failure scenarios derived from the PHI flow map. For example, the team can check whether unnecessary patient details enter a prompt, PHI appears in an error log after a failed connector call, identifiable text is retained in a vector database, or a technical support ticket captures the agent’s output with patient details. These tests help spot missing controls before the agent is connected to live workflows.

Agentic AI is hard to trust when healthcare teams cannot see how a case moved through the workflow, which systems the agent called, what data was used, and where the process stopped. Visibility is needed even when the workflow does not require formal approval. Staff should be able to review agent activity, source data, status changes, escalations, and manual overrides in the work queue where they already work.

Each agentic workflow needs a monitoring owner or review group, depending on its risk and scale. They can regularly review failed actions, exception rates, PHI handling issues, and audit trail samples. In higher-risk workflows, this review may involve operational leads, clinical experts, IT security specialists, and the development team. This helps detect unsafe or inefficient patterns before they lead to delayed cases, user complaints, or audit findings.