By Kajol Shah, Director, Budventure Technologies Pvt. Ltd.
LinkedIn: Kajol Bhatt
LinkedIn: Budventure Technologies Pvt. Ltd.
Healthcare organizations are moving quickly from AI pilots to AI-enabled workflows. What began as administrative chatbots and documentation support is now expanding into AI agents that can assist with scheduling, intake, patient communication, clinical documentation, revenue cycle tasks, and information retrieval across connected systems.
But as AI becomes more operational, one area is still frequently underestimated: the cost of controls.
For providers and payers, an AI agent is not simply a model connected to a chat interface. If the system touches electronic protected health information, supports staff workflows, or connects to clinical and financial systems, it becomes part of the organization’s broader health IT environment. That means the budget cannot be limited to software development, licensing, or model usage. It must include the safeguards, governance, monitoring, and documentation needed to operate safely.
The HHS HIPAA Security Rule requires regulated entities to use administrative, physical, and technical safeguards to protect electronic protected health information. In practical terms, healthcare AI projects must account for access controls, audit logs, encryption, workforce policies, incident response procedures, business associate agreements, and risk management processes.
These are not optional add-ons after launch. They should shape the system architecture from the beginning.
A useful starting point is to separate healthcare AI costs into three categories.
First, organizations should define workflow and data boundaries. What information can the AI agent access? Can it retrieve clinical records, billing information, appointment data, or payer policy documents? Can it only suggest actions, or can it initiate tasks? Which outputs require human review? These questions directly influence role-based access, permission design, audit logging, and escalation workflows.
Second, teams should plan for interoperability and transparency. ONC’s recent Health Data, Technology, and Interoperability rulemaking, including the HTI-4 Final Rule and the proposed HTI-5 rule, reflects the continued importance of interoperability, transparency, information sharing, and certified health IT expectations. Even when an AI system is not itself a certified EHR module, providers should still ask how the system handles data provenance, source attribution, FHIR-based integrations, user permissions, and explainability for clinical users.
Third, organizations should budget for ongoing assurance. AI systems are not static. Prompts change, workflows change, knowledge bases change, source documents change, and user behavior changes. A healthcare AI agent that performs well during testing may degrade if monitoring, evaluation, and governance are not maintained. The NIST AI Risk Management Framework offers a helpful reference point for thinking about AI risk across design, development, deployment, use, and evaluation.
The most common budgeting mistake is treating healthcare AI as a one-time implementation project. A more realistic plan separates launch costs from recurring control costs.
Launch costs may include system architecture, data mapping, integration work, access-control design, audit logging, security testing, compliance documentation, and vendor review. Recurring costs may include cloud infrastructure, monitoring tools, model evaluation, periodic access reviews, security updates, incident response testing, compliance reviews, and vendor risk assessments.
This distinction matters when comparing build, buy, or hybrid approaches.
A SaaS solution may be appropriate when the workflow is standardized, integration needs are limited, and the organization can operate within the vendor’s controls. A custom or hybrid approach may be more appropriate when workflows are proprietary, EHR integration is complex, audit requirements are specific, or the organization needs greater control over data flows and long-term operating costs.
The right decision is not always build. It is also not always buy. The better question is: which approach gives the organization the right level of control for the risk it is accepting?
Before deploying an AI agent, healthcare leaders should ask:
- What data will the system access, store, or infer?
- Which actions can the AI agent take without human approval?
- How will the organization audit user activity and AI-assisted decisions?
- What happens when an output is wrong, incomplete, or based on outdated information?
- Does each vendor that touches ePHI have the right contractual and security controls in place?
- How will performance, safety, and privacy risks be monitored after launch?
The economics of healthcare AI are not driven only by model cost. They are driven by the control environment around the model.
For providers and payers, responsible AI adoption requires more than a promising use case. It requires a cost-of-control plan that addresses compliance, interoperability, governance, security, and operational accountability before the first workflow goes live.