By Rony Sebastian, Founder and CEO, Softnotions Technologies
LinkedIn: Rony Sebastian
LinkedIn: Softnotions Technologies
Healthcare is moving quickly from AI pilots to AI agents. These tools can schedule appointments, draft clinical notes, check coverage, and support prior authorization with little human input. The promise is real: less administrative work and faster service for patients. But when software acts on its own inside clinical and financial workflows, the cost of a mistake grows. Governance should come first, not after launch.
Why AI agents raise the stakes
Traditional software follows fixed rules. AI agents interpret information, make choices, and trigger actions. They also handle protected health information, which brings obligations under HIPAA. Without clear rules, organizations risk biased outputs, privacy exposure, and unsafe recommendations that reach patients before anyone notices. The World Health Organization has also called for strong ethics and governance guidance for AI in health, noting that the technology can help only when it is designed and used responsibly.
Five governance basics
- Assign clear ownership. Every AI tool needs a named owner who is accountable for its performance, risks, and updates. Shared responsibility often means no responsibility.
- Protect data quality and privacy. An agent is only as reliable as the data it uses. Set rules for what data it can access, how it is stored, and how it is shared. Clean, well-connected data across systems also reduces errors.
- Keep humans in the loop for clinical decisions. Agents can prepare and recommend, but clinicians should review anything that affects diagnosis or treatment. Define which tasks can run automatically and which need approval.
- Test and monitor after launch. Approval is not the finish line. The NIST AI Risk Management Framework offers a practical way to map, measure, and manage AI risk over time. Track accuracy, bias, and unexpected behavior on a regular schedule.
- Keep audit trails and documentation. Record what the agent did, what data it used, and who reviewed the result. Clear logs help with compliance reviews, incident response, and patient trust.
A practical way to start
Governance does not have to begin with a large program. Start with one low-risk use case, such as appointment reminders or insurance eligibility checks. Form a small review group that includes clinical, IT, compliance, and operations staff. Define success measures, test the agent against real workflows, and review results before expanding. Each pilot then becomes a template for the next, so lessons are reused instead of repeated.
Common mistakes to avoid
- Treating AI like normal software. AI outputs can change as data changes, so a one-time test is not enough.
- Skipping staff training. Clinicians and administrators need to know what the agent can do, where it can fail, and when to step in.
- Ignoring model drift. Performance can decline as patient populations, coding rules, or workflows change. Without monitoring, problems stay hidden.
Governance builds trust
Strong governance is not a barrier to innovation. It is what allows healthcare teams to adopt AI agents with confidence and scale them safely.
Organizations that set ownership, privacy rules, human review, monitoring, and documentation early will move faster later, because clinicians, patients, and regulators will have reason to trust the results. The best time to build that foundation is before the first agent goes live.