By Arinder Singh Suri, Founder & CEO, Taction Software
LinkedIn: Arinder Suri
LinkedIn: Taction Software
Predictive algorithms are no longer a future-state ambition in healthcare delivery. They are embedded in the electronic health record’s clinicians open every shift, flagging sepsis risk, prioritizing worklists, drafting documentation, and surfacing patients who may need intervention. For provider and payer organizations, the question has shifted from whether to adopt clinical AI to how to govern it responsibly once it is live.
Federal policy has begun to answer part of that question. The HTI-1 Final Rule from the Office of the National Coordinator introduced first-of-its-kind transparency requirements for predictive algorithms built into certified health IT. It established a category called “predictive decision support interventions” and required certified developers to disclose a defined set of source attributes, information about how a model was trained, validated, and tested for fairness. ONC also pointed organizations toward the FAVES principles: that decision support should be Fair, Appropriate, Valid, Effective, and Safe.
This is meaningful progress. With more than 96% of hospitals using ONC-certified health IT, baseline transparency about the algorithms shaping care is overdue. But provider and payer leaders should treat regulation as a floor, not a ceiling. A “nutrition label” on a model tells you what went into it; it does not tell you how that model behaves on your patient population, in your workflows, six months after go-live.
That gap is where local governance matters. Three disciplines separate organizations that deploy AI safely from those that merely deploy it.
Monitor for drift. A model that performed well at launch can degrade quietly as patient demographics, documentation habits, and clinical practice change. Performance is not a one-time validation; it is a continuous measurement against real outcomes. Organizations need a defined cadence for re-evaluating every production model, and a clear trigger for pulling one that has slipped.
Evaluate for bias, locally. A vendor’s fairness testing was conducted on the vendor’s data. The population you serve may differ in ways that materially affect accuracy. Governance means examining model performance across the subgroups in your own community and being willing to act on what you find.
Preserve clinician judgment. Decision support should inform decisions, not replace them. Clinicians need to understand, at least in plain terms, why a model produced a given recommendation, and they need a frictionless way to override it. A black box that erodes clinical autonomy loses clinician trust, and a tool that clinicians do not trust is a tool they will not use, regardless of its accuracy.
A useful scaffold for all of this is the NIST AI Risk Management Framework, which organizes oversight around governing, mapping, measuring, and managing risk. It is voluntary and sector-neutral, but it translates well to healthcare and gives multidisciplinary teams a shared vocabulary.
The practical takeaway for leadership is organizational, not technical. Effective AI governance requires a standing, cross-functional body in clinical, IT, compliance, data, and quality all represented, that owns the lifecycle of every model from selection through retirement. It requires defining, before deployment, what success and failure look like in measurable terms. And it requires the discipline to walk away from impressive technology the organization cannot yet support.
Clinical AI will keep advancing, and the regulatory framework will evolve alongside it. The ONC has already signaled that further rulemaking is coming. The organizations that benefit most will not be the ones that adopt the fastest. They will be the ones that built the governance to deploy with confidence, earning the trust of clinicians and patients by design rather than by accident.