AI in Healthcare: Operational Value,
Limitations, and Oversight
A framework for evaluating any AI tool in healthcare — built for respiratory therapists and allied health professionals who need to think critically about the AI that is already in their workflow, whether they know it or not.
Operations AI vs. Clinical AI — Ask Which Column First
When someone says “AI in healthcare,” the most important question is not whether it works. The question is: which layer of the system does it touch? Operations AI and Clinical AI are fundamentally different problems with different risk profiles, different validation requirements, and different failure modes.
Operations AI — scheduling, billing, prior authorization processing, ambient documentation — is already deployed at scale in major health systems. The risk of a scheduling error and the risk of a vent weaning error are not the same category of harm. Operations AI handles high-volume, structured, repeatable tasks where failure is recoverable. It has real ROI and it is already running.
Clinical AI touches diagnosis, treatment recommendations, deterioration prediction, and dosing decisions. It requires prospective validation, population-specific data, and a governance structure that most institutions do not yet have. The scrutiny is categorically different — and the oversight requirement does not transfer to the algorithm.
Three Questions for Any AI Tool
These three questions apply at a vendor demo, a department pilot, and a governance committee. Put them in your back pocket before the next AI pitch.
What Is Reviewing Your Prior Auths
This is not a future concern. Algorithmic prior authorization review is operating at scale now. Algorithms screen requests against coverage criteria before any human sees them. Denials are auto-generated. The review window is measured in seconds.
The clinical implication is direct: knowing this changes how you write prior authorizations. Include explicit SpO&sub2; values. Use objective thresholds. Mirror the criteria language. Under two seconds of algorithmic review means keywords matter — that is a clinical documentation skill in 2026, not an administrative one.
** Senate Permanent Subcommittee on Investigations, 2024 report on Medicare Advantage AI prior authorization
Red Flags in Any AI Pitch
Three Priorities for Respiratory Therapy
RT-specific ambient documentation tools are 18 to 24 months out from broad deployment. The question to ask your institution now is whether a pilot is underway and whether RT workflows are in scope. Being in the room before implementation beats being trained on a system built without RT input.
Revenue cycle AI is disrupting the prior authorization layer right now. This directly affects RT authorization workflows for ventilators, high-flow oxygen, and pulmonary rehabilitation. The documentation skill described above is not theoretical — it is already the difference between approvals and denials.
Predictive deterioration AI is directly RT-relevant. When your institution evaluates it, an RT should be in the evaluation room as a clinical evaluator, not just a training session attendee. Demand population-specific validation data. Your skepticism is professional judgment, not resistance to innovation.