Most AI tools work from simplified assumptions: injury severity, treatment duration, bills, and generalized categories like pain and suffering. That’s helpful for education, but it doesn’t account for the way a real claim is built locally—especially when the facts are disputed.
In Salt Lake City, many cases involve common fact patterns that are hard to reduce to a form:
- Long waits between appointments at busy clinics or specialty practices, followed by worsening symptoms
- Complex chronic conditions managed across multiple providers, where causation can be contested
- Post-procedure follow-up gaps (missed imaging, delayed re-evaluation, unclear discharge instructions)
- Care delivered across settings (hospital, urgent care, rehab, outpatient therapy), which can complicate the timeline
An AI estimate usually can’t see those workflow issues—or the medical reasoning that ties them to harm.


