You don’t have to prove that “AI caused everything” to have a potential claim. In many cases we see, the issue is how information traveled through the system—how results were interpreted, routed, or acted on.
Common scenarios include:
- Imaging or scan interpretation delays tied to workflow backlogs or prioritization decisions
- Risk scoring or triage tools that influenced urgency, follow-up timing, or disposition
- Clinical decision-support prompts that were treated as decisive when they should have been verified against objective findings
- Lab result handling where abnormal values were not flagged, acknowledged, or escalated promptly
In Grand Forks, these problems can be amplified when patients are trying to manage fast-moving schedules—especially when a symptom starts during a busy work week, a weekend visit, or a period when follow-up appointments take time to obtain.


