AI and automated systems are increasingly part of the healthcare workflow—sometimes in ways patients never see. In practice, problems often don’t look like “the computer was wrong.” They look like:
- A risk score or triage pathway that routed a patient the wrong way
- Imaging or lab review that was treated as “good enough” without appropriate escalation
- Documentation tools that shaped what clinicians noticed (or didn’t notice)
- Delayed follow-up on abnormal results when systems rely on automated prompts
In Brandon, these issues can be tied to real-world constraints: time pressures during busy clinic hours, ER throughput, and the way abnormal findings are handled after discharge. A delayed diagnosis claim often turns on whether the healthcare team responded appropriately to the information they already had.


