Diagnostic mistakes don’t always look dramatic. Often, they show up as a pattern:
- A first visit that moves quickly—symptoms are documented, but the next step (repeat testing, specialist referral, or close follow-up) isn’t completed.
- Results that appear “normal” at the wrong time—sometimes the problem is that abnormal findings weren’t escalated, or they weren’t clearly communicated.
- Follow-up that doesn’t happen—especially when patients are juggling work, transportation, or childcare.
- Care transitions—for example, urgent care-to-hospital transfers or ER discharge instructions that are hard to execute on schedule.
In cases involving AI or automated workflow tools, the concern is typically not that technology “caused everything.” Instead, the issue is often how the tool’s output was used: whether clinicians treated it as definitive, whether limitations were accounted for, and whether the system’s recommendations were documented accurately.


