Many diagnostic errors aren’t obvious in the moment. In Daphne, we often see issues tied to time pressure and fragmented care—especially when patients cycle through urgent care, imaging centers, emergency departments, and follow-up visits.
Common patterns include:
- Abnormal results not acted on promptly after an ED or urgent care visit (e.g., imaging reads updated later, but treatment didn’t shift quickly enough).
- Symptoms minimized because a condition “looked familiar” during a busy shift—then the correct diagnosis emerged only after worsening.
- AI or automated triage influenced the urgency level, meaning a patient was routed to the wrong workflow or monitored too lightly.
- Decision support recommendations treated like conclusions, rather than one input among many.
- Hand-off gaps between facilities or providers, where the “important detail” never made it into the next note.
The key question isn’t whether AI exists in healthcare—it’s how it was used, what the clinician did with the output, and whether the care team met the appropriate standard of medical judgment.


