Misdiagnosis claims aren’t limited to one setting. In Columbus, errors often show up in the same real-world contexts people recognize—urgent timelines, heavy patient volumes, and fragmented records.
Some patterns we see include:
- ER or urgent care visits during peak hours (evening rush, weekend nightlife, after events), where triage relies on risk scoring or automated documentation.
- Imaging or radiology support where AI flags “possible” findings but the final interpretation and follow-up don’t match the objective record.
- Repeat visits—you return because symptoms worsen, only to learn later that earlier test results were overlooked, delayed, or not escalated.
- Lab and result handling—abnormal results may sit in the system without timely action, especially when multiple providers touch the file.
- Discharge and follow-up failures—instructions are vague, referrals stall, or a “watch and wait” plan ignores the severity suggested by earlier data.
AI involvement may not be the headline reason for the error, but it can shape what clinicians see first, what gets prioritized, and what gets documented. The legal question is whether the care team appropriately used the tool and whether they acted reasonably on the information available at the time.


