Medical diagnostic mistakes rarely come from one “bad decision” in isolation. In practice, diagnostic errors can emerge from a chain of moments—symptoms documented one way, test results interpreted another way, follow-up handled differently than expected, or urgent changes not escalated quickly.
In and around Grand Rapids, you may see patterns such as:
- Delayed follow-up after abnormal results while a patient is trying to secure the next step.
- Triage and routing issues that send a person down the wrong care pathway.
- Imaging or lab interpretation delays that push the correct diagnosis later than it should have arrived.
- Communication gaps between visits, especially when care shifts between outpatient settings, urgent care, and hospital departments.
- Automated documentation or risk-scoring that influences what clinicians focus on—sometimes without a clear match to the patient’s reported symptoms.
If you suspect an AI-assisted system influenced the decision-making, the key question becomes: How was the output used, verified, and documented at the time?


