In the Chicago suburbs, diagnostic errors often surface in familiar settings—busy clinics, high-volume imaging centers, and emergency departments managing many patients at once. AI and automated workflows can appear “helpful,” but they don’t replace clinical judgment.
Common Arlington Heights scenarios we investigate include:
- Urgent care or ER discharge with unclear follow-up: A patient is released with instructions that don’t match the severity suggested by test results or risk factors.
- Imaging or lab review bottlenecks: Results may sit in a workflow longer than they should, or they may be misinterpreted during high-volume periods.
- Clinical decision support treated as definitive: A tool flags a likely condition, but clinicians don’t adequately verify it against objective findings.
- Multiple visits before the correct diagnosis is recognized: Symptoms can be minimized or attributed to the “most likely” explanation—until the condition progresses.
These aren’t just “technology problems.” The legal focus is on process, oversight, and decision-making—including how information was communicated and acted on.


