In many modern medical settings, clinicians may rely on tools that “flag” risks, suggest likely conditions, or route patients to certain tests. The issue isn’t that technology is always harmful—it’s that the legal system looks at whether the care team met the standard of care for the information they had at the time.
In Bentonville, we commonly see diagnostic error fact patterns tied to real-world workflow pressures, such as:
- Repeat visits for the same or worsening symptoms while earlier results weren’t acted on decisively.
- Abnormal lab or imaging findings that were buried in the record, delayed in review, or not escalated to the right provider.
- Triage decisions that sent a patient to the wrong level of care (urgent vs. emergency vs. specialty follow-up).
- Documentation shortcuts—especially when care is split across multiple facilities or providers.
When AI tools are part of the process, investigators often focus on how the tool’s output was communicated, verified, and used alongside clinical judgment.


