In a community shaped by commuting and multi-step care, diagnostic errors frequently surface as a chain of events rather than a single mistake. For example, a patient may:
- be seen for symptoms during a busy clinic visit,
- receive initial testing (or imaging) with results that aren’t clearly acted on,
- face a referral or handoff to another provider,
- experience a delay while records move through systems,
- and only later learn the diagnosis was missed or postponed.
Whether AI was involved or not, the legal question is typically not just what the diagnosis ultimately was—it’s whether the earlier evaluation met the accepted standard of care and whether a timely, accurate diagnosis could reasonably have changed the outcome.


