Spokane’s healthcare flow can be fast-paced—particularly around urgent care, ER visits during peak hours, and patients trying to get answers before a condition worsens. Common local scenarios we see include:
- Multiple visits before the correct diagnosis appears (symptoms persist, but the “working diagnosis” doesn’t change quickly enough)
- Abnormal test results not acted on promptly (or follow-up instructions aren’t clear enough to trigger action)
- Imaging or lab review that depends on automated triage and then gets “signed off” without a meaningful reconciliation to the patient’s overall picture
- Care transitions (ER to hospital, hospital to outpatient, urgent care to primary care) where key details get lost or delayed
If AI tools were part of the workflow—whether at the front end (triage/documentation) or behind the scenes (risk estimation, decision support, interpretation assistance)—the question becomes: how did clinicians use the tool’s output, and what safeguards were followed?


