AI tools can be tempting because they offer quick ranges. They typically take inputs like injury severity, length of treatment, and medical bills, then apply simplified assumptions.
The problem is that Minnesota malpractice cases turn on proof, not just the presence of harm. A calculation can’t reliably account for:
- whether the provider’s actions matched the accepted standard of care at the time
- whether the care actually caused the specific injury (causation is often disputed)
- what documentation exists in the chart to support your timeline
- how damages are supported with records (not estimates)
For Chaska patients, that documentation gap is common in real life—for example, when care is split between clinics, urgent care visits, imaging centers, and follow-up appointments. AI doesn’t know where those records live, what’s missing, or what must be tied together.


