Online tools tend to assume that every claim follows the same path: diagnosis → treatment → work restrictions → impairment discussion → settlement. In real life, New York claims often diverge because of how evidence is built.
In Spring Valley, common factors that can make AI estimates misleading include:
- Delayed or inconsistent documentation after an injury (for example, waiting too long to report symptoms clearly to the treating provider).
- Work-impact disputes when restrictions change but are not updated in medical notes.
- Earnings complexity for workers who rely on overtime, shift differentials, or variable hours.
- Incident-account challenges when the event description doesn’t match early paperwork or contemporaneous records.
AI tools can’t reliably account for these real-world gaps. If those gaps exist in your file, an “average” range can be low—even when you’re entitled to more.


