OOF-AII-992457
Length of Stay False Diagnosis Cut Off Insurer's Payment for Treatment of Elderly Woman
An elderly Wisconsin woman was algorithmically determined to have a rapid recovery, an output which the insurer based on to cut off payment for her treatment despite medical notes showing her still experiencing debilitating pain.
- Event date
- 2019-06-03
- Country
- United States of America
- Severity
- Low
- Evidence state
- Single-source
Assessment status
Generated from available public incident evidence using OOF® governance methodology. It is not an official investigation, legal determination, regulatory finding, certification, or OOF® Approved Assessment.
View source record from AI Incident Database (Responsible AI Collaborative)
Assessment confidence dimensions
- Source corroboration
- Single-source
- Extraction completeness
- High
- Methodological assignment confidence
- Limited
Measures source-group corroboration only; it does not establish factual truth.
Measures populated source fields after deterministic normalization; it does not measure correctness.
Measures automated assignment support; substantive correctness requires authorized human review.
Known facts from the incident record
- The incident record identifies the event date as 2019-06-03.
- The incident record identifies the location as United States of America.
Reported claims
- An elderly Wisconsin woman was algorithmically determined to have a rapid recovery, an output which the insurer based on to cut off payment for her treatment despite medical notes showing her still experiencing debilitating pain.
Material unknowns
- Independent corroboration is not established in the available incident record.
Primary governance problem
The incident appears to expose a governance problem involving support, determined. Available reporting supports preliminary methodological interpretation, not a finding of fault or non-compliance.
Architecture intelligence
PGA™ — Prediction Governance Architecture
Primary architecture
The reported support, determined concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.
Governance focus: How should support, determined be governed in the conditions described by the available incident evidence?
Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.
CLIA® — Cognitive Governance Intelligence Architecture
Contributing architecture
The reported decision concern falls within the governed questions represented by CLIA® — Cognitive Governance Intelligence Architecture.
Governance focus: How should decision be governed in the conditions described by the available incident evidence?
Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.
ORA™ - Operational Reality Architecture
Contributing architecture
The reported decision, recovery concern falls within the governed questions represented by ORA™ - Operational Reality Architecture.
Governance focus: How should decision, recovery be governed in the conditions described by the available incident evidence?
Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.
Preliminary governance questions
- How should support, determined be governed in the conditions described by the available incident evidence?
- How should decision be governed in the conditions described by the available incident evidence?
- How should decision, recovery be governed in the conditions described by the available incident evidence?
- What operational authority and controls applied to Security Health Plan, Navihealth at the relevant time?
- What evidence would establish how Ai Enabled Decision Support Systems produced or enabled the reported outcome?
Evidence gaps and limitations
- Only one independent source group is represented; corroboration is not established.
- AI technology is not specified in the source record.