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An investigation revealed that Medicare Advantage insurers are using a proprietary predictive algorithm, nH Predict by NaviHealth, to determine when to cut off payment for patient care. The algorithm often produces rigid discharge predictions that conflict with individual medical needs, leading to widespread payment denials for elderly patients. These denials frequently force patients to pay for their own care or forgo treatment, despite many denials being overturned upon appeal.
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.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
Also classified as
Insurers overrely on the predictive algorithm as a hard-and-fast rule to deny care, ignoring individual clinical realities.
The nH Predict algorithm fails to perform reliably or effectively under varying patient conditions, generating rigid, speculative predictions that do not account for individual clinical realities.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The incident was caused by the nH Predict AI system's inaccurate and rigid predictions of patient recovery times after its deployment.
Added categories peak at 2: Minor (Psychological Harm, Indirect)
Private insurers utilized a proprietary AI algorithm, nH Predict, to automate and restrict post-acute care coverage for elderly Medicare Advantage patients. While the incident has caused significant financial and physical distress for thousands of patients and prompted federal regulatory intervention, its direct implications for national security remain minor and manageable within standard domestic regulatory frameworks.
The nH Predict system acts as an Assistant because it generates assessments and length-of-stay predictions to guide and inform human insurance coordinators and medical directors, rather than executing coverage terminations autonomously.
High Risk: The system is used in healthcare and insurance decision-making to determine patient care and coverage, which has significant implications for the safety and fundamental rights of vulnerable elderly patients.
The primary function of nH Predict is to allocate post-acute care resources by predicting the precise length of stay and therapy hours a patient will require.
The report provides extensive, high-quality investigative journalism from STAT, including specific patient case studies, court rulings, and statistics on appeals, which clearly document the role of the nH Predict algorithm in driving insurance denials.
Insurers argue the tool is merely a non-binding guide and that denials are based on standard Medicare criteria, suggesting the harm is entirely due to human administrative decisions rather than the AI itself.
Technology
Proprietary Black-Box Algorithm
nH Predict algorithm is proprietary, preventing independent review of its logic.
Untested Real-World Performance
NaviHealth published no scientific studies assessing algorithm performance.
Data Inputs
Averaged Statistical Database
Database of 6 million patients abstracts individuals into generic statistics.
Ignoring Individual Comorbidities
Input data fails to account for complex, individual patient health histories.
Human Factors
Blind Reliance on Predictions
Insurers treat suggestive predictions as hard-and-fast rules for discharge.
Inability to Challenge Algorithm
Patients and doctors are unable to see or question the AI calculations.
Process and Methods
Lack of Algorithmic Transparency
AI reports are run secretly and not shared with patients or providers.
Rigid Prior Authorization Process
Insurers use AI to systematically deny care and force long appeals.
Management
Profit Prioritization over Care
Insurers acquire AI tools to aggressively cut costs and boost profits.
Lack of Quality Controls
Management failed to implement quality controls over AI-driven determinations.
Regulatory Environment
Unregulated Insurance Algorithms
AI tools used for coverage decisions lack FDA or government oversight.
Permissive Medicare Rules
Current regulations allow insurers to use internal proprietary criteria.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
