PRISM

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PRISM ​

Predictive Recommendations for Improved Screening in Medicine ​

Transforming insurance claims data into early disease detection through AI pattern recognition

Key Features ​

🎯 Pattern Recognition for Early Detection ​

PRISM analyzes comprehensive insurance claims data to identify healthcare utilization patterns that predict beneficial screening opportunities—often years before conditions are typically diagnosed.

🤖 Ensemble of 100 Independent AI Models ​

One hundred specialized models, each trained on completely separate patient populations, create robust consensus-based recommendations through natural cross-validation.

🔒 Completely Anonymous Data ​

Privacy-through-architecture design uses truly anonymous data from the start—not de-identified, but genuinely anonymous—eliminating re-identification concerns entirely.

✅ Constructive-Only Architecture ​

The system can only suggest additional screening, never deny or restrict care. This constraint is built into the fundamental architecture, not just policy.

📊 Three-Pattern Learning ​

Automated training extracts GOOD (early detection), BAD (late diagnosis), and NOPE (alternative diagnosis) examples to teach models optimal screening timing.

🤝 Public Benefit Corporation ​

Patient outcomes are legally enshrined as the primary objective, creating alignment between doing good and doing well.

Welcome to PRISM ​

The Challenge ​

Healthcare costs continue rising while millions live with undiagnosed conditions that could be detected and treated early. Primary aldosteronism—PRISM's demonstration condition—affects up to 30,000 people per million population, yet 95% remain undiagnosed until serious complications develop. These patients cycle through years of ineffective treatments and escalating medications before receiving a simple blood test that could have identified their condition much earlier.

This pattern repeats across dozens of conditions where early detection through routine screening dramatically improves outcomes and reduces costs. The knowledge exists. The tests exist. The treatments exist. What's missing is systematic identification of which patients would benefit from which screening when.

The PRISM Solution ​

PRISM transforms insurance claims data—the comprehensive record of every patient's healthcare journey—into a pattern recognition system that identifies beneficial screening opportunities. Using an ensemble of 100 independent AI models trained on completely anonymous medical histories, PRISM recognizes healthcare utilization phenotypes that predict when established diagnostic tests are likely to yield actionable results.

How It Works ​

  1. Data Processing: Insurance claims are converted to a standardized, completely anonymous format capturing the full medical journey
  2. Pattern Recognition: 100 independent models learn to recognize healthcare utilization patterns that precede successful early diagnosis
  3. Consensus Voting: Multiple models must independently agree before generating screening suggestions
  4. Physician Notification: Suggestions are communicated to primary care physicians who maintain complete clinical autonomy
  5. Results Validation: PRISM succeeds only when suggestions lead to documented successful early detection

Core Innovations ​

Pure Sequence Completion ​

PRISM uses deliberately simplified AI—models trained to continue medical billing sequences without reasoning, explanation, or complex prompting. This radical simplicity eliminates failure modes that plague sophisticated systems while focusing all computational capacity on pattern recognition.

Ensemble Architecture ​

Complete data isolation between 100 models creates natural cross-validation. When models trained on entirely different patient populations independently suggest the same screening, it provides powerful evidence of genuine medical patterns.

Privacy by Architecture ​

PRISM operates on truly anonymous data—not de-identified records that could potentially be re-linked, but data that never contained identifiable information. This architectural approach to privacy eliminates surveillance and discrimination concerns.

Results-Based Alignment ​

PRISM earns revenue only when screening suggestions lead to documented successful early detection. This model creates powerful incentives for accuracy while ensuring patients face no financial barriers to suggested screening.

Get Started ​

For Healthcare Professionals ​

For Insurance Companies ​

For Researchers & Data Scientists ​

For Technical Teams ​

Core Principles ​

Patient Benefit First ​

PRISM operates as a Public Benefit Corporation with patient health outcomes legally enshrined as the primary objective. Every architectural decision, business practice, and operational choice prioritizes patient benefit above all other considerations.

Constructive Only ​

The system's architecture prevents care denial by design. PRISM can only suggest additional screening—it cannot and will never restrict, deny, or limit access to care. This isn't policy; it's fundamental to how sequence completion works.

Privacy Through Architecture ​

Complete anonymity from the start eliminates privacy concerns that typically constrain healthcare AI. The system never has access to identifiable patient information, preventing surveillance or discrimination by design.

Open Collaboration ​

Insurance companies can share trained models (but never patient data) through secure repositories, creating collective intelligence that improves pattern recognition for all participants while protecting competitive and privacy interests.

Evidence-Based Validation ​

Perfect retrospective-prospective equivalence enables comprehensive validation across millions of historical cases, demonstrating exactly how the system would have performed in real-time scenarios.

The Demonstration: Primary Aldosteronism ​

Primary aldosteronism serves as PRISM's proof-of-concept condition because it perfectly embodies the patterns the system is designed to detect:

  • High prevalence: 5-10% of people with hypertension (15,000-30,000 per million population)
  • Massive underdiagnosis: 95% remain undiagnosed until complications develop
  • Clear diagnostic test: Simple blood test (aldosterone and renin levels)
  • Dramatic intervention difference: Early treatment prevents complications costing hundreds of thousands of dollars
  • Distinctive patterns: Escalating medications, specific symptoms, and lab abnormalities visible in claims data

Success with primary aldosteronism validates the technical architecture for expansion to dozens of additional conditions sharing similar characteristics.

Explore the Documentation ​

The complete PRISM documentation is organized into focused sections covering every aspect of the system:

Start with the Executive Summary for a comprehensive overview, or dive into the Table of Contents to explore specific topics.

PRISM transforms a persistent healthcare challenge—systematic underutilization of beneficial screening—into a scalable technology solution that creates value for all stakeholders while prioritizing patient benefit above all other considerations.