PRISM Initiative: Executive Summary
The Problem
Healthcare costs continue to spiral upward while millions of Americans live with undiagnosed conditions that could be detected and treated early. Primary aldosteronism—a hormonal cause of high blood pressure used as a representative example throughout PRISM documentation—illustrates the challenge: affecting up to 30,000 people per million population, yet 95% remain undiagnosed until serious complications develop. These patients cycle through years of ineffective treatments, emergency visits, and escalating medications before receiving a simple blood test that could have identified their condition months or years earlier. The total cost difference between early detection and late diagnosis often exceeds hundreds of thousands of dollars per patient in prevented complications.
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 Solution
PRISM (Predictive Recommendations for Improved Screening in Medicine) transforms insurance claims data—the comprehensive record of every patient's healthcare journey—into a pattern recognition system that identifies beneficial screening opportunities years before conditions are typically diagnosed. 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.
The system operates on a fundamental principle: it can only suggest additional screening, never deny or restrict care. Every suggestion represents an opportunity for beneficial early intervention, communicated to primary care physicians who maintain complete autonomy over clinical decisions. PRISM succeeds only when its suggestions lead to documented successful early detection, aligning technology incentives perfectly with patient benefit.
Core Innovation
PRISM's breakthrough lies in recognizing patterns in how patients interact with the healthcare system as conditions develop, rather than waiting for obvious clinical symptoms. A patient developing primary aldosteronism creates a distinctive signature: escalating blood pressure medications, emergency visits for headaches, potassium abnormalities, and specialist consultations. Each provider sees only their piece, but insurance claims capture the complete pattern across years of care.
The system processes these patterns with computational consistency across all populations, applying the same algorithmic attention to medical histories regardless of where care was received or how it was accessed. This uniform pattern recognition may surface screening opportunities that traditional care delivery might miss due to time constraints, fragmentation, or systemic factors that affect care delivery in different settings.
The system processes completely anonymous data—not de-identified but truly anonymous from the start—eliminating patient re-identification concerns entirely. This privacy-through-architecture approach enables pattern recognition across millions of patients without the privacy constraints that typically limit healthcare AI applications.
The technical architecture deliberately prioritizes reliability over sophistication. Rather than complex AI systems that might fail unpredictably, PRISM uses "intentionally broken" models that can only continue medical billing sequences. One hundred independent models, each trained on separate patient populations, must reach consensus thresholds before generating suggestions. This ensemble approach ensures robust pattern recognition while preventing spurious patterns from affecting patient care.
Business Model Alignment
PRISM operates on results-based compensation, earning revenue only when screening suggestions lead to documented successful early detection. Insurance companies pay a pre-negotiated percentage of the savings generated by preventing complications through early intervention. This model creates powerful incentives for accuracy: excessive false positives generate costs without savings, while missed opportunities generate no revenue.
The system commits to ensuring patients face no financial barriers to suggested screening tests, removing cost obstacles that might prevent beneficial early detection. This commitment reflects PRISM's fundamental orientation toward patient benefit rather than cost reduction as the primary objective.
Implementation Strategy
PRISM integrates into existing insurance operations without requiring workflow changes from providers or patients. The system processes claims data that insurance companies already collect, using existing communication channels to deliver suggestions to primary care physicians. On-premises hardware clusters, managed remotely by PRISM's team, provide the computational infrastructure while maintaining complete data isolation within insurance company facilities.
The zero-integration approach extends to regulatory positioning. PRISM operates within established Clinical Decision Support frameworks, suggesting established diagnostic tests that physicians can freely accept or decline. The system cannot order tests, cannot interpret results, and cannot make diagnoses—it simply identifies patterns suggesting that established screening protocols might be beneficial.
Safeguards and Ethics
PRISM's architecture embeds multiple safeguards against misuse. The system functions exclusively as a medical sequence continuation engine—it can only generate potential next events in a patient's medical journey, suggesting diagnostic tests when patterns indicate they might naturally follow. This pure pattern continuation architecture means the system literally cannot output anything except possible forward sequences. Complete data anonymity prevents surveillance or discrimination concerns. The ensemble requirement filters out spurious patterns that might appear in individual models.
The Public Benefit Corporation structure legally enshrines patient benefit as the primary objective, even above shareholder returns. This isn't mere mission alignment but legal requirement that persists regardless of ownership changes or market pressures. Board members have fiduciary duties to consider patient outcomes alongside financial performance.
Evidence Base
PRISM's validation approach achieves perfect equivalence between retrospective and prospective analysis. By truncating historical patient data at specific points and having models predict what should come next, the system demonstrates exactly how it would have performed in real-time scenarios. This methodology enables comprehensive validation across millions of historical cases without waiting years for prospective studies.
Initial focus on primary aldosteronism provides proof of concept for the broader approach. With clear diagnostic tests, established treatments, and dramatic differences between early and late intervention costs, this representative example demonstrates PRISM's value proposition while validating the technical architecture for expansion to additional conditions.
Collaborative Framework
PRISM's open collaboration model enables insurance companies to share trained models (but never patient data) through secure repositories, creating collective intelligence that improves pattern recognition for all participants. Each organization contributes models trained on their unique population while benefiting from models trained on millions of additional patients they could never access directly.
This collaboration creates powerful network effects where each new participant improves outcomes for all existing participants. Early adopters gain disproportionate benefits as the collective intelligence grows, while later participants benefit from refined systems and proven value. The model encourages widespread adoption through mutual benefit rather than zero-sum competition.
Team and Organization
PRISM's founding team combines essential expertise: enterprise data systems, insurance operations, and AI/ML optimization. Brian Jorden brings decades of experience building data systems for regulated industries plus the original insight about healthcare utilization phenotypes. Timothy Collins contributes deep insurance industry knowledge ensuring practical deployment and serves as the operational leader driving project momentum forward. Ryan Herchig provides computational expertise for scalable implementation and academic validation.
The team's complementary backgrounds ensure PRISM addresses real healthcare challenges with technically sound solutions that can actually be implemented within complex insurance and healthcare environments. Advisory relationships with medical professionals, researchers, and industry experts provide ongoing guidance for clinical relevance and regulatory compliance.
Future Vision
PRISM represents a new approach to healthcare AI that creates value through beneficial screening identification rather than care restriction or administrative efficiency. Success with the primary aldosteronism demonstration enables rapid expansion to additional conditions sharing similar characteristics: clear diagnostic tests, significant early-versus-late intervention differences, and sufficient prevalence for pattern learning.
The platform potential extends to dozens of conditions where systematic screening could prevent complications and improve outcomes. Each new condition leverages the same infrastructure, data processing, and consensus mechanisms, enabling rapid expansion without architectural redesign.
Beyond individual screening suggestions, PRISM's population-scale pattern analysis may reveal previously unknown relationships between medical events, potentially advancing understanding of disease progression and healthcare delivery. These insights, validated through peer review and academic collaboration, could contribute to medical knowledge while demonstrating the system's scientific rigor.
Investment Opportunity
PRISM creates genuine value through early detection rather than shifting costs between stakeholders. When successful early detection prevents hundreds of thousands in complications, sharing a percentage of those savings with PRISM while retaining the majority creates sustainable economics for all parties. Patients avoid suffering and disability. Insurance companies reduce net costs. Healthcare providers deliver more effective care. PRISM generates revenue proportional to value created.
The Public Benefit Corporation structure attracts mission-aligned investment while providing legal protection for patient-focused decisions. Traditional venture capital models work within this framework because genuine patient benefit through early disease detection creates enormous economic value that supports strong returns.
The market opportunity scales with healthcare costs and the aging population. As more conditions become amenable to early detection and more data becomes available for pattern recognition, PRISM's capabilities and value creation expand. The collaborative model accelerates this growth by rapidly improving pattern recognition capabilities across all participants.
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. This isn't healthcare technology that might help; it's healthcare technology that demonstrably does help, with built-in safeguards ensuring it continues helping as it grows.