Continuous Batch Inference Process
Background Processing Design
PRISM operates as a continuous pattern recognition system, processing batches of patients through scheduled evaluations that run constantly without requiring any triggering event or human initiation. The system maintains regular evaluation cycles, working through patient populations systematically to identify patterns that might suggest beneficial screening opportunities. This proactive approach means pattern recognition happens regardless of whether patients actively engage with the healthcare system.
The continuous batch approach reflects a fundamental truth about preventive healthcare: the patients who most need screening are often those not actively seeking care. Someone developing primary aldosteronism might go years between doctor visits, their slowly worsening hypertension dismissed as stress or aging. By the time symptoms drive them to seek care, complications may have already developed. PRISM's background processing can identify patterns suggesting screening would be beneficial during these silent periods between healthcare encounters.
The batch processing architecture treats patient evaluation as a systematic operation. The system continuously queues patients for evaluation based on various prioritization criteria, processes them through the ensemble models in orchestrated batches, collects predictions, identifies consensus patterns, generates appropriate suggestions when thresholds are met, and delivers them through established channels. Then it continues with the next batch, maintaining constant operation without human oversight or intervention.
This relentless processing helps ensure patients don't fall through the cracks simply because they weren't top-of-mind for their physician during a busy clinic day. It applies pattern recognition systematically across large populations, identifying screening opportunities whether patients see their doctors regularly or rarely. The patterns that suggest beneficial screening get identified based on the medical history in the claims data, not on whether someone remembered to order an evaluation.
Patient Prioritization Logic
The selection of which patients to process involves prioritization strategies that balance multiple objectives: maximizing the likelihood of finding actionable patterns, ensuring timely evaluation when it matters most, and efficiently utilizing computational resources. Multiple approaches can contribute to this prioritization, with the specific combination determined through operational experience and medical advisory input.
Temporal alignment with healthcare encounters represents one potential prioritization factor. Patients with upcoming appointments where screening could naturally be incorporated might receive priority, ensuring any suggestions arrive when they're most actionable. This approach recognizes that screening suggestions are most valuable when they can influence imminent care decisions rather than requiring separate visits.
Vector similarity search offers another prioritization mechanism. Using embedding technology, PRISM can identify patients whose recent medical histories show mathematical similarity to confirmed historical cases where early screening proved beneficial. If certain patterns preceded successful early detection in the past, the system can search for similar patterns across the patient population and prioritize those showing the strongest similarities. This focuses computational resources based on empirical evidence from historical successes.
Various clinical and demographic factors might also influence prioritization. The presence of specific diagnostic codes, recent changes in healthcare utilization patterns, or other signals in the claims data could suggest that pattern recognition would be particularly valuable for certain patients. Medical advisory input shapes how these various factors combine to create effective prioritization strategies.
The prioritization system includes deliberate randomization elements that ensure no systematic bias causes certain patient segments to be consistently processed later or less frequently. While vector similarity and clinical factors influence prioritization, baseline random selection ensures every patient receives periodic evaluation regardless of their medical complexity, care frequency, or provider types. This prevents the system from inadvertently focusing only on patients who already receive regular care while overlooking those with fragmented or episodic healthcare utilization.
The key insight is that prioritization isn't random but strategic, using available information to focus pattern recognition where it's most likely to provide value. The specific strategies will evolve based on empirical results, computational constraints, and medical guidance, always optimizing for maximum patient benefit within available resources.
No Physician Initiation Required
PRISM's automated operation eliminates friction by removing any requirement for physicians to request evaluations or trigger analyses. Physicians don't need to remember PRISM exists, don't need to identify which patients might benefit, don't need to navigate interfaces or submit requests. Pattern recognition happens automatically in the background, with suggestions appearing only when consensus thresholds are met, requiring nothing from providers except clinical judgment about whether to act on suggestions received.
This automation is essential because physician cognitive load already represents a major barrier to optimal care delivery. Primary care physicians managing large patient panels while seeing dozens of patients daily cannot systematically evaluate each one for subtle patterns suggesting screening opportunities. They're focused on immediate concerns, managing chronic conditions, and handling acute issues. Adding another task even requesting pattern analysis would ensure opportunities get missed due to cognitive overload.
Automatic operation also ensures equity in pattern recognition. Without requiring physician initiation, PRISM can apply the same analysis broadly across patient populations regardless of individual physician awareness, enthusiasm, or available time. Geographic variations in practice patterns don't affect whether patients receive pattern analysis. The system operates uniformly, identifying patterns based on medical histories rather than physician engagement.
By removing physician initiation, PRISM sidesteps potential liability concerns that might arise from selective use. Questions about why some patients received analysis while others didn't become moot when the process is automatic and systematic rather than physician-directed. The technology serves as infrastructure that operates continuously rather than a tool requiring active use.
Computational Efficiency
PRISM achieves the computational efficiency necessary for population-scale pattern recognition through systematic optimization across multiple dimensions. These optimizations transform what would otherwise require massive computational resources into something achievable with practical hardware deployments, making continuous batch processing economically viable.
Model architecture selection balances pattern recognition capability against inference speed, ensuring models can process efficiently while maintaining accuracy. Rather than defaulting to the largest available models, PRISM uses appropriately sized architectures that provide necessary capabilities without excessive computational demands.
Precision optimization through quantization and other techniques reduces memory requirements and increases throughput while maintaining pattern recognition quality. Modern optimization frameworks transform trained models into highly efficient inference engines specifically tuned for deployment hardware, implementing various acceleration techniques that multiply performance improvements.
Advanced attention mechanisms and algorithmic improvements provide additional acceleration by reimagining how core model operations execute on modern hardware. These optimizations work at the mathematical level, finding more efficient ways to achieve the same pattern recognition results with less computation.
The combination of these optimizations enables PRISM to process large patient populations without prohibitive hardware requirements. Each optimization compounds with others, creating dramatic improvements in overall system throughput while maintaining the pattern recognition quality necessary for identifying beneficial screening opportunities.
This document details PRISM's continuous pattern recognition operations. The Cluster Architecture Approach document describes the hardware infrastructure that enables this processing. The Zero Integration Burden document explains how this continuous processing remains invisible to healthcare operations. The Insurance Company Unique Vantage Point document explains the data that feeds this continuous process.