Beyond Business Computing: Scientific Computing
Most healthcare software is built the way business software is built: define requirements, write rules, ship features. That approach works for scheduling and billing. It fails at patient identity, because identity isn’t a business rule problem — it’s a measurement problem.
Two records either represent the same patient or they don’t, and that question can’t be answered by a checklist of if-then logic. It has to be answered the way a scientific question is answered: form a hypothesis about what a match looks like, test it against real data, measure the error rate, and refine the model. That’s the scientific method, and it’s the foundation of every product Avant builds.
Our products are built using scientific computing — probabilistic models, statistical confidence scoring, and algorithms validated against measured outcomes — rather than the rules-based, deterministic logic that business computing relies on. That distinction is why our products can hold up against the reality of healthcare data: inconsistent, incomplete, and never quite as clean as a business rule assumes it will be.
Patient Matching →
Our Latent Feature Detection (LFD) algorithm identifies duplicate and matching patient records with a multi-dimensional model — demographic, biometric, and clinical signal combined — that outperforms legacy probabilistic linkage.
Clinical Data Errors →
Detection and remediation for overlay records, wrong-patient errors, and other clinical data integrity issues that put patient safety and compliance at risk.
Patient Search →
Fault-tolerant, enterprise patient search that works with any EHR to confidently locate the correct patient record — even when names are misspelled or the search query doesn’t exactly match.