Move from reactive compliance burden to proactive clinical intelligence with our healthcare-native generative AI enterprise platform.
Across medical record abstraction, research workflows, quality reporting, and compliance, manual data work has become a major drain on performance. Siloed systems slow decision-making. Fragmented data adds variation.
Outsourced processes cost billions and still deliver inconsistent results. When clinicians spend nearly a third of their time on administrative tasks, the equation is broken – and it’s time to fix it.
Health systems need a smarter way to manage clinical data, one that restores time, improves consistency, and strengthens value-based care delivery.
A closed loop, starting with identification and ending with actionable intervention, purpose-built for healthcare settings, includes the following components
AI-guided, criteria-driven patient/case identification and automated assignment.
Native SLMs extract discrete elements from unstructured notes (patents filed for Gen-AI abstraction).
Condenses long clinical narratives to reduce cognitive load.
Predictive categorization (e.g., high/medium/low) to prioritize interventions.
Enriched datasets feed quality, utilization, and performance dashboards.
Recommendations to close gaps and improve adherence.
Automated validation and standards-compliant file delivery to governance/registry bodies.
A clinical intelligence loop feeds insights back into the system for continuous learning and improvement, ensuring proactive care, fewer human errors and data-driven decisions aligned with evidence-based guidelines.
A flexible low code / no code maker platform that enables fast setup of new registries, workflows, and clinical programs, without heavy engineering lift.
Prebuilt healthcare connectors and data pipelines that integrate with Electronic Health Records (EHRs) and ancillary systems, ensuring smooth ingestion, orchestration, and real-time data flow.
Enterprise-grade privacy securing Protected Health Information (PHI) with governance frameworks baked into every layer, ensuring secure, compliant, and responsible AI use.
A scalable foundation with multi-tenancy, usage controls, licensing, and configurability designed for external deployments across partners and health systems.
AI workflow for registry abstraction and compliance, e.g., Get with The Guidelines (GWTG), National Cardiovascular Data Registry (NCDR), Society of Thoracic Surgeons (STS), Vermont Oxford Network (VON), Vascular Quality Initiative (VQI).
Multi-vendor Patient-Reported Experience Measures (PREM) survey compliance with analytics.
AI-powered cancer case identification streamlines review
and accuracy.
Systematic monitoring to prevent diagnostic harm.
Accurate Surgical Site Infection (SSI) detection, abstraction, and National Healthcare Safety Network (NHSN) submission.
Centralized reporting aggregator that unifies insights across Business Intelligence (BI) tools.
Industry-leading solution for early infection risk prediction, enhancing patient safety and care quality.
Providence built an AI-driven infection risk prediction model using data from 83,000 patients across 56 facilities and 1.3M records, delivering real-time risk detection, easing the way for infection preventionists to deliver proactive and life-saving care for high-risk patients.
The model has achieved an AUC of 0.93 & 85% balanced accuracy, outperforming legacy models, providing real-time predictions within the highest risk window of 24-72 hours.
