This startup provides a clinical performance analytics platform that uses data science to deliver insights for healthcare providers. It helps integrate, store, and analyze cloud-based clinical data to improve patient outcomes, optimize financial performance, and measure the real-world effectiveness of treatments and medical technologies.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare organizations often struggle to effectively integrate and analyze disparate clinical data sources, hindering their ability to improve patient outcomes, optimize financial performance, and measure treatment effectiveness. The lack of a unified platform makes it difficult to identify key drivers of outcomes, track adherence to best practices, and benchmark performance against peers.
Solution
ArborMetrix provides a clinical performance analytics platform that aggregates and transforms real-world data from various sources, including electronic health records (EHRs), claims data, patient-reported outcomes (PROs), and clinical registries. The platform applies advanced analytics and data science methodologies to quantify measurements, identify areas for improvement, and predict at-risk populations. Actionable insights are delivered through meaningful, easily understood visualizations, empowering healthcare providers, payers, government entities, and societies to make data-driven decisions, drive real-time interventions, and optimize clinical and financial performance.
Target Audience
The primary target audience includes healthcare providers (ACOs, health systems, specialty groups), payers, government entities, and medical societies seeking to improve clinical outcomes, optimize financial performance, and advance healthcare research.
Features
- Data ingestion from disparate sources, including EHRs, claims, patient surveys, and clinical abstraction
- Data enrichment processes to ensure a comprehensive knowledge base for quality initiatives
- Advanced analytics to quantify measurements and assess the impact of interventions
- Risk and reliability adjustment methodologies to ensure fair comparisons and account for variability in data quality
- Predictive analytics to forecast optimum care paths and support real-time interventions
- Registry data management to capture, aggregate, and enrich data for research and quality improvement
- Patient cohort identification and analysis to understand subpopulations and treatment effects
- Support for post-market surveillance to examine the real-world safety and effectiveness of medical devices and pharmaceuticals
- Integration of patient-reported outcomes (PROs) to capture patient perspectives and inform care decisions
- Health equity analytics to identify and address disparities in care delivery and outcomes