R2ML delivers rigorous epidemiological analysis, advanced data science, and policy evaluation for federal, state, and local public health agencies. From surveillance system assessments to predictive risk modeling, we help you make data-driven decisions.
Audit your surveillance infrastructure against CDC guidelines. We evaluate data quality, completeness, timeliness, and sensitivity to identify reporting gaps and system inefficiencies.
From cohort studies to policy impact evaluations, we design rigorous studies using causal inference methods to understand health outcomes and disease patterns.
Identify high-risk populations and predict adverse health outcomes using machine learning models. Enable targeted interventions and resource allocation.
Discover patterns of co-occurring conditions using advanced clustering methods. Understand complex patient phenotypes and refine clinical guidelines.
Analyze treatment patterns, follow-up care adherence, and clinical decision-making in behavioral health. Identify gaps and optimize care delivery.
Assess workforce capacity, distribution, and competency. Plan recruitment and training based on data-driven needs assessment.
Integrate EHR, claims, and registry data. Standardize coding systems (ICD-9/10-CM, HCPCS, SNOMED-CT). Build secure, HIPAA-compliant data pipelines.
Extract structured data from clinical notes, provider documentation, and free-text fields. Identify clinical concepts and phenotypes from unstructured text.
Build executive dashboards and public health surveillance dashboards. Enable real-time monitoring of KPIs and critical health metrics.
Led analysis of 41 comorbid conditions in 250K+ TBI patients, identifying unnecessary benzodiazepine prescriptions. Established MHS-wide clinical operations metrics that improved follow-up care coordination and prevented adverse drug interactions across the military health system.
Developed machine learning model that achieved 15% improvement in risk stratification accuracy for high-cost chronic disease patients by integrating social determinants of health with claims data—enabling targeted interventions and resource optimization.
Designed and deployed real-time executive health-services dashboard for the nation's 2nd largest health system, accelerating deployment by 50% while enabling continuous monitoring of workforce and clinical performance metrics.
Identified measurable policy effects: helmet laws reducing TBI hospitalizations by 12% across 40 states, and in-person driver licensing reducing dementia-related crashes by 37-38%. Findings directly informed state DMV and public health policy reforms.
Contact R2ML to discuss how our expertise can address your organization's research and analytics needs.
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