Real-World Evidence & Outcomes Analytics in Spine Surgery and Orthopaedic Oncology
Leveraging TRINETX & local registries for complication risk modeling
Real-World Evidence & Outcomes Analytics in Spine Surgery and Orthopaedic Oncology
Overview
Our research leverages large-scale clinical databases and real-world evidence to understand patterns of care, predict outcomes, and identify risk factors in spine surgery and orthopaedic oncology. We utilize advanced analytics to transform clinical data into actionable insights for improving patient care.
Data Sources
TRINETX Network
- Multi-institutional clinical data network
- Real-time access to de-identified patient records
- Longitudinal follow-up across healthcare systems
- Standardized data collection protocols
Local Clinical Registries
- Institutional spine surgery databases
- Orthopaedic oncology patient registries
- Quality improvement databases
- Patient-reported outcome measures (PROMs)
Administrative Databases
- Claims data analysis
- Hospital discharge databases
- National quality registries
- Medicare/Medicaid datasets
Research Applications
Complication Risk Modeling
- Predictive models for surgical complications
- Risk stratification algorithms
- Pre-operative risk assessment tools
- Post-operative monitoring systems
Outcomes Analytics
- Long-term functional outcomes analysis
- Comparative effectiveness research
- Treatment pathway optimization
- Quality metrics development
Population Health Studies
- Epidemiological analyses of spine conditions
- Cancer registry studies in orthopaedic oncology
- Healthcare utilization patterns
- Cost-effectiveness analyses
Analytical Methods
Machine Learning Approaches
- Supervised learning for outcome prediction
- Unsupervised learning for pattern discovery
- Deep learning for complex data relationships
- Ensemble methods for robust predictions
Statistical Modeling
- Survival analysis for time-to-event outcomes
- Propensity score matching for causal inference
- Multilevel modeling for hierarchical data
- Bayesian approaches for uncertainty quantification
Data Mining Techniques
- Natural language processing of clinical notes
- Phenotype extraction from EHR data
- Temporal pattern analysis
- Anomaly detection in clinical pathways
Current Projects
Spine Surgery Outcomes
- Predictive modeling for fusion success rates
- Complication risk assessment in complex spine surgery
- Patient-reported outcome prediction models
- Revision surgery risk factors
Orthopaedic Oncology Analytics
- Survival prediction models for bone and soft tissue sarcomas
- Treatment response prediction
- Metastasis pattern analysis
- Quality of life outcome modeling
Healthcare Delivery Research
- Care pathway optimization
- Resource utilization analysis
- Provider performance metrics
- System-level quality improvement
Technology Stack
- R/Python for statistical analysis and machine learning
- SQL for database querying and management
- Tableau/PowerBI for data visualization
- REDCap for clinical data collection
- Cloud Computing platforms for large-scale analytics
Clinical Impact
Decision Support Tools
- Risk calculators for clinical use
- Treatment recommendation systems
- Outcome prediction dashboards
- Quality monitoring tools
Quality Improvement
- Benchmarking and performance metrics
- Identification of best practices
- Process improvement initiatives
- Outcome optimization strategies
Collaborative Network
We work with:
- Multi-institutional research consortiums
- Clinical quality improvement teams
- Health services researchers
- Biostatisticians and data scientists
- Clinical informaticists
Future Directions
- Integration of genomic data with clinical outcomes
- Real-time predictive analytics in clinical workflows
- Federated learning across healthcare systems
- AI-powered clinical decision support systems
- Patient-centered outcome prediction models
Our real-world evidence research aims to bridge the gap between clinical research and everyday practice, ensuring that data-driven insights translate into better patient outcomes.