Surgical Risk Prediction Suite
is a clinical decision support tool
authored by
powered by
Surgical Risk Prediction Suite (SRPS) helps orthopaedic surgeons navigate medical risk assessment for hip, knee, and shoulder arthroplasty patients.
SRPS references variables from a patient’s chart, calculates risk levels, then provides patient risk stratification directly in your existing EHR workflow.
Available for three high-volume arthroplasty use cases
SRPS consists of a set of risk prediction tools that use data from a patient’s electronic health record (EHR) and customizable risk thresholds to identify healthy and at-risk surgery candidates and surfaces the data into existing workflows. The resulting patient stratification information enables providers to proactively modulate the appropriate levels of care to each patient in an individualized and personalized manner. SRPS is currently available for three high-volume orthopaedic surgery use cases: total hip, knee, and shoulder arthroplasty.
The SRPS algorithm was developed by the Duke Predictive Modeling Team within Duke Orthopaedic Surgery and operationalized by the digital health specialists at Pattern Health. Validation partners at Rush/Midwest Orthopaedics and New York University Orthopaedic Surgery have additionally conducted, or are in the process of completing, independent validations of SRPS.
SRPS provides value to systems operating on value-based or fee-for-service reimbursement models.
Industry-Leading Predictive Capabilities
SRPS stratifies patients as “healthy” or “unhealthy” relative to specific adverse outcomes. This allows providers to counsel patients more effectively and choose the most appropriate treatment pathway and resource level. The predictive technology boasts industry-leading AUC scores, detailed below. Each provider has the flexibility to customize the risk threshold cutoffs based on the particular priorities, resources, and timelines of their organization.
Supporting Publications
About the Author
Duke Orthopaedic Surgery
Duke Orthopaedic Surgery wanted a sophisticated and systematic way of stratifying patients for high-volume arthroplasty operations. They looked at all the solutions on the market and found them lacking. Existing solutions had large margins for error, a lack of complexity and supporting validation data, and no way to incorporate the process into existing workflows.
The SRPS algorithm was developed by the Duke Predictive Modeling Team within Duke Orthopaedic Surgery and operationalized by the digital health specialists at Pattern Health. Validation partners at Rush/Midwest Orthopaedics and New York University Orthopaedic Surgery have additionally conducted, or are in the process of completing, independent validations of SRPS.
