A mobile clinical decision-support application developed with the University of Pennsylvania to translate complex patient variables into risk scores.
Healthcare & Life Sciences
HerniaRisk: Surgical Risk Prediction Platform
The Story
PennMedicine had years of surgical research sitting in published papers that were valuable, validated, and largely out of reach for a surgeon mid-procedure in a hospital with no time to consult a library.
The Challenge
Bridging the gap between surgical research and clinical practice. Translating complex historical patient data into clear, actionable risk scores at the moment of care.
The Solution
We took that research and put it in their pocket. Enter a few patient details, get a risk score backed by one of the most respected surgical programs in the country.
The Outcome
“By putting PennMedicine’s research directly into a clinical tool, surgeons around the world now have instant, evidence-backed risk scores at the moment they need them most. Guesswork is removed from pre-operative planning, and care teams can move from generalized assumptions to individualized, data-driven surgical strategies; ultimately closing the gap between research and real-world practice.”
Logic Layer Implementation
Validated ML Model
Predictions powered by machine learning trained on PennMedicine’s patient outcomes data, continuously refined as new research emerges.
Offline-First Architecture
Works seamlessly across iOS and Android in low-connectivity settings, with HIPAA-compliant data handling and the option to log anonymized cases.
Individualized Scoring
Instant probability scores based on BMI, wound classification, and surgical approach.
Global Accessibility
Multilingual interface with evidence-based recommendations on surgical technique and post-operative follow-up.
ML Model
HIPAA Compliant
Offline-First
Decision Support
Case Logging
