A Visual Decision Support Tool for Appendectomy Care

Abstract

Appendectomy is the most common abdominal surgical procedure performed in children in the United States. In order to assist care providers in creating treatment plans for the postoperative management of pediatric appendicitis, we have developed a predictive statistical model of outcomes on which we have built a prototype decision aid application. The model, trained on 3724 anonymized care records and evaluated on a separate set of 2205 cases from a tertiary care center, achieves 97.0% specificity, 25.1% true sensitivity, and 58.8% precision. We have also built an interactive decision support tool augmented with simple visualization techniques designed for clinicians to use in the course of making care decisions (e.g., discharge) and in patient/stakeholder communication. Its focus is on end-user ease of use and integration into existing clinician workflows, and is designed to evolve its predictions as more and better data become available.