A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.


This paper describes a natural language processing (NLP)-based clinical decision support (CDS) system that is geared towards colon cancer care coordinators as the end users. The system is implemented using a metadata- driven Structured Query Language (SQL) function (discriminant function). For our pilot study, we have developed a training corpus consisting of 2,085 pathology reports from the VA Connecticut Health Care System (VACHS). We categorized reports as “actionable”- requiring close follow up, or “non-actionable”- requiring standard or no follow up. We then used 600 distinct pathology reports from 6 different VA sites as our test corpus. Analysis of our test corpus shows that our NLP approach yields 98.5% accuracy in identifying cases that required close clinical follow up. By integrating this into our cancer care tracking system, our goal is to ensure that patients with worrisome pathology receive appropriate and timely follow-up and care.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science