Creating a Highly Reliable Sign and Symptom Tracking System to Prevent Delays in Cancer Diagnosis
PI: Dr Talya Salant, Bowdoin Street Health Center
CRICO Risk Management Foundation
Avoiding missed or delayed serious diagnoses is at the heart of ambulatory safety-nets –processes that help providers and patients manage diagnostic uncertainty around symptoms. While focused symptom tracking systems have emerged within oncology and in response to COVID-19, highly reliable tracking systems embedded in primary care for symptoms and signs that might portend cancer are notably lacking. This project will design and implement a symptom and sign tracking system (SSTS) embedded in the EHR that ensures reliable tracking of worrisome symptoms to diagnostic elucidation or symptom resolution. Systems engineering methods will be used to understand and improve upon the current state and partner with IS at our home institution to operationalize a symptom tracking system that improves outcomes across a range of qualitative and quantitative measures.
Use systems engineering methods to understand the current state of symptom tracking in primary care across two primary care sites and identify key components of an ideal, equitable, symptom tracking system.
Use a high reliability conceptual approach to develop and implement a sign and symptom tracking system (SSTS) that leverages and links to the EHR.
Use iterative participatory design methods to identify implementation challenges, barriers, and needed refinements to the SSTS.
Demonstrate the benefits of symptom and sign tracking system as compared to usual care in terms of time to diagnosis, time to resolution, user satisfaction, burnout, cost, and health equity.
James Benneyan, PhD, Professor of Industrial Engineering and Operations Research and Director of the Healthcare Systems Engineering Institute at Northeastern University
Dr. Talya Salant, Bowdoin Street Health Center
Dr. Russell Phillips, Center for Primary Care, Harvard Medical School