Risk Stratification-Guided Patient Navigation Model for Student-Run Health Fairs

Abstract

Background: The patient navigation model has been used to connect patients who attend community health fairs with follow-up care. Optimizing the organizational structure of a patient navigation model centered around risk stratification may be important for improving rates of successful healthcare coordination and access. This report describes the experience and lessons learned from implementing and optimizing a patient navigation model to fit the needs of a low-income and ethnically diverse South Florida community.
Methods: A patient navigation model based on an algorithmic risk stratification system was created and implemented with the focus on tailored follow-up and specialized navigator training to achieve successful patient contact and follow-up.
Results: Incorporating patient risk stratification which was used to guide student navigator training and follow-up guidelines led to a hands-on learning experience for medical students with skills that could be applied to clinical practice as well as higher achievement of successful patient contact and navigation outcomes. Over the three years that navigation outcomes were monitored, this system allowed students to successfully complete the navigation process with 52.5% of patients who attended health fairs.
Conclusion: The structure based on risk stratification and set follow-up timeline all contributed to greater success in teaching medical students how to connect patients to local community resources as well as achieving patient contact and navigation outcomes in our patient navigation program serving the South Florida community.