Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
ORCID
https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Forcid.org%2F0009-0008-7726-5898&data=05%7C02%7Crichmoje%40dukes.jmu.edu%7C99a69ba125c04dbd4fcf08de0abba20b%7Ce9333c23cac742f499895cee3d4a79c0%7C0%7C0%7C638959998176792947%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Oz3Luz92eMftpcVC1KLBNspE4hm0qb4j%2B65Z04XGr%2F0%3D&reserved=0
Date of Graduation
12-13-2025
Semester of Graduation
Fall
Degree Name
Doctor of Nursing Practice (DNP)
Department
School of Nursing
First Advisor
Jeannie Corey
Abstract
Abstract
Community Health Centers (CHCs) and Federally Qualified Health Centers (FQHCs) provide primary care to over 20 million people in the US, many of whom are low-income, underinsured, or uninsured in both urban and rural areas. Missed appointments are a challenge in safety-net clinic settings that care for vulnerable populations, and high no-show rates negatively impact the quality of care, exacerbate disparities in access to care, waste resources, and reduce operational efficiency. This quality improvement project sought to address this challenge by implementing a predictive modeling tool and targeted patient reminders across a multi-clinic FQHC in Central Virginia. The aim was to reduce the no-show rate by at least 5% over six months by using a predictive modeling tool to identify patients with a ≥50% probability of no-show and making targeted reminder calls one day prior to their appointment. The Institute for Healthcare Improvement’s (IHI) Triple Aim framework and a Plan-Do-Study-Act (PDSA) design guided the project's development and implementation. The intervention achieved a 3% absolute reduction in no-show rates (from 15% to 12%), which was statistically significant (p=0.017). The 3% reduction also translates to a 20% relative change, further highlighting the impact. Improved attendance rates led to approximately 2500 completed visits, preserving an estimated $582,936 in revenue. Reasons for no-shows included forgetfulness, transportation, and financial concerns. Barriers included staff workload and discontinuation of the predictive tool, while training and feedback facilitated implementation. Findings indicate that predictive modeling combined with reminder calls can reduce no-shows, reduce disparities in healthcare access, and provide a financially sustainable model for healthcare systems. Recommendations include exploring cost-effective alternatives to predictive modeling, developing community partnerships for cost-sharing, and integrating targeted outreach into clinic workflows.
Keywords: No-show, predictive modeling, Federally Qualified Health Center, quality improvement, patient outreach, reminder calls
Included in
Community Health and Preventive Medicine Commons, Family Practice Nursing Commons, Health and Medical Administration Commons, Health Information Technology Commons, Health Services Administration Commons, Health Services Research Commons, Medical Education Commons, Nursing Administration Commons, Other Public Health Commons, Public Health and Community Nursing Commons, Public Health Education and Promotion Commons
