Doctor of Nursing Practice
Dr. Constance Glenn DNP, MSN, FNP-BC, CNE
Janice Tavares APRN FNP-BC
Significance and Background: No- shows are a global problem that creates a significant challenge for the healthcare system. When a patient misses an appointment, it decreases health care staff productivity, creates a waste of resources and it negatively impacts revenue (Lance et. al, 2021). Current evidence supports interventions that incorporate voluminous data and artificial intelligence with strategies such as overbooking, appointment notification systems and financial incentives to reduce outpatient no-shows (Oikonomidi et al., 2022). This project will evaluate the impact of a no-show policy and provide insights to improve primary care attendance and inform decisions.
Purpose: To evaluate an outpatient primary care office adherence to a no-show policy and its impact on practice revenue based on current evidence.
Methods: Plan-Do-Study-Act. Plan- No- show and demographic attributes from March 2022 to February 2023 were discussed. Do- No- show data from March 2022 to February 2023 was collected from the electronic health record. Study- No-show data was analyzed. Act- Present to stakeholders and plan for next PDSA cycle.
Outcome: During a 12-month period, the primary care practice reported 1435 (17%) no-show occurrences, in comparison to the national average no show rate of 18%. The practice implemented a short message service (SMS) appointment notification system. There was a 38% decrease in the average monthly rate of no-shows in the 4 months post SMS implementation. With the United States national average cost of $218 for a primary care visit, a return on investment for SMS implementation was $11,554. Implications from the study suggest that no- show patients have greater than 10 chronic illness’ and are missing follow- up visits. SMS implementation was shown to decrease the number of no-show appointments of patients with chronic illnesses and missed follow up appointments.
Discussion: Appointment reminders reduce no-shows. Artificial intelligence can optimize solutions to mitigate no-shows. Machine learning can be utilized to detect patterns in identifying appointments at high risk of no-show and guide practice decision making. Reducing no-show rates can decrease the cost on the healthcare system, improve resource efficiency and patient outcomes while decreasing loss of revenue.
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Harding, A. S. (2023). Evaluating the impact of a no-show policy: A quality improvement project. [Unpublished DNP project]. Sacred Heart University.
Available for download on Friday, May 10, 2024
Nursing Commons, Primary Care Commons, Quality Improvement Commons
A DNP project submitted in partial fulfillment of the requirements for the degree of Doctor of Nursing Practice, Sacred Heart University Davis & Henley College of Nursing.