| Description |
Background: No-show appointments are common in primary care, leading to financial burdens for healthcare facilities, limited access for other patients, and adverse effects on patients' health. High no-show rates contribute to poorly controlled chronic conditions like diabetes and heart failure, leading to increased mortality. Contributors to no-shows are multifactorial and include patient and system variables. Understanding these variables is essential to designing interventions that have the potential to decrease no-shows. Local Problem: Primary care clinics within an academic healthcare system in the Intermountain West face challenges related to higher-than-expected no-show rates. Multiple interventions have been implemented throughout the system; however, a comprehensive evaluation of contributing patient demographics and system factors has not been completed. Methods: A needs assessment was conducted utilizing system and patient data from two primary care clinics, a SWOT analysis, and interviews with clinic staff and leaders. The SWOT analysis and EHR data findings were integrated into the intervention recommendations and presented to operational stakeholders. Interventions: System factors were evaluated through semi-structured interviews with clinical leaders, staff, and content experts and a review of policies and procedures. Central themes were identified and organized into a SWOT analysis. Additionally, retrospective electronic health record (EHR) data of patient demographics and visit characteristics from October 2023 to September 2024 were analyzed for trends and gaps. Results: EHR data from two primary care clinics showed that 39,899 patients scheduled 128,114 visits. Of all the visits, 8% no-showed, 61% completed, and 31% cancelled. New patient visits had the highest no-show rate at 13%. Visits not scheduled with the patient's provider had higher no-show rates (10%) than those scheduled with their provider (6%). No-show rates were highest for younger patients (18-34) and males-6% no-show rate for both groups. No-show rates varied based on insurance status, the highest being 18% for self-pay patients and 12% for those with Medicaid. The no-show rates for the Native Hawaiian/Pacific Islander and Black/African American ethnicities were 9% for each group. Social determinants of health screenings had been previously completed for 9% of patients, with food insecurity and lack of finances for utility and housing costs identified as the most frequent barriers. The SWOT analysis identified strengths in leadership and innovative strategies, weaknesses in consistently implementing mitigation strategies, opportunities for system enhancements, and threats from financial impacts and access challenges. The SWOT analysis and EHR data findings were integrated into the intervention recommendations and presented to operational stakeholders. Stakeholders deemed the recommended interventions feasible and usable, committing both support and advocacy for implementation. Conclusion: System factors emerged as the primary focus for the recommended interventions in this needs assessment. These included updating policy, capturing cancellation reasons, improving the consistency of social determinants of health screening, and utilizing care management to assist chronic no-show patients. Finally, it is recommended that the organization continue monitoring no-show rates, expand current strategies to more providers, regularly evaluate interventions' effectiveness, and explore innovative approaches to further reduce no-shows. |
