Decreasing the Rate of No-shows at a Student-led Clinic: An Evidence-Based Quality Improvement Project

No-show appointments present a significant challenge in primary care, particularly among underserved populations, leading to poorer health outcomes and increased healthcare costs. Research indicates that men, younger individuals, women, geriatric patients, those with mental health conditions, and lower socioeconomic groups have higher no-show rates. Missed appointments contribute an estimated $150 billion in annual healthcare costs, negatively impacting healthcare delivery and resource allocation. There are five student-led clinics in Salt Lake City, where medical and DNP students provide care under faculty supervision. These clinics offer hands-on experience across multiple specialties, equipping students with a broad understanding of medicine and the healthcare system. However, no-show rates ranged from 10-30%, affecting patient care and limiting learning opportunities for students working with underserved populations. To address the high no-show rates, an automated text notification system was implemented to remind patients of their appointments at seven, two, and one day before their appointments. Additionally, medical students conducted phone reminders one week before appointments, using a standardized script to confirm appointments, assess transportation needs, and provide information on insurance assistance. Students logged patient responses and notes into an Excel spreadsheet for consistent tracking. Patients needing assistance received transportation vouchers to mitigate access barriers. The intervention combined automated text reminders (seven, two, and one day before appointments) with student-led phone reminders one week prior. Calls followed a standardized script to confirm attendance, assess transportation needs, and provide insurance assistance. Students documented phone call responses in an Excel spreadsheet for tracking, and transportation vouchers were offered as needed. Students underwent structured training to ensure consistency, including a PowerPoint presentation on the call script, documentation, and patient engagement strategies. Additionally, Redcap surveys were used to evaluate patient engagement and the effectiveness of reminders, supporting ongoing improvements to the intervention. The total number of patients seen increased from 275 (baseline) to 483(post-intervention), with appointment completion rates improving slightly from 59.3% (n=163) to 60.2% (n=292). Confirmed cancellations decreased from 27.6% (n=76) to 21.7% (n=105). However, no-show rates increased from 12.4% (n=34) to 17.6% (n=85), indicating that more patients were scheduled and came to their appointments, yet there were also more no-shows. Males had higher overall appointment completion rates of 53.3% (n=155) than females at 46.7% (n=136). Female no-show rates also increased from 41.2% (n=14) to 48.2% (n=41), suggesting that the intervention was more effective for male patients. Appointment confirmation was strongly linked with appointment completion as confirmed patient no-show rates were lower, 2.9% (n=1) to 12.9% (n=11) than non-confirmed patients, 97.1% (n=33) to 87.1%(n=74). Overall, this DNP-QI project improved appointment adherence and reduced cancellations, yet decreasing the number of no-shows continues to be challenging. There were continual issues with language barriers and students not completing phone reminders during the holidays. Overall, the intervention was valuable, as patients who confirmed their appointments during telephone calls from the students were more likely to attend the scheduled appointment. However, sustainability remains a challenge due to student turnover and limited experience. To help ensure consistency, a sustainability plan should include faculty oversight and structured student training.

1 Decreasing the Rate of No-shows at a Student-led Clinic: An Evidence-Based Quality Improvement Project Vishant Thapa, Larry Garrett, Sonja Raum, Patrick Dieudonne College of Nursing: The University of Utah NURS 7703: DNP Scholarly Project III April 18, 2025 2 Abstract Background No-show appointments present a significant challenge in primary care, particularly among underserved populations, leading to poorer health outcomes and increased healthcare costs. Research indicates that men, younger individuals, women, geriatric patients, those with mental health conditions, and lower socioeconomic groups have higher no-show rates. Missed appointments contribute an estimated $150 billion in annual healthcare costs, negatively impacting healthcare delivery and resource allocation. Local Problem There are five student-led clinics in Salt Lake City, where medical and DNP students provide care under faculty supervision. These clinics offer hands-on experience across multiple specialties, equipping students with a broad understanding of medicine and the healthcare system. However, no-show rates ranged from 10-30%, affecting patient care and limiting learning opportunities for students working with underserved populations. Methods To address the high no-show rates, an automated text notification system was implemented to remind patients of their appointments at seven, two, and one day before their appointments. Additionally, medical students conducted phone reminders one week before appointments, using a standardized script to confirm appointments, assess transportation needs, and provide information on insurance assistance. Students logged patient responses and notes into an Excel spreadsheet for consistent tracking. Patients needing assistance received transportation vouchers to mitigate access barriers. Interventions 3 The intervention combined automated text reminders (seven, two, and one day before appointments) with student-led phone reminders one week prior. Calls followed a standardized script to confirm attendance, assess transportation needs, and provide insurance assistance. Students documented phone call responses in an Excel spreadsheet for tracking, and transportation vouchers were offered as needed. Students underwent structured training to ensure consistency, including a PowerPoint presentation on the call script, documentation, and patient engagement strategies. Additionally, Redcap surveys were used to evaluate patient engagement and the effectiveness of reminders, supporting ongoing improvements to the intervention. Results The total number of patients seen increased from 275 (baseline) to 483(post-intervention), with appointment completion rates improving slightly from 59.3% (n=163) to 60.2% (n=292). Confirmed cancellations decreased from 27.6% (n=76) to 21.7% (n=105). However, no-show rates increased from 12.4% (n=34) to 17.6% (n=85), indicating that more patients were scheduled and came to their appointments, yet there were also more no-shows. Males had higher overall appointment completion rates of 53.3% (n=155) than females at 46.7% (n=136). Female no-show rates also increased from 41.2% (n=14) to 48.2% (n=41), suggesting that the intervention was more effective for male patients. Appointment confirmation was strongly linked with appointment completion as confirmed patient no-show rates were lower, 2.9% (n=1) to 12.9% (n=11) than non-confirmed patients, 97.1% (n=33) to 87.1%(n=74). Conclusion Overall, this DNP-QI project improved appointment adherence and reduced cancellations, yet decreasing the number of no-shows continues to be challenging. There were continual issues with language barriers and students not completing phone reminders during the holidays. 4 Overall, the intervention was valuable, as patients who confirmed their appointments during telephone calls from the students were more likely to attend the scheduled appointment. However, sustainability remains a challenge due to student turnover and limited experience. To help ensure consistency, a sustainability plan should include faculty oversight and structured student training. Keywords: no-show appointments, student-led clinic, quality improvement, appointment reminders, underserved populations, healthcare access, patient engagement. 5 Decreasing the rate of no-shows at a Primary Care Clinic: An EvidenceBased Quality Improvement Project Problem Description A "no-show appointment" refers to a missed medical appointment where a patient fails to attend their scheduled appointment. These occurrences disrupt clinic scheduling, waste resources, and contribute to longer wait times for other patients. Ultimately, no-shows can lead to poorer health outcomes for the patient and increased healthcare costs (Kaplan-Lewis & Percac-Lima, 2013). Several factors contribute to no-shows, including demographic characteristics, logistical challenges, and individual patient circumstances (Lee et al., 2005). Specific factors linked to missed appointments include age, gender, transportation issues, and clinic-related elements such as booking efficiency and the quality of patient-staff interactions (Ellis et al., 2017). Understanding the complex interplay of these factors is essential for developing targeted interventions to reduce no-show rates and improve overall healthcare delivery (Ellis et al., 2017). Missed appointments have significant financial implications for healthcare systems, and addressing their prevalence and health impact is critical to creating effective strategies to enhance patient engagement and reduce health disparities (Ellis et al., 2017). Frequently missed appointments indicate deeper patient engagement issues, particularly in preventive care. This can lead to substantial public health challenges, highlighting the urgent need for focused interventions to improve healthcare access and equity (Ellis et al., 2017). This Doctor of Nurse Practice quality improvement (DNP-QI) project was conducted at a student-led clinic in Salt Lake City, Utah. The clinic operations are managed by the School of Medicine (SOM) and Doctor of Nurse Practice (DNP) students under the guidance of preceptors. 6 Currently, this clinic has a no-show rate of 10-30%, negatively affecting patient health outcomes and the student's ability to gain knowledge from valuable hands-on learning experiences. Each missed appointment is a lost opportunity to practice clinical skills and interact with a diverse patient population. This student-led clinic emphasizes hands-on learning under the supervision of licensed providers while serving underserved populations. Students actively engage in scheduling, patient education, and clinical decision-making, which uniquely positions them to observe and address issues related to missed appointments. The collaborative environment at the clinic provides an opportunity to implement innovative solutions to reduce no-show rates, foster patient engagement, and enhance healthcare delivery for vulnerable populations. Available Knowledge No-shows present a significant challenge in primary care, especially for underserved populations, potentially leading to poorer health outcomes. Men, younger individuals, women, geriatric patients, patients with mental health conditions, and patients from lower socioeconomic backgrounds tend to have higher no-show rates. A study at a rural free clinic in the Southeastern U.S. revealed specific reasons for no-shows, such as difficulty accessing transportation, seeing multiple doctors, long waiting times, adverse weather conditions, and fear of medical settings (Marbouh et al., 2020). Overall, missed appointments contribute to an annual cost of approximately 150 billion dollars (Shour et al., 2023). This issue significantly affects healthcare delivery, fees, and resource planning, underscoring the need to address the factors leading to patient no-shows (Shour et al., 2023). Accessing healthcare in urban areas presents distinct challenges, often characterized by high patient volumes, socioeconomic disparities, and barriers to effective patient engagement. While infrastructure in urban settings is generally well-developed, other issues—such as 7 transportation affordability, cultural and linguistic barriers, and fragmented care coordination— can impede access and delivery of healthcare services (Shour et al., 2023). Addressing these urban-specific issues requires targeted strategies that prioritize equity, streamline care processes, and enhance patient engagement. Research on patients who frequently miss appointments indicates the presence of a core group that consistently fails to attend. Patients who miss at least one appointment a year are more likely to miss subsequent ones (Ellis et al., 2017). In England, a National Health Service (NHS) study found that one in 50 patients (65,590 out of 3.5 million) who missed one appointment went on to miss three or more within three months (Ellis et al., 2017). These statistics suggest significant implications for patients, healthcare providers, and service managers. A 2017 focus group analysis of general practitioners highlighted that clinicians distinguish between patients who miss a few appointments and those with multiple no-shows. Those with repeated absences often have more complex social and health needs, indicating that targeted interventions may be necessary to address the unique challenges faced by this population (Ellis et al., 2017) Research on multiple automated text reminder notifications in healthcare shows promising results for enhancing appointment attendance and overall medical compliance. A growing body of literature indicates that short message service (SMS) reminders are an inexpensive, easily implemented, and automatable solution that can significantly improve attendance rates (Schwebel & Larimer, 2018). These reminders help patients remember their appointments and enhance prospective memory, allowing individuals to recall and engage in behaviors they wish to change (Schwebel & Larimer, 2018). Overall, incorporating SMS reminders may be a valuable strategy for healthcare providers to boost attendance and support patient engagement in their health management. 8 Research by Vang et al. (2020) demonstrated the importance of staff reminder calls in a safety-net clinic to decrease the no-show rate. In this clinic, staff reminder calls resulted in a 2.9% absolute decrease in no-shows (Vang et al., 2020). Patients who confirmed their plans to attend via phone demonstrated higher attendance rates than those who received voicemails or were not reached. Live reminder calls foster stronger connections between patients and healthcare teams, enhancing continuity and quality of care (Vang et al., 2020). With a 2.9% decrease in no-show rates to 60,000 visits annually in the Safety Net Medicine Clinic, the clinic could accommodate an additional 1,740 visits yearly. Based on a conservative estimate of $50 earned per visit, the return on investment for the caller's salary of $30,000 plus benefits would exceed 2:1. This highlights the financial viability of implementing staff reminder calls to improve appointment attendance specific to the clinic's operations (Vang et al., 2020). Rationale The Johns Hopkins Evidence-Based Practice (EBP) Model was selected as the framework for this DNP-QI project due to its systematic and practical approach to integrating evidence-based interventions into clinical practice (Dearholt & Dang, 2017). This model's threestep process—practice question, evidence, and translation—provided a structured pathway to address high no-show rates at the student-led clinic associated with this quality improvement project. The practice question phase guided the identification of the specific issue: high no-show rates and their impact on clinic efficiency. In the evidence phase, a thorough literature review was conducted to determine effective interventions, including text reminders and patient phone calls. Finally, in the translation phase, these evidence-based strategies were adapted and 9 implemented within the clinic’s unique context to reduce no-show rates and improve patient engagement. Specific Aims The purpose of this DNP-QI evidence-based practice project was to decrease the rate of no-shows at a student-led clinic by implementing an evidence-based practice that included automated text reminder notifications sent at least seven, two, and one day before patients' appointments, as well as consistent phone calls from medical and DNP students to remind patients of their appointments. The medical students assessed the newly implemented evidencebased interventions for feasibility, usability, and satisfaction. Methods Context This Doctor of Nursing Practice (DNP) Quality Improvement (QI) project was conducted at a student-led clinic from mid-October to mid-December 2024. Situated in the urban area of Salt Lake City, the clinic serves a diverse patient population, primarily composed of recent immigrants, low-income families, and individuals who were uninsured or enrolled in Medicare or Medicaid. All patients seen at the clinic are adults aged 21 and older. The clinic operates on weekdays from 9:00 AM to 4:00 PM, with an average of eight daily patient visits. Each appointment typically lasts 45 to 60 minutes. Patient care is provided by a combination of healthcare students, including medical and nursing students in their early years of training, under the supervision of licensed faculty healthcare providers. On any given day, the clinic is staffed by a team of medical and nurse practitioner students working collaboratively with supervising physicians and nurse practitioners. 10 Intervention(s) Phase I: Identification of Barriers and Planning During the project's initial phase, collaboration with the clinic’s administrative staff, physicians, and nurse practitioners focused on identifying the key barriers contributing to high no-show rates. Despite existing efforts to reduce these rates through patient reminder calls, previous initiatives have been unsuccessful. A focus group with clinic staff uncovered recurring challenges faced by patients, including transportation difficulties, forgetfulness, and inconsistencies in the students completing the reminder calls. Discussions also revealed that issues such as insurance complexities and patient demographics further compounded these challenges. Understanding these barriers helped target strategies to enhance patient attendance and engagement. For this DNP project, a pre-survey questionnaire was not conducted because all medical students involved were freshmen, and DNP students were still being integrated into the clinical setting when the project began. The integration of DNP students into this student-led clinic had never been done before, making it impractical to obtain meaningful pre-survey data during this initial implementation phase. Phase II: Development of Strategies In this phase, three automated text reminder notifications were implemented for patients seven, two, and one day before their appointments. This approach ensured that timely reminder notifications were provided to patients. While the clinic had an existing SMS reminder system, this QI project evaluated its effectiveness by analyzing whether increasing the number of reminders influenced patient attendance. The project also integrated student-led phone call reminders to complement SMS notifications, ensuring patients received automated and personal 11 outreach. To increase the accuracy of the reminder system, healthcare staff verified patients' current addresses and contact information upon check-in at the front desk, helping to ensure that all calls and text messages were delivered correctly. For first-time appointments, the necessary contact information was collected during patient registration, including phone numbers and email addresses, and entered into the scheduling system. This process ensured that new patients also received timely appointment reminders. To enhance patient outreach efforts, it became evident that a structured system was needed to address gaps in the current process, including inconsistencies in reminder calls and a lack of accountability among students. After discussions with clinic staff and key stakeholders, the decision was made to build an Excel spreadsheet (Appendix A) to streamline the tracking of patient outreach. This spreadsheet served multiple purposes: monitoring which patients had been contacted, evaluating the effectiveness of outreach efforts, and ensuring that all students completed their assigned calls. Students were assigned to make reminder calls based on a structured rotational schedule. This schedule evenly distributes the responsibility, promotes accountability, and reduces the reliance on a self-regulated or honor system. Assignments were communicated, and the spreadsheet allowed clinic staff to monitor compliance and provide feedback when necessary. A scripted protocol (Appendix B) was developed to standardize the communication process during patient calls. This protocol included guidelines for reminding patients of their appointments, confirming access to reliable transportation, and verifying insurance status. If a patient lacked health insurance, the student would notify a Community Health Worker (CHW) to assist in identifying suitable health insurance options tailored to the patient’s income and family size. A PowerPoint training session (Appendix B) was created and delivered to ensure all 12 students were adequately prepared for this task. The training covered using the scripted protocol, navigating the spreadsheet, and the importance of effective and empathetic communication during patient calls. This training ensured that students had the necessary skills and knowledge to perform their responsibilities effectively and confidently. An audio PowerPoint presentation (Appendix B) was also created to educate medical students about the impact of no-shows. This presentation highlighted how missed appointments negatively affect patient health outcomes, hinder students’ hands-on clinical learning, and financially burden the healthcare system and clinic. Phase III: Implementation of Interventions In this phase, the new evidence-based tools were implemented 8 weeks from midOctober to mid-December 2024. The implementation consisted of two key components: Automated Text Reminders: Patients received automated text notifications seven, two, and one day before their upcoming appointments. The automated text messages were designed to be clear, concise, and culturally appropriate for the patient population. They were also sent in the patient’s primary language as indicated in their electronic medical record (EMR). This ensured the text content included essential appointment details and a request for patients to confirm their attendance. Personalized Phone Calls: Medical students called patients one week in advance to remind them of their appointments. During these calls, they used a prepared script (Appendix B) to assess the patients’ transportation needs and insurance status while providing reminders about their upcoming appointments. Students assigned to make these calls recorded detailed notes and comments in an Excel spreadsheet, maintaining a consistent record of patient interactions and enabling effective follow-up. Transportation vouchers were also offered to patients if needed. 13 Phase IV: Monitor Interventions The Excel sheet used to track students' names and their assigned duties for calling patients was reviewed two to three times per week. When the call logs in the Excel sheet (Appendix A) were found to be incomplete, the Medical Assistant (MA) supervising the students was contacted promptly to address the issue. This proactive approach ensured consistency and accountability in patient appointment reminder calls. Plan-Do-Study-Act (PDSA) cycles were used throughout the implementation of this QI project to guide iterative refinements and ensure consistency. In the planning phase, scripted protocols, Excel tracking systems, and student training materials were developed. During the implementation "Do" phase, student phone call activity and no-show rates were monitored weekly. When incomplete call logs were identified in the Excel spreadsheet, the supervising Medical Assistant (MA) was notified, and students were reminded of their responsibilities. This "Study" phase revealed gaps in follow-through during holidays and highlighted language barriers. In response "Act" phase, reinforcement of training, clarification of student roles, and real-time feedback loops were established to improve accountability. These ongoing PDSA cycles supported timely adjustments and ensured the intervention remained responsive to challenges throughout the eight-week period. Study of the Intervention(s) The primary approach for evaluating the intervention in this DNP-QI project involved comparing pre- and post-intervention no-show rates using EHR chart audits. This data-driven approach allowed for the quantitative measurement of changes in no-show rates after implementing automated text reminders and pre-appointment personalized phone calls. Data on no-show rates were collected from mid-October to mid-December 2023 to establish a baseline 14 for comparison. After implementing the intervention for 8 weeks from mid-October to midDecember 2024, the no-show rates were assessed and compared to the baseline period. Data was monitored weekly during the intervention phase to allow for rapid-cycle improvements. This frequent monitoring enabled adjustments to the intervention, such as changes to message content or timing of reminder notifications, based on emerging trends or feedback from patients and clinic staff. Additionally, a Redcap survey (Appendix C) was administered post-implementation to evaluate the effectiveness of the personalized phone call system. This survey collected quantitative and qualitative data, focusing on the feasibility, usability, and acceptability of the intervention. Feedback was gathered from students and clinic staff who participated in making the calls. Their insights helped assess the intervention's practicality within the clinic workflow and identify areas for improvement. The project also tracked the response rate to reminder notifications (automated texts) to evaluate patient engagement. The percentage of patients who acknowledged reminders and attended their appointments was compared to those who did not respond. Measures EHR Chart Audits Retrospective chart audits were conducted for three months to establish a baseline noshow rate. In addition to missed appointments, patient demographics—such as language barrier (English vs. non-English), age, and chronic conditions—were collected to describe the variables and high no-show rates. After implementing the automated text reminder system and personalized phone call interventions, no-show data was monitored and recorded weekly over eight weeks from mid-October to mid-December 2024. 15 Patient Engagement Tracking Automated Text Notifications and Personalized Calls: The Excel spreadsheet (Appendix A) tracked patient responses to personalized phone calls, and data from the EHR was used to confirm the patient’s response to automated text reminders. The Excel spreadsheet captured whether patients confirmed, canceled, or did not respond to the personalized phone call. The data also tracked how many patients responded to automated text messages. The collected data were analyzed to calculate the percentage of patients who acknowledged the reminders and to assess whether acknowledgment affected attendance. Redcap survey The survey (Appendix C) included a combination of question types to evaluate the intervention's outcomes and integration into the clinic’s operational framework. Likert scale questions assessed the ease of use, perceived effectiveness, and overall satisfaction with the personalized phone call system. This was determined using Yes/No questions about the feasibility of the system and its alignment with staff workflows and capacities. Additionally, open-ended questions collected qualitative feedback, allowing participants to provide suggestions for improvement and reflections on the intervention’s impact. This multifaceted approach ensured a robust evaluation by capturing quantitative and qualitative insights, guiding further intervention refinement. Analysis This DNP QI project utilized a mixed-methods approach, combining qualitative and quantitative techniques to evaluate interventions. Following the post-implementation phase, 16 qualitative data from the group discussions were grouped by theme to explore trends, challenges, and potential solutions to mitigate no-shows. The quantitative analysis involved calculating weekly no-show rates and assessing trends using the mean and standard deviation over an eight-week post-implementation period. These rates were then compared to baseline no-show rates recorded during an eight-week preimplementation period. The analysis examined the percentage change in no-show rates from preto post-implementation. Specifically, it evaluated whether the no-show rates decreased after implementing three automated text reminders and one student-initiated phone call per appointment. The chi-square test of independence was used to compare categorical variables (noshow, completed, canceled) between the pre-and post-implementation periods and confirmation of automated text reminders to determine if the intervention had an effect. A cost-benefit analysis (Table 1) was conducted to compare net revenue between preintervention (N = 275) and post-intervention (N = 483) periods, evaluating overall savings from reduced no-shows against the costs associated with implementing the interventions, including automated text reminders and student-led phone calls. Ethical Considerations The University of Utah's Institutional Review Board (IRB) reviewed this DNP QI project and determined it was exempt from human subject research, as it qualified as a quality improvement initiative. Informed consent was not required since the project's primary objective was to enhance appointment attendance and reduce no-show rates. The project adhered to core ethical principles, prioritizing minimizing harm and promoting beneficence by improving access to care without introducing additional risks. It upheld equity and fairness by addressing the needs of marginalized populations, such as recent 17 immigrants, through culturally tailored interventions. Appointment reminders were sent in patients' native languages, and resources for transportation and insurance assistance were provided to mitigate common barriers to care. No conflicts of interest were identified, as the initiative was dedicated to improving patient outcomes and ensuring equitable access to healthcare, particularly for vulnerable populations. Results Before proceeding, it is important to note a significant difference in the denominators between the baseline and intervention timeframes. Consequently, comparisons between the two timeframes can be challenging to interpret. The total number of patient visits increased from the baseline (n=275) to the intervention (n=483) timeframes as the clinic became busier over time. Patient Appointment Adherence and No-Show Rates A chi-square test was conducted to evaluate differences in appointment adherence between pre- and post-intervention periods. Results revealed observable changes; completed appointments increased slightly from 59.3% (n=163) pre-intervention to 60.2% (n=291) postintervention, cancellations decreased from 27.6% (n=76) to 21.7% (n=105), and no-show rates increased from 12.4% (n=34) to 17.6% (n=85). However, these changes were not statistically significant (χ² = 5.6417, p = 0.0596) (Table 2). Appointment status by gender Similar to the previous section, none of the results in this section were significant, but notable patterns emerged. A statistically nonsignificant association existed between gender and appointment adherence pre- and post-intervention. For male patients, completed appointments slightly decreased from 56.4% (n=92) in 2023 to 53.3% (n=155) in 2024, cancellations decreased from 52.6% (n=40) to 42.9% (n=45), and no-show rates slightly decreased from 18 58.8% (n=20) to 51.8% (n=44); these changes were not statistically significant (χ² = 4.2168, p = 0.121). Female patients showed a modest increase in completed appointments from 43.6% (n=71) to 46.7% (n=136), yet also experienced increased cancellations from 47.4% (n=36) to 57.1% (n=60) and higher no-show rates from 41.2% (n=14) to 48.2% (n=41); these variations were also nonsignificant (χ² = 2.322, p = 0.313) (Table 3). Appointment status by age Comparing pre- and post-intervention appointment adherence across various age groups revealed no statistically significant differences. While some positive trends were noted, such as improved completion rates in patients younger than 18 years (χ² = 2.90, p = .24), 18-29 years (χ² = 4.01, p = .13), and 40-49 years (χ² = 1.49, p= .47), these improvements did not reach statistical significance. Additionally, minimal changes were observed in appointment adherence among older age groups (50-59, 60-69, 70-79, and >80 years), with none demonstrating statistically significant differences pre- to post-intervention (Table 4). Appointment status by confirmation A statistically significant association was found between confirmation status (confirmed vs. non-confirmed) and appointment adherence pre- and post-intervention. Before the intervention, confirmed appointments showed significantly higher completion rates (80.6%, n=29) compared to non-confirmed appointments (56.5%, n=134) and lower cancellations (16.7%, n=6 vs. 29.5%, n=70) and no-show rates (2.8%, n=1 vs. 13.9%, n=33) (χ² = 7.996, p=.018). This relationship notably strengthened post-intervention (χ² = 41.748, p < .001), with confirmed appointments maintaining higher completion rates (81.3%, n=126 vs. 50.6%, n=165), lower cancellations (11.6%, n=18 vs. 26.7%, n=87), and significantly lower no-show rates (7.1%, n=11 vs. 22.7%, n=74) (Table 5). 19 Redcap Post-Questionnaire Responses A post-intervention survey (Appendix C) was sent to 40 participating students, achieving a 35% response rate (n=14). The survey aimed to assess the feasibility, usability, and satisfaction of the phone call reminder system implemented to reduce no-show rates. Among the respondents, 71.4% (n=10) agreed or strongly agreed that the phone call reminders helped decrease the no-show rate, and 78.5% (n=11) reported being likely or very likely to recommend the continuation of phone call reminders for future patient appointments. Additionally, 64.2% (n=9) found it easy or very easy to reach patients for reminders. In comparison, 35.7% (n=5) reported a neutral or challenging experience, primarily due to incorrect phone numbers, language barriers, and patients' refusal to confirm appointments. Half of the respondents found it easy or very easy to access and navigate the Excel tracking spreadsheet. Moreover, 57.1% (n=8) expressed satisfaction with the phone call reminder system. Meanwhile, 28.5% (n=4) were neutral, highlighting a mix of experiences—some found it effective, while others faced challenges. Discussion Summary This DNP-QI project aimed to reduce no-show rates at a student-led clinic by implementing targeted interventions, including phone call reminders and automated text notifications. While there was an overall increase in patient visits, the intervention resulted in only a slight improvement in completed appointments, with no significant impact on reducing cancellations or no-show rates. However, the project demonstrated key strengths, such as increased patient engagement and confirmation, which were associated with a lower likelihood of missed appointments. Additionally, fewer cancellations contributed to improved scheduling 20 efficiency. Despite these gains, challenges such as language barriers, seasonal variations, and inconsistencies in outreach efforts may have influenced the overall effectiveness of the intervention. These findings highlight the need for tailored strategies to enhance patient adherence and optimize clinic operations. PDSA cycles were critical in identifying workflow issues and real-time adjusting processes. These cycles enabled ongoing evaluation and responsiveness to challenges such as inconsistent outreach and student accountability, ultimately enhancing the reliability of the intervention. Interpretation The results of this DNP-QI project were partially expected and generally aligned with findings in the existing literature. The observed increase in patient visits and modest improvement in appointment completion rates support prior studies that indicate reminder systems—particularly those using SMS and phone calls—can positively influence attendance (Hasvold & Wootton, 2011; Schwebel & Larimer, 2018). However, the unexpected rise in noshow rates highlights persistent challenges beyond the reach of reminders alone. These include transportation issues, scheduling conflicts, and broader social determinants of health (SDoH), consistent with prior research identifying barriers as critical contributors to missed appointments (Kaplan-Lewis & Percac-Lima, 2013; Ellis et al., 2017). The increase in female no-show rates post-intervention further underscores the importance of gender-sensitive approaches, as literature shows that caregiving duties and work constraints disproportionately affect women’s ability to attend healthcare appointments (Parsons et al., 2021). Additionally, the strong correlation between appointment confirmation and attendance mirrors findings from Junod Perron et al. (2013) and Ulloa-Pérez et al. (2022), confirming that patient engagement through confirmation significantly improves follow-through. 21 This project’s impact extended beyond individual patients to the clinic system. Patients benefited from enhanced communication and transportation support, while medical and nursing students gained valuable hands-on training in patient engagement and outreach. Importantly, the project also sheds light on health equity issues. Many patients were immigrants with limited English proficiency, and language barriers limited the effectiveness of SMS and phone reminders. This finding supports McLean et al. (2016), who emphasized the need for culturally and linguistically tailored interventions to improve access and adherence in underserved populations. The cost-benefit analysis (Table 1) further demonstrated that improving patient attendance enhances care delivery and supports financial sustainability for the clinic when the scheduled patient visits increased from 275 (pre-intervention) to 483 (post-intervention). Though the intervention required significant student time and effort, it increased clinic revenue and demonstrated the long-term value of investing in systems supporting patient follow-through. These findings emphasize the importance of addressing structural and communication barriers to reduce no-show rates and promote equitable healthcare access. Limitations While this project successfully implemented strategies to reduce no-show rates at a student-led clinic run by first-year medical students, several limitations should be considered when interpreting the findings and their generalizability. The study was conducted in a studentled clinic, where first-year medical students managed patient outreach and scheduling. Their experience level and ability to effectively engage with patients may have influenced appointment adherence differently than in clinics staffed by experienced providers. The study occurred between October and December, during major holidays like Thanksgiving and Christmas. Holiday-related clinic closures and reduced student availability led to inconsistent call reminders 22 and follow-ups. Another limitation was language barriers, as many patients were recent immigrants, and English was not their primary language. While automated text reminders were sent in the patient’s preferred language, student callers primarily spoke English, leading to communication challenges that may have impacted confirmation rates and adherence. Patients with limited English proficiency may have struggled with appointment details and confirmation procedures, increasing the likelihood of missed appointments. Another factor that may have influenced adherence was appointment length. Visiting for more than an hour could cause conflict with family and work schedules, making it difficult for patients to attend or avoid lastminute cancellations. Conclusions This DNP-QI project focused on reducing no-show rates at a student-led clinic run by first-year medical students through targeted interventions, including appointment reminders by phone and automated text reminder notifications. The findings demonstrated modest improvements in completed appointments and reductions in cancellations. However, challenges such as language barriers and inconsistent reminder calls due to holidays. Participants generally found the intervention worthwhile, as confirmed patients had significantly lower no-show rates. However, sustainability remains challenging, given the reliance on students with limited experience and frequent turnover. To ensure continuity, a sustainability plan should involve designating a faculty or clinic staff member as a champion to oversee the intervention and establish a structured training program for incoming students. Future projects should explore implementing an automated voice messaging system that delivers appointment reminders in the patient's primary language, improving accessibility for non-English-speaking patients. Additionally, offering weekend clinic hours could accommodate patients who cannot take time 23 off during weekdays. Lastly, shorter clinic visits (30 minutes instead of an hour) may better suit patients with demanding work schedules, enhancing appointment adherence and overall clinic efficiency. 24 Acknowledgments This project was conducted without external funding. First and foremost, I would like to express my deepest gratitude to Dr. Sonya Raaum, Medical Director of the student-led clinic, for her unwavering support, guidance, and expertise throughout this project. Her leadership and mentorship were invaluable in shaping the success of this initiative. I would also like to sincerely thank Dr. Diane Chapman and Dr. Gwen Latendresse, whose encouragement and support allowed me to undertake this meaningful work. Additionally, I am incredibly grateful to my Project Chair, Dr. Larry Garrett, for his insightful feedback, continuous guidance, and assistance refining my manuscript. A special thank you to the clinic staff, medical and nursing students, and Patrick Dieudonne, whose dedication and collaboration were instrumental in the clinic's daily operations and in facilitating this project’s implementation. Finally, none of this would have been possible without my family's unwavering love, support, and encouragement, who inspired me every day and stood by me through every challenge and success. 25 References Chung, S., Martinez, M. C., Frosch, D. L., Jones, V. G., & Chan, A. S. (2020). Patient-centric scheduling with the implementation of health information technology to improve the patient experience and access to care: Retrospective case-control analysis. Journal of Medical Internet Research, 22(6), e16451. https://doi.org/10.2196/16451 Dang, D., & Dearholt, S. (Eds.). (2018). Johns Hopkins nursing evidence-based practice: Model and guidelines (Third edition). Sigma Theta Tau International Ellis, D. A., McQueenie, R., McConnachie, A., Wilson, P., & Williamson, A. E. (2017). Demographic and practice factors predicting repeated non-attendance in primary care: A national retrospective cohort analysis. The Lancet. Public Health, 2(12), e551–e559. https://doi.org/10.1016/S2468-2667(17)30217-7 Hasvold, P. E., & Wootton, R. (2011). Use of telephone and SMS reminders to improve attendance at hospital appointments: A systematic review. Journal of Telemedicine and Telecare, 17(7), 358–364. https://doi.org/10.1258/jtt.2011.110707 Junod Perron, N., Dao, M. D., Righini, N. C., Humair, J.-P., Broers, B., Narring, F., Haller, D. M., & Gaspoz, J.-M. (2013). Text-messaging versus telephone reminders to reduce missed appointments in an academic primary care clinic: A randomized controlled trial. BMC Health Services Research, 13, 125. https://doi.org/10.1186/1472-6963-13-125 Kaplan-Lewis, E., & Percac-Lima, S. (2013). No-show to primary care appointments: Why patients do not come. Journal of Primary Care & Community Health, 4(4), 251–255. https://doi.org/10.1177/2150131913498513 26 Kenniff, J., & Ginat, D. (2023). Evaluation of an automated reminder system for reducing missed MRI appointments. Journal of Patient Experience, 10, 23743735231151548. https://doi.org/10.1177/23743735231151548 Lee, V. J., Earnest, A., Chen, M. I., & Krishnan, B. (2005). Predictors of failed attendances in a multi-specialty outpatient center using electronic databases. BMC Health Services Research, 5, 51. https://doi.org/10.1186/1472-6963-5-51 Marbouh, D., Khaleel, I., Al Shanqiti, K., Al Tamimi, M., Simsekler, M. C. E., Ellahham, S., Alibazoglu, D., & Alibazoglu, H. (2020). Evaluating the impact of patient no-shows on service quality. Risk Management and Healthcare Policy, 13, 509–517. https://doi.org/10.2147/RMHP.S232114 McLean, S. M., Booth, A., Gee, M., Salway, S., Cobb, M., Bhanbhro, S., & Nancarrow, S. A. (2016a). Appointment reminder systems are effective but not optimal: Results of a systematic review and evidence synthesis employing realist principles. Patient Preference and Adherence, 10, 479–499. https://doi.org/10.2147/PPA.S93046 Parsons, J., Bryce, C., & Atherton, H. (2021). Which patients miss appointments with general practice and the reasons why: A systematic review. The British Journal of General Practice, 71(707), e406–e412. https://doi.org/10.3399/BJGP.2020.1017 Schwebel, F. J., & Larimer, M. E. (2018). Using text message reminders in health care services: A narrative literature review. Internet Interventions, 13, 82–104. https://doi.org/10.1016/j.invent.2018.06.002 Shour, A., & Onitilo, A. A. (2023). Distance Matters: Investigating no-shows in a large rural provider network. Clinical Medicine & Research, 21(4), 177–191. https://doi.org/10.3121/cmr.2023.1853 27 Shour, A. R., Jones, G. L., Anguzu, R., Doi, S. A., & Onitilo, A. A. (2023). Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system. BMC Health Services Research, 23, 989. https://doi.org/10.1186/s12913-023-09969-5 Steiner, J. F., Zeng, C., Comer, A. C., Barrow, J. C., Langer, J. N., Steffen, D. A., & Steiner, C. A. (2021). Factors associated with opting out of automated text and telephone messages among adult members of an integrated health care system. JAMA Network Open, 4(3), e213479. https://doi.org/10.1001/jamanetworkopen.2021.3479 Ulloa-Pérez, E., Blasi, P. R., Westbrook, E. O., Lozano, P., Coleman, K. F., & Coley, R. Y. (2022). Pragmatic randomized study of targeted text message reminders to reduce missed clinic visits. The Permanente Journal, 26(1), 64–72. https://doi.org/10.7812/TPP/21.078 Vang, M., Linzer, M., Freese, R., Vickery, K., Shippee, N. D., & Coffey, E. (2020). Facilitating visit attendance with staff reminder calls in a safety-net clinic. Journal of General Internal Medicine, 35(4), 1317–1319. https://doi.org/10.1007/s11606-020-05655-y 28 Tables and Figures Table 1 Cost-Benefit Analysis Comparing Net Revenue Pre- and Post-Intervention (October 15– December 15, 2023, vs. 2024 (N = 275 vs. N = 483). Factor Pre-Intervention Post-Intervention Total Patients 275 483 Cost of 3 automated text $.15X3=$0.45 $.15X3=$0.45 Total cost of automated text $123.75 $217.35 Total cost of students calling $0.00 $0.00 Average revenue per visit $150.00 $150.00 Gross revenue $150.00X275=$41,250.00 $150.00X483=$72,450.00 Net Revenue $41, 250.00- $72,450.00-$217.35= $123.75=$41,126.25 $72,232.65 patients Difference in Net Revenue $72,232.65$41,126.25=$31,106.4 Note. N=275 patients were scheduled to be seen at the student-led clinic from 15th October to 15th December 2023 (pre-intervention), and N=483 patients were scheduled to be seen from 15th October to 15th December 2024 (post-intervention). 29 Table 2 Appointment status Pre- and Post-Intervention (October -December, 2023-2024) Completed Cancelled No-show Chi-square n n n (χ²) PreIntervention 163 76 34 5.6417 PostIntervention 291 105 85 p-value 0.05956 Note. N=275 patients were scheduled to be seen at the student-led clinic from 15th October to 15th December 2023 (pre-intervention), and N=483 patients were scheduled to be seen from 15th October to 15th December 2024 (post-intervention). 30 Table 3 Appointment Status by Gender, Pre- and Post-Intervention (October 15–December 15, 2023, and 2024) Male Completed Canceled No-show n n n PreIntervention 92 40 20 PostIntervention 155 45 44 Female Completed Canceled No-show n n n PreIntervention 71 36 14 PostIntervention 136 60 44 Chi-square (χ²) p-value 4.2168 0.1214 Chi-square (χ²) p-value 2.3224 0.3131 Note. N=275 patients were scheduled to be seen at the student-led clinic from 15th October to 15th December 2023 (pre-intervention), and N=483 patients were scheduled to be seen from 15th October to 15th December 2024 (post-intervention). 31 Table 4 Appointment Status by various age groups, Pre- and Post-Intervention (October 15–December 15, 2023, and 2024) Less than 18 years old Completed Canceled No-show n n n Chi-square (χ²) p-value Fisher test p-value PreIntervention 11 5 5 2.8958 PostIntervention 24 6 3 18-29 years old Completed Canceled No-show n n n PreIntervention 25 9 2 PostIntervention 49 19 17 30-39 years old Completed Canceled No-show n n n PreIntervention 35 18 6 PostIntervention 48 19 18 0.2351 0.2351 Chi-square (χ²) p-value 4.0123 0.1345 Chi-square (χ²) p-value 3.4822 0.1753 32 40-49 years old Completed Canceled No-show n n n PreIntervention 23 9 2 PostIntervention 48 14 9 50-59 years old Completed Canceled No-show n n n PreIntervention 17 6 6 PostIntervention 35 16 15 60-69 years old Completed Canceled No-show n n n PreIntervention 30 19 10 PostIntervention 47 20 18 Chi-square (χ²) p-value Fisher’s test pvalue 1.4914 0.4744 0.4966 Chi-square (χ²) p-value 0.26269 0.8769 Chi-square (χ²) p-value 1.4163 0.4925 33 70-79 years old Completed Canceled No-show n n n PreIntervention 17 7 3 PostIntervention 33 10 4 Greater than 80 years old Completed Canceled No-show n n n PreIntervention 5 3 0 PostIntervention 7 1 1 Chi-square (χ²) p-value Fisher’s test pvalue 0.41735 0.8117 0.8034 Chi-square (χ²) p-value Fisher’s test pvalue 2.2824 0.3194 0.4244 Note. N=275 patients were scheduled to be seen at the student-led clinic from 15th October to 15th December 2023 (pre-intervention), and N=483 patients were scheduled to be seen from 15th October to 15th December 2024 (post-intervention). 34 Table 5Appointment Status by Confirmation, Pre- and Post-Intervention (October 15–December 15, 2023, and 2024) PreIntervention Completed Cancelled No-show Chi-square p-value n n n (χ²) Confirmed 29 6 1 7.9958 0.01835 Not confirmed 134 70 33 PostIntervention Completed Cancelled No-show Chi-square p-value n n n (χ²) Confirmed 126 18 11 41.748 Not confirmed 165 87 74 8.6e-10 Note. N=275 patients were scheduled to be seen at the student-led clinic from 15th October to 15th December 2023 (pre-intervention), and N=483 patients were scheduled to be seen from 15th October to 15th December 2024 (post-intervention). 35 Figure 1 Total Number and Percentage of Patients Seen by Appointment Status During an 8-Week Period (October 15–December 15, 2023) Number of patients by appointment status (PreIntervention) 300 275 250 200 163 150 76 100 34 50 59.30% 0 Total number of patients seen 27.60% Completed 12.40% Cancelled No-show Total Number and Percentage of Patients Seen by Appointment Status During an 8-Week Period (October 15–December 15, 2024) Number of patients by appointment status(PostIntervention) 600 500 483 400 291 300 200 105 100 60.20% 0 Total patients seen Completed 21.70% Cancelled 85 17.60% No-show Note. Completion rates increased slightly, cancellations decreased, and no-show rates increased when comparing the post-intervention period to the pre-intervention 8-week period. However, these changes were not statistically significant. 36 Figure 2 Appointment status by gender during an 8-week period (October 15–December 15, 2023) Appopintment status by gender Pre-intervention (N=275) 70 60 56.4 50 52.6 43.6 58.8 47.4 41.2 40 Male(%) 30 Female(%) 20 10 0 Completed Cancelled No-show Appointment status by gender during an 8-week period (October 15–December 15, 2024) Apppointment status by gender Post-Intervention (N=483) 60 50 53.3 57.1 46.7 42.9 51.8 48.2 40 30 20 10 0 Completed Cancelled Male(%) No-show Female (%) Note. This figure compares the percentage of completed, canceled, and no-show appointments between male and female patients during the pre-intervention (October 15–December 15, 2023) and post-intervention (October 15–December 15, 2024) periods. Male patients had higher completion rates during both periods, while female patients experienced a higher no-show rate post-intervention. These trends were not statistically significant. 37 Figure 3 Appointment status by age group during an 8-week period (October 15–December 15, 2023) Appointment status by Age Group Pre-Intervention (N=275) Percentage 80 60 20 0 29.4 17.6 40 14.7 6.6 6.7 Less than 18 5.9 11.8 15.3 23.7 5.9 11.8 14.1 17.6 10.5 10.4 22.4 21.5 18.4 8.8 9.2 10.4 18-28 29-39 40-49 50-59 60-69 70-79 0 3.9 3.1 Greater than 80 Age group in years Completed (%) Cancelled (%) No-Show (%) Appointment status by age group during an 8-week period (October 15–December 15, 2024) Percentage Appointment Status by Age Group Post-Intervention (N=483) 70 60 50 40 30 20 10 0 3.5 5.7 8.2 Less than 18 22.4 20 21.2 18.1 18.1 15.8 17.2 16.8 11 17.2 5.9 9.5 11.3 18-28 29-39 40-49 50-59 60-69 70-79 10.6 12.4 15.3 19 16.2 1.2 0.9 2.4 Greater than 80 Age group in years Completed (%) Cancelled (%) No-Show (%) Note. This figure shows appointment status by age group before and after the intervention (Oct 15–Dec 15, 2023, and 2024). Slight improvements were seen in adherence among patients under 18 and those aged 70–79, while no-show rates remained higher in the 18–29 and 60–69 age groups. Changes were not statistically significant. 38 Figure 4 Appointment status by confirmation during an 8-week period (October 15–December 15, 2023) Appointment status by Confirmation Pre-Intervention (N=275) 120 100 80 60 97.1 92.1 82.2 40 20 0 17.8 7.9 Completed 2.9 Cancelled Confirmed(%) No-show Not confirmed(%) Appointment status by confirmation during an 8-week period (October 15–December 15, 2024) Appointment status by Confirmation Post-Intervention (N=483) 100 87.1 82.9 80 60 56.7 43.3 40 17.1 20 12.9 0 Completed Cancelled Confirmed(%) No-show Not confirmed(%) Note. Completion rates were higher, and no-show rates were significantly lower among patients who confirmed their appointments than those who did not. The relationship between confirmation and attendance was statistically significant post-intervention (χ² = 41.748, p < .001), indicating improved patient engagement and adherence. 39 Appendix A 40 Appendix B PowerPoint Slides for the students at the clinic DECREASING THE RATE OF NOSHOWS AT A PRIMARY CARE CLINIC VISHANT THAPA, BSN, DNP STUDENT UNIVERSITY OF UTAH COLLEGE OF NURSING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DOCTOR OF NURSING PRACTICE © UNIVERSITY OF UTAH HEALTH, 2018 BACKGROUND q PREVALANCE 5% to 30% q DEMOGRAPHICS Low-income and chronic condiBons q COST TO HEALTHCARE SYSTEM $150 billion annually ($200-$300 average) q EFFECIENCY Increased burden on the staff © UNIVERSITY OF UTAH HEALTH, 2018 41 PROBLEM STATEMENT q PATIENT’S PERSPECTIVE q Delayed care q Compromised Health q Disrupted ConBnuity of Care q CLINIC’S PERSPECTIVE q Staffing Problems q Financial Impact q Access Issues © UNIVERSITY OF UTAH HEALTH, 2018 PROBLEM STATEMENT q PATIENT’S PERSPECTIVE q Delayed care q Compromised Health q Disrupted ConBnuity of Care q CLINIC’S PERSPECTIVE q Staffing Problems q Financial Impact q Access Issues © UNIVERSITY OF UTAH HEALTH, 2018 42 Objec0ve 3. Implement q Revised workflow of automated text reminder no'fica'ons three 'mes (7,3,1 days) before the pa'ent’s appointment q Tool to ensure that students consistently call each pa'ent at least twice 1 to 2 days before confirming their appointment and track the progress Method Product/Outcome q Implement the automated text reminder no'fica'on 7,3, and 1 day before the appointment q Each pa'ent will receive at least three (7,3,1 days) automated text reminder no'fica'ons before they come to their appointment, and medical and DNP students will consistently call the pa'ent to remind them of their appointment q b. Generate a list of all DNP and Medical students and monthly appointment pa'ent logs; each student will sign up to call at least 5 pa'ents 4. Evaluate: q Effec'veness of the interven'on, including usability, feasibility, and sa'sfac'on of the revised reminder no'fica'on q Generate the no-show rate aRer two months using the EMR soRware q Provide a Red Cap survey to the students on the revised reminder no'fica'on q Evaluate post-implementa'on and survey data q A complete log will ensure each student’s accountability for comple'ng the task of calling pa'ents q Check appointment and student logs every two weeks © UNIVERSITY OF UTAH HEALTH, 2018 TWO MOST IMPORTANT LINKS q https://uofutahmy.sharepoint.com/:x:/g/personal/u0763368_ umail_utah_edu/EV7z6uf4skxMnof nu6zcvkBqTAUIj3zlHOe0uIz58SCOg?e=hvzOFQ q https://uofutahmy.sharepoint.com/:w:/g/personal/u0763368 _umail_utah_edu/EZPbvSD9mJdIhfl 2usF1jAB9H9euRGNCGCzeZOCRILOQ?CID=d0f9b31d -df03-04e3-7536bf10c9741619 © UNIVERSITY OF UTAH HEALTH, 2018 43 SCRIPT FOR CALLING PT. • • • • • • • • • • • • • • Scrip for Calling patients Hi, my name is Vishant, and I am calling from the Rose Park clinic. May I speak with Taylor Swift? I need to confirm a few details for privacy reasons. Can you please verify your DOB? Thank you so much for that. I am calling to remind you of your upcoming appointment with Dr. Raaum on 02 October, this Wednesday, at 2 pm at Rose Park Clinic. Will you be able to make it? Will you need any help with transportation? (workflow for transport) If a new patient – they should be updated on the clinic being student-integrated, students will be involved in all aspects of care with faculty supervision, which means that they have more time with the care team. We do not prescribe long-term opiate therapy but can help connect patients to resources to help with chronic pain. Self-pay patient – If no insurance listed – ask patient if they have insurance, and if not, will need to provide information on presumptive charity. * Make sure info up to date (address, phone number) * Household size / income * Info to be sent to Jeebika Dahal (Student Director of Finance) and Carina Nelson (UU Finance oQice) Please bring your insurance card and list of current medication or medication bottles. We recommend you write down any concerns you hope to address during the visit. We also recommend arriving at least 10-15 minutes early. © UNIVERSITY OF UTAH HEALTH, 2018 Thank you so much for your time, Taylor. We will see you soon. ACKNOWLEDGEMENTS Project Commi,ee q Project Chair: Dr. Larry CurBs GarreZ, PhD, MPH, RN q Content Expert: Dr. Sonja Raaum, MD, FACP Other Roles q Project Sponsor: Dr. Sonja Raaum, MD, FACP q Medical Director- Rose Park Primary Care Clinic q Specialty Track Director: Dr. Amanda Al-Khudairi, DNP, APRN, FNP-C, WHNP-BC q Assistant Dean MS/DNP: Dr. Julie Peila Gee, PhD, MSNED,RN q My email is [e-mail] © UNIVERSITY OF UTAH HEALTH, 2018 44 REFERENCES Hasvold, P. E., & Wootton, R. (2011). Use of telephone and SMS reminders to improve attendance at hospital appointments: A systemati c review. Journal of Telemedicine and Telecare, 17(7), 358-364. https://doi.org/10.1258/jtt.2011.110707 Lee, V. J., Earnest, A., Chen, M. I., & Krishnan, B. (2005). Predictors of failed attendances in a multi-specialty outpatient center using electronic databases. BMC Health Services Research, 5, 51. https://doi.org/10.1186/1472-6963-5-51 Marbouh, D., Khaleel, I., Shanqiti, K. A., Tamimi, M. A., Emre Simsekler, M. C., Ellahham, S., Alibazoglu, D., & Alibazoglu, H. (2020). Evaluating the impact of patient no-shows on service quality. Risk Management and Healthcare Policy, 13, 509-517. https://doi.org/10.2147/RMHP.S232114 McLean, S. M., Booth, A., Gee, M., Salway, S., Cobb, M., Bhanbhro, S., & Nancarrow, S. A. (2016). Appointment reminder systems are effective but not optimal: Results of a systematic review and evidence synthesis employing realist principles. Patient Preference and Adherence, 10, 479-499. https://doi.org/10.2147/PPA.S93046 Parsons, J., Bryce, C., & Atherton, H. (2021). Which patients miss appointments with general practice and the reasons why: A systematic review. The British Journal of General Practice, 71(707), e406. https://doi.org/10.3399/BJGP.2020.1017 Perron, N. J., Dao, M. D., Righini, N. C., Humair, P., Broers, B., Narring, F., Haller, D. M., & Gaspoz, M. (2013). Text-messaging versus telephone reminders to reduce missed appointments in an academic primary care clinic: A randomized controlled trial. BMC Health Services Research, 13, 125. https://doi.org/10.1186/1472-6963-13-125 Schwebel, F. J., & Larimer, M. E. (2018). Using text message reminders in health care services: A narrative literature review. Internet Interventions, 13, 82-104. https://doi.org/10.1016/j.invent.2018.06.002 © UNIVERSITY OF UTAH HEALTH, 2018 45 Appendix C Post-Intervention Questionnaire