Presentation
Factors Contributing to Diagnostic Imaging No-Shows at a Large Canadian Hospital
SessionPoster Session 1
DescriptionBackground
Diagnostic imaging (DI) services, such as MRI and CT scans, are critical to support early detection, accurate diagnosis, and timely treatment for a wide range of medical conditions. DI appointment no-shows (i.e., missed appointments without prior notice or same-day cancellations [1]), can cause financial losses, healthcare provider inefficiency, and negative patient outcomes [2]. For example, MRI no-shows cost hospitals in Ontario, Canada over $6 million in 2018 [3]. Because they are difficult to rebook, no-shows can lead to underutilized resources [3]. For patients, DI no-shows can result in delayed diagnosis and treatment, potentially leading to adverse health outcomes and increased emergency department visits and hospitalizations [4]. These impacts can be more severe for vulnerable populations (e.g., low-income individuals, Indigenous communities, and uninsured patients), who already face systemic barriers to accessing healthcare [5].
Multiple intersecting factors can contribute to DI no-shows. Operational inefficiencies, including long wait times and inadequate appointment confirmation and tracking, can lead to higher no-show rates. For example, a retrospective analysis conducted over 12 months at a large US academic hospital found that DI no-show rates increased linearly with appointment waiting time, particularly if the wait time is longer than seven days [6]. A 2018 audit of four Ontario hospitals found that hospitals sent reminders by mail, with no easy way for patients to confirm them [3]. Additionally, none of the four hospitals tracked reasons for no-shows or how many missed appointments could be filled by other patients [3].
Socioeconomic disparities, including transportation challenges and lack of insurance coverage can also increase no-shows, disproportionately impacting groups such as African American and Hispanic patients in the US [6]. Another US-based study found that uninsured patients or patients with public coverage were more likely than those with private insurance to be no-shows for outpatient radiology appointments [5]. A study conducted in Saudi Arabia (which provides free healthcare coverage to citizens) noted higher MRI no-shows among patients with a high school education or less (compared to those with at least a bachelor’s degree) and patients with no access to transportation (compared to those whose family members would drive them) [7].
Psychological factors, such as anxiety, forgetfulness, and discomfort, can contribute to no-shows [2]. Finally, environmental factors such as increase in snowfall can further exacerbate no-shows [8]. Thus, multi-faceted approaches are required to understand and address DI no-shows.
The Current Study
Using a multi-method approach, this study will investigate causes of DI no-shows at a large hospital in the Greater Toronto Area in Ontario, Canada, with the aim of supporting the development of interventions that reduce no-show rates. Hospital staff have already conducted qualitative analysis of their electronic health record (EHR) data and interviews with clerical staff. Findings pointed to long appointment wait times and the effortfulness of appointment cancellation as key contributors to no-shows. Recommendations from these initial findings included 1) increasing the reminder time prior to an appointment to give patients more opportunity to respond or plan to attend and 2) implementing a new, simpler means of responding to notifications (such as through text message). Upcoming efforts will focus on analyzing EHR data in greater depth and conducting interviews with patients and their caregivers to capture patient-centered perspectives on scheduling challenges and other factors contributing to no-shows, with the goal of developing evidence-based recommendations.
Methods
1. EHR Analysis
Building on the hospital’s previous analysis, additional analysis will be conducted to identify further underlying factors and characteristics of patients who might be more likely to have higher no-show rates. Statistical analysis and predictive modeling will integrate comprehensive information about diagnostic imaging visits, including information on:
• no-shows, cancellations, and rescheduled visits
• type of DI scan (e.g., MRI, CT)
• priority level of the visit (according to the hospital’s classification): Emergent (within 24 hours), Urgent (within 48 hours), Semi-urgent (within 10 days), Non-urgent (within 28 days)
• visit wait time
• patient characteristics (e.g., age, demographics, medical condition and diagnosis)
• diagnostic imaging operating capacity (e.g., hours of diagnostic imaging availability)
To address limitations in previous research, the analysis may also aim to integrate external and contextual information (e.g., weather, time of day of the visit). Findings will help identify patient groups who can be involved in further investigation through interviews and inform targeted interventions.
2. Patient Interviews
While the hospital’s previous efforts involved interviewing clerical DI staff, this study will aim to understand patient concerns. A sample of 20 patients (or their care partners) who have previously missed, cancelled, or rescheduled a DI appointment will be identified based on the EHR analysis and will be interviewed to understand their experiences with missing, cancelling, or rescheduling appointments. Participants will be asked questions regarding the issues they face when scheduling (including rescheduling and canceling) and attending DI appointments (e.g., transportation challenges, contacting the hospital to cancel/reschedule appointments, fear or discomfort during DI procedures). Patients will also be asked about what solutions they believe would help them attend their appointments or cancel/reschedule in a timely manner. Interviews will be conducted over the phone and audio-recorded so that they can later be transcribed and analyzed through thematic analysis. Findings of the patient interviews will be integrated with the EHR analysis insights to inform the design of interventions to reduce DI no-shows at the hospital.
Conclusions and Next Steps
DI no-shows result in inefficient healthcare utilization, sunk financial costs, and negative health consequences for the scheduled patient as well as other patients needing DI services. This project aims to gain an in-depth, patient-centered perspective on the causes of DI no-shows at a large Canadian hospital, which can serve as a case study for other North American hospitals. The expected outcome of the project is a set of design recommendations to alleviate DI no-shows. Proposed interventions may include multiple automated notifications (e.g., through SMS and email) to patients as well as personalized reminders and context-specific instructions. Low fidelity prototypes will be developed to communicate the proposed design specifications.
References
[1] H. D. Schmalzried and J. Liszak, “A Model Program to Reduce Patient Failure to Keep Scheduled Medical Appointments,” J. Community Health, vol. 37, no. 3, pp. 715–718, Jun. 2012.
[2] D. Marbouh et al., “Evaluating the Impact of Patient No-Shows on Service Quality,” Risk Manag. Healthc. Policy, vol. 13, pp. 509–517, Jun. 2020.
[3] Office of the Auditor General of Ontario, “3.08 MRI and CT Scanning Services,” in 2018 Annual Report Volume 1.
[4] A. S. Hwang et al., “Appointment ‘no-shows’ are an independent predictor of subsequent quality of care and resource utilization outcomes,” J. Gen. Intern. Med., vol. 30, no. 10, pp. 1426–1433, Oct. 2015.
[5] A. Aijaz, Z. Hao, T. G.-N. Tran, D. Anderson, J. Shah, and G. Sadigh, “Sociodemographic Factors Associated with Outpatient Radiology No-shows Versus Cancellations,” Acad. Radiol., vol. 31, no. 8, pp. 3406–3414, 2024.
[6] D. Daye, E. Carrodeguas, M. Glover, C. E. Guerrier, H. B. Harvey, and E. J. Flores, “Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors,” J. Am. Coll. Radiol., vol. 15, no. 5, pp. 713–720, May 2018.
[7] M. O. AlRowaili, A. E. Ahmed, and H. A. Areabi, “Factors associated with No-Shows and rescheduling MRI appointments,” BMC Health Serv. Res., vol. 16, no. 1, Art. no. 1, Dec. 2016.
[8] R. J. Mieloszyk, J. I. Rosenbaum, C. S. Hall, D. S. Hippe, M. L. Gunn, and P. Bhargava, “Environmental Factors Predictive of No-Show Visits in Radiology: Observations of Three Million Outpatient Imaging Visits Over 16 Years,” J. Am. Coll. Radiol., vol. 16, no. 4, pp. 554–559, Apr. 2019.
Diagnostic imaging (DI) services, such as MRI and CT scans, are critical to support early detection, accurate diagnosis, and timely treatment for a wide range of medical conditions. DI appointment no-shows (i.e., missed appointments without prior notice or same-day cancellations [1]), can cause financial losses, healthcare provider inefficiency, and negative patient outcomes [2]. For example, MRI no-shows cost hospitals in Ontario, Canada over $6 million in 2018 [3]. Because they are difficult to rebook, no-shows can lead to underutilized resources [3]. For patients, DI no-shows can result in delayed diagnosis and treatment, potentially leading to adverse health outcomes and increased emergency department visits and hospitalizations [4]. These impacts can be more severe for vulnerable populations (e.g., low-income individuals, Indigenous communities, and uninsured patients), who already face systemic barriers to accessing healthcare [5].
Multiple intersecting factors can contribute to DI no-shows. Operational inefficiencies, including long wait times and inadequate appointment confirmation and tracking, can lead to higher no-show rates. For example, a retrospective analysis conducted over 12 months at a large US academic hospital found that DI no-show rates increased linearly with appointment waiting time, particularly if the wait time is longer than seven days [6]. A 2018 audit of four Ontario hospitals found that hospitals sent reminders by mail, with no easy way for patients to confirm them [3]. Additionally, none of the four hospitals tracked reasons for no-shows or how many missed appointments could be filled by other patients [3].
Socioeconomic disparities, including transportation challenges and lack of insurance coverage can also increase no-shows, disproportionately impacting groups such as African American and Hispanic patients in the US [6]. Another US-based study found that uninsured patients or patients with public coverage were more likely than those with private insurance to be no-shows for outpatient radiology appointments [5]. A study conducted in Saudi Arabia (which provides free healthcare coverage to citizens) noted higher MRI no-shows among patients with a high school education or less (compared to those with at least a bachelor’s degree) and patients with no access to transportation (compared to those whose family members would drive them) [7].
Psychological factors, such as anxiety, forgetfulness, and discomfort, can contribute to no-shows [2]. Finally, environmental factors such as increase in snowfall can further exacerbate no-shows [8]. Thus, multi-faceted approaches are required to understand and address DI no-shows.
The Current Study
Using a multi-method approach, this study will investigate causes of DI no-shows at a large hospital in the Greater Toronto Area in Ontario, Canada, with the aim of supporting the development of interventions that reduce no-show rates. Hospital staff have already conducted qualitative analysis of their electronic health record (EHR) data and interviews with clerical staff. Findings pointed to long appointment wait times and the effortfulness of appointment cancellation as key contributors to no-shows. Recommendations from these initial findings included 1) increasing the reminder time prior to an appointment to give patients more opportunity to respond or plan to attend and 2) implementing a new, simpler means of responding to notifications (such as through text message). Upcoming efforts will focus on analyzing EHR data in greater depth and conducting interviews with patients and their caregivers to capture patient-centered perspectives on scheduling challenges and other factors contributing to no-shows, with the goal of developing evidence-based recommendations.
Methods
1. EHR Analysis
Building on the hospital’s previous analysis, additional analysis will be conducted to identify further underlying factors and characteristics of patients who might be more likely to have higher no-show rates. Statistical analysis and predictive modeling will integrate comprehensive information about diagnostic imaging visits, including information on:
• no-shows, cancellations, and rescheduled visits
• type of DI scan (e.g., MRI, CT)
• priority level of the visit (according to the hospital’s classification): Emergent (within 24 hours), Urgent (within 48 hours), Semi-urgent (within 10 days), Non-urgent (within 28 days)
• visit wait time
• patient characteristics (e.g., age, demographics, medical condition and diagnosis)
• diagnostic imaging operating capacity (e.g., hours of diagnostic imaging availability)
To address limitations in previous research, the analysis may also aim to integrate external and contextual information (e.g., weather, time of day of the visit). Findings will help identify patient groups who can be involved in further investigation through interviews and inform targeted interventions.
2. Patient Interviews
While the hospital’s previous efforts involved interviewing clerical DI staff, this study will aim to understand patient concerns. A sample of 20 patients (or their care partners) who have previously missed, cancelled, or rescheduled a DI appointment will be identified based on the EHR analysis and will be interviewed to understand their experiences with missing, cancelling, or rescheduling appointments. Participants will be asked questions regarding the issues they face when scheduling (including rescheduling and canceling) and attending DI appointments (e.g., transportation challenges, contacting the hospital to cancel/reschedule appointments, fear or discomfort during DI procedures). Patients will also be asked about what solutions they believe would help them attend their appointments or cancel/reschedule in a timely manner. Interviews will be conducted over the phone and audio-recorded so that they can later be transcribed and analyzed through thematic analysis. Findings of the patient interviews will be integrated with the EHR analysis insights to inform the design of interventions to reduce DI no-shows at the hospital.
Conclusions and Next Steps
DI no-shows result in inefficient healthcare utilization, sunk financial costs, and negative health consequences for the scheduled patient as well as other patients needing DI services. This project aims to gain an in-depth, patient-centered perspective on the causes of DI no-shows at a large Canadian hospital, which can serve as a case study for other North American hospitals. The expected outcome of the project is a set of design recommendations to alleviate DI no-shows. Proposed interventions may include multiple automated notifications (e.g., through SMS and email) to patients as well as personalized reminders and context-specific instructions. Low fidelity prototypes will be developed to communicate the proposed design specifications.
References
[1] H. D. Schmalzried and J. Liszak, “A Model Program to Reduce Patient Failure to Keep Scheduled Medical Appointments,” J. Community Health, vol. 37, no. 3, pp. 715–718, Jun. 2012.
[2] D. Marbouh et al., “Evaluating the Impact of Patient No-Shows on Service Quality,” Risk Manag. Healthc. Policy, vol. 13, pp. 509–517, Jun. 2020.
[3] Office of the Auditor General of Ontario, “3.08 MRI and CT Scanning Services,” in 2018 Annual Report Volume 1.
[4] A. S. Hwang et al., “Appointment ‘no-shows’ are an independent predictor of subsequent quality of care and resource utilization outcomes,” J. Gen. Intern. Med., vol. 30, no. 10, pp. 1426–1433, Oct. 2015.
[5] A. Aijaz, Z. Hao, T. G.-N. Tran, D. Anderson, J. Shah, and G. Sadigh, “Sociodemographic Factors Associated with Outpatient Radiology No-shows Versus Cancellations,” Acad. Radiol., vol. 31, no. 8, pp. 3406–3414, 2024.
[6] D. Daye, E. Carrodeguas, M. Glover, C. E. Guerrier, H. B. Harvey, and E. J. Flores, “Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors,” J. Am. Coll. Radiol., vol. 15, no. 5, pp. 713–720, May 2018.
[7] M. O. AlRowaili, A. E. Ahmed, and H. A. Areabi, “Factors associated with No-Shows and rescheduling MRI appointments,” BMC Health Serv. Res., vol. 16, no. 1, Art. no. 1, Dec. 2016.
[8] R. J. Mieloszyk, J. I. Rosenbaum, C. S. Hall, D. S. Hippe, M. L. Gunn, and P. Bhargava, “Environmental Factors Predictive of No-Show Visits in Radiology: Observations of Three Million Outpatient Imaging Visits Over 16 Years,” J. Am. Coll. Radiol., vol. 16, no. 4, pp. 554–559, Apr. 2019.
Event Type
Poster Presentation
TimeMonday, March 234:45pm - 6:15pm EDT
LocationRhinelander Gallery
Hospital Environments