Vision 2030 healthcare efficiency: Radiology no-shows at King Fahd Hospital of the University – Financial and operational burden

Abstract

BACKGROUND:

No-shows present a global challenge in managing the resources of healthcare systems, particularly in radiology departments, where missed appointments result in machine idle time, staff underutilization, and significant loss of revenue. The objective of the study was to quantify the financial and operational burden of outpatient radiology no-shows at an academic tertiary care hospital.

MATERIALS AND METHODS:

In this cross-sectional study, data were retrospectively reviewed for all outpatient appointments at the Radiology Department of King Fahd Hospital of the University (KFHU) from January 1, 2024, to October 31, 2025. Data were extracted from the Hospital Information System. Financial loss was estimated by using the hospital’s private services price list in Saudi Riyals (SAR), and operational burden was assessed using machine idle hours and staff underutilization hours.

RESULTS:

During the study period, out of 76,926 overall outpatient radiological appointments, 17.9% (n = 13,743) appointments were “no-show.” The “no-shows” resulted in substantial financial losses, with magnetic resonance imaging (MRI) contributing to the highest cumulative burden. Operational impact included 3.5–4 h of idle machine time and staff underutilization per missed case.

CONCLUSION:

Radiology no-shows in the study represent a measurable association between missed appointments and estimated operational and financial loss at KFHU; longer appointment lead time was an important associated factor, suggesting that a reduction of booking intervals and prioritization of targeted scheduling strategies may help improve efficiency in radiology, resource utilization, and patient access.

Introduction

Patients who fail to show up for their planned outpatient service appointments without prior notification are referred to as “no-shows.”[1] No-show is a known cause of inefficiency, such as machine idle hours in outpatient services. It refers to periods when functional medical equipment is powered on, but remains unused. This has significant repercussions in radiology, considering the wasted expense incurred with idle equipment, the need for specialist personnel, and the therapeutic significance of prompt diagnostic imaging.[2,3,4,5]

Radiology no-show rates range from 10% to 35%, according to international surveys.[6] No-shows result in both direct income loss and indirect costs owing to inefficient scheduling, personnel downtime, and idle machine time.[7] In addition to creating false scarcity, unfilled slots lengthen wait times and cause delays in other patients’ diagnoses.[8] The reasons for no-shows in the radiology department are multi-factorial.[3] Patient-related factors include age, gender, history of no-shows, and health literacy levels.[9] Healthcare systems factors, such as inadequate reminder systems, inflexible scheduling, long appointment lead times,[6,10] and unclear information on the need to prepare for the appointment, have also been implicated,[9,11] as has payor type.[12] However, the degree to which different patient groups and healthcare systems are affected by these factors varies.[13]

The Saudi Vision 2030 program in Saudi Arabia is based on three key pillars: access to high-quality healthcare services, resource optimization, and healthcare efficiency.[14] Despite these national imperatives, the local healthcare research on the financial and operational implications of outpatient radiology no-shows is limited, especially as it relates to the country’s tertiary academic healthcare facilities.[8,9] King Fahd Hospital of the University (KFHU) is a tertiary academic facility that offers a broad range of diagnostic imaging modalities.[15,16]

Therefore, this study’s aim was to quantify the financial and operational burden of outpatient radiology no-shows at KFHU. Data were distributed in varied groups according to patient and appointment characteristics. Evidence-based recommendations were made in alignment with Saudi Vision 2030 healthcare efficiency goals.

Materials and Methods

This cross-sectional study retrospectively examined outpatient appointments from January 1, 2024, to October 31, 2025, which were recorded as “Not Attended” (no-shows) and “Attended” (shows) at the Radiology Department of the study hospital. The inclusion criteria comprised all outpatient radiology appointments booked between January 1, 2024, and October 31, 2025, that were documented as “Not Attended.” Conversely, records that were outside of the study period were from emergency or inpatient settings; had negative lead times; or had a status other than “Attended” or “Not Attended” were eliminated during the data cleaning process. Ethical approval was obtained from the Institutional Review Board of Imam Abdulrahman Bin Faisal University vide Letter No. IRB-UGS-2025-01-0677 22/10/2025, with a waiver of informed consent since there was no direct relation with human subjects in this study.

A total of 13,743 appointments were included. No formal sampling was performed; the study used a census approach, capturing all eligible no-show appointments within the defined study period from the Hospital Information System (HIS).

Following ethical approval, a formal request for data was then submitted to the Information Technology department to extract all outpatient radiology records from the InterSystems TrakCare HIS for the period of January 1, 2024, to October 31, 2025. A formal sample size calculation was not performed because the study used a census approach to include all eligible outpatient radiology appointments recorded in the hospital scheduling system during the predefined study period. The study period, determined prior to data extraction as the period of study, represented the most recent complete months available at the time of data collection planning in November 2025. The 22 months were selected to maximize the use of complete available data. Both attended, and no-shows were extracted to enable direct comparison. The resulting dataset comprised several fields per appointment, including Unique Reference Number (URN), booking and appointment dates, status, doctor/care provider, department, service, payor, patient name, age, gender, nationality, patient category, modality, and price list service name.

All patient names were removed from the data to protect their privacy, but URNs were kept as the only patient identifier to maintain the required appointment-level link. For thorough cleaning and preparation based on particular exclusion criteria, the dataset was imported into Microsoft Excel. Records that were outside the study period, or from emergency or inpatient settings, or had negative lead times, or had a status other than “Attended” or “Not Attended” were eliminated. After cleaning, the dataset was exported to IBM SPSS Statistics for examination.

The lead time was found by subtracting the booking date from the appointment date. Appointments were grouped according to the type of imaging used, and were expected to occur during Ramadan if they were booked during the official Ramadan dates, from March 10 to April 9, 2024, and from March 1 to March 29, 2025.

The main results were the number of people who didn’t show up and the revenue lost because of it. To calculate the economic impact, the hospital connected each appointment to the private service fees listed, giving a specific service cost in Saudi Arabian Riyals (SAR), including the cost of such items as contrast agents, kits, and active substances. The financial loss for each no-show was considered the full-service fee, assuming that missed appointments could not be easily replaced. The total amount of lost revenue was found by adding up all the costs from unattended appointments.

Secondary outcome measures included the number of hours when machines were not in use and the number of hours when staff were not fully utilized. The calculation of machine idle time and staff underutilization hours was based on fixed standard values provided by the hospital for each missed appointment. Data were divided into groups based on the type of test, who paid for the care, which department referred the patient, the patient’s category, their nationality, and whether the appointment was scheduled during Ramadan. Monetary losses were calculated both for each person and for each group.

The data were analyzed using the Statistical Package for the Social Sciences software, version 31.0 (SPSS, IBM Corporation, Armonk, NY, USA), and Microsoft Excel was used to create graphs. As the continuous variables were not normally distributed, nonparametric tests were applied. The Mann–Whitney U test was used to compare lead time between attended and no-show appointments, while the Kruskal–Wallis test was used for comparisons across more than two groups. Categorical variables were summarized as frequencies and percentages and analyzed using the Chi-square test. Spearman’s correlation was used to assess the association between lead time and operational inefficiency. P < 0.05 was considered statistically significant.

Results

The following section describes the descriptive and inferential analysis of no-show appointments in the Radiology Department at the study hospital. Overall, missed appointments accounted for less than one-fifth of scheduled radiology visits, with an overall no-show rate of approximately 17%–18%. This indicates generally good attendance in the department; however, no-show patterns varied substantially according to imaging modality, appointment lead time, payor category, specialty, and month of appointment.

Table 1 summarizes the demographic and administrative characteristics of the study population. Most no-show patients were female, representing 57.1% of the sample. Adults aged 19–64 years accounted for the largest age group at 66.6%, followed by patients aged ≥65 years at 23.3%, while patients aged ≤18 years represented 10.1%. Saudi patients accounted for 85.3% of cases. Government-funded patients represented the majority of no-show appointments at 94.6%, followed by insurance, self-funded, and noneligible patients.

Table 1.

Demographic and administrative characteristics of the patients with no-show for outpatient appointments at the Radiology Department of King Fahd Hospital of the University (KFHU) from January 1, 2024, to October 31, 2025

Characteristics		N (%)
Gender
Female		7848 (57.1)
Male		5895 (42.9)
Age group
0–18		1393 (10.1)
19–64		9150 (66.6)
65 and older		3200 (23.3)
Nationality group
Saudi		11,663 (85.3)
Arab Asia		714 (5.2)
Non-Arab Asia		680 (5.0)
African		582 (4.3)
Western		37 (0.3)
Missing/unknown		67 (0.5)
Payor
Government		13,005 (94.6)
Insurance		515 (3.8)
Noneligible		5 (0.0)
Self-pay/unspecified		218 (1.6)

The highest no-show rates were observed in surgical specialties, at approximately 44%, followed by general medical specialties at approximately 28%. Oncology and interventional radiology had the lowest no-show rates, at approximately 1.5%. Monthly variation was also observed, with the highest proportions of missed appointments in March 2025 and July 2025, at approximately 22% and 21%, respectively. August 2024 had the lowest proportion of no-shows at 14.4%. The association between appointment month and attendance status was statistically significant using the Chi-square test (χ² = 169.978, P < 0.001), although no consistent increasing or decreasing trend was identified over time.

Table 2 shows the distribution of no-show appointments across imaging modalities. Ultrasound accounted for the largest proportion of no-show appointments, representing 29.8% of all no-shows, followed by X-ray at 25.1%, magnetic resonance imaging (MRI) at 22.8%, computed tomography (CT) at 16.6%, Mammography at 3.2%, and Nuclear Medicine at 2.4%. The difference across modalities was statistically significant using the Chi-square test (χ² (5) = 65189.738, P < 0.001). Higher no-show rates were concentrated in more complex and resource-intensive modalities, particularly MRI, Nuclear Medicine, and CT.

Table 2.

Distribution of no-show appointments at the Radiology Department of King Fahd Hospital of the University (KFHU) from January 1, 2024, to October 31, 2025 imaging modality

Modality		No-show n		Percentage of all no-shows		Show n		Total appointments
Ultrasound		4094		29.8		17,012		21,106
X-Ray		3455		25.1		26,440		29,895
MRI		3139		22.8		7429		10,568
CT		2286		16.6		8736		11,022
Mammogram		440		3.2		2492		2932
Nuclear medicine		329		2.4		1074		1403
Total		13,743		100.0		63,183		76,926

X-Ray=X-Radiation, MRI=Magnetic resonance imaging, CT=Computed tomography

Table 3 presents continuous variables, including machine idle hours, staff underutilization hours, and service cost. Missed appointments resulted in measurable operational inefficiency, with a mean of 3.49 idle machine hours and equivalent staff underutilization hours per no-show.

Table 3.

Descriptive statistics for machine idle hours, staff underutilization, and service price for no-show appointment at the Radiology Department of King Fahd Hospital of the University (KFHU) from January 1, 2024, to October 31, 2025

Variable		Mean±SE		95% CI		Median		Trimmed mean		SD		Minimum–maximum		Range		IQR
Machine idle hours		3.49±0.007		3.48–3.50		4.00		3.53		0.872		2–5		3		1
Staff underutilization hours		3.49±0.007		3.48–3.50		4.00		3.53		0.872		2–5		3		1
Price		1078.91±8.75		1066.79–1091.04		720.00		989.73		1025.44		0–17,500		17,500		1400

CI=Confidence interval, SE=Standard error, IQR=Interquartaile range, SD=Standard deviation

Lead time was substantially longer in no-show appointments than in attended appointments. The mean lead time of no-show patients was 80 days, with a median of 40 days, compared with a mean of 42 days and a median of 2 days among attended patients. This difference was statistically significant using the Mann–Whitney U-test (P < 0.001). Lead time also varied by category of funding, with government-funded patients showing the longest mean lead time.

Table 4 shows the total estimated financial loss associated with missed appointments. The cumulative estimated revenue loss was 14.83 million SAR. MRI accounted for the greatest cumulative financial loss, followed by CT and Ultrasound. Nuclear Medicine had the highest mean cost per missed appointment, but its lower appointment volume limited its total contribution. These findings indicate that the financial burden was influenced by both the cost of each missed appointment and the number of missed appointments within each modality.

Table 4.

Financial burden of no-shows by modality, including total financial loss and cost per missed appointment at the Radiology Department of King Fahd Hospital of the University (KFHU) from January 1, 2024, to October 31, 2025

Modality		Total financial loss (SAR)		Contribution (%)		Mean		Median		Minimum		Maximum		SD		P25		P75
MRI		6,211,700		41.9		1979		1800		0		3000		339		1800		2200
CT		3,994,700		26.9		1747		1600		750		17,500		918		1200		1800
Ultrasound		2,176,600		14.7		532		400		0		3000		307		400		650
Nuclear medicine		1,202,350		8.1		3655		2500		800		7500		2440		1900		7000
X-ray		883,410		6.0		256		150		90		2100		280		100		260
Mammogram		358,750		2.4		815		750		500		8000		689		750		750
Total		14,827,510		100

SAR=Saudi Arabian Riyal, CI=Confidence interval, X-ray=X-Radiation, MRI=Magnetic resonance imaging, CT=Computed tomography, SD=Standard deviation

Operational burden differed across imaging modalities. Staff underutilization was highest in Mammography, Nuclear Medicine, and MRI, while X-ray showed the lowest underutilization. Machine idle hours also varied by category of funding, with government-funded patients showing the highest mean idle time.

Longer appointment lead time was moderately associated with increased operational inefficiency, with a correlation coefficient of approximately r = 0.39 and P < 0.001. In contrast, age had only a negligible association with operational inefficiency. Ramadan periods contributed only a small proportion of no-show appointments, suggesting that operational planning should focus more on modality type, lead time, and scheduling intervals rather than seasonal or religious periods alone.

Figure 1 presents the seasonal and temporal distribution of no-show appointments. The highest monthly no-show percentages were observed in March 2025 and July 2025, while the highest absolute counts of missed appointments were observed in December 2024, May 2024, and January 2024. Although month-to-month variation was present, the overall pattern of attendance remained relatively stable.

Figure 1.

Monthly attendance pattern of appointments at the Radiology Department of King Fahd Hospital of the University (KFHU) from January 1, 2024, to October 31, 2025

Ramadan periods contributed only a small proportion of no-show appointments. This suggests that religious seasonal factors did not have a major influence on attendance behavior in this study. Instead, findings indicate that operational planning should focus more on imaging modality, appointment lead time, and scheduling intervals rather than seasonal or religious periods only.

Discussion

The time it takes to schedule and the type of imaging to be used were associated with patients not showing up for appointments. Even though there was a lot of demand for these services, expensive imaging equipment often remained unused, which made the financial impact worse compared to other outpatient services.[6,12,17,18,19] The number of no-shows differed considerably depending on the type of imaging; Ultrasound accounted for the largest proportion of missed appointments, representing 29.8% of all no-shows, followed by X-ray at 25.1%, MRI at 22.8%, CT at 16.6%, mammography at 3.2%, and nuclear medicine at 2.4%. Previous studies have repeatedly shown that the rate of nonattendance went up as the treatment method became more complex. Marbouh et al., found a big difference between different types of scans and missed appointments. In some cases, MRI appointments were missed by more than 80% of patients, while X-ray appointments were usually not missed much, staying under 10%.[3] Alturbag et al., also noticed that a high percentage of MRI appointments (34.5%) were either missed or rescheduled.[9] There are many reasons for this; for example, the complexity of the procedures, patient worry, longer scan times, and longer wait times could lead to more people not showing up for advanced imaging tests.[3] In contrast, more familiar, shorter-duration, and simpler modalities such as X-Ray are generally more convenient and accessible for patients.[20]

Lead time can be considered another major factor associated with patient no-shows. In the patient show group, the median lead time was only 2 days compared to a markedly longer median of 40 days in the no-show group. This 38-day difference suggests that after a certain point, patients’ involvement dropped a lot. This aligns with the findings of bigger studies, models, and reviews. These studies usually point out that the amount of time before an appointment, called lead time, is a main reason why people don’t show up.[6,21,22,23] Qualitative research has also connected long waiting times with memory problems, difficulty in planning appointments, and unhappy patients.[9]

In this study, MRI showed the longest waiting times, while X-ray had the shortest. Even though nuclear medicine had a quicker process than both ultrasound and mammography, more people did not show up for their appointments with nuclear medicine at 38% compared to 32% for ultrasound and 26% for mammography. This means that factors related to the type of communication used might be more important than the amount of time interval before an event.[6,12] In the end, as the time needed to complete tasks grew longer, more workers were not being used properly, and machines were kept idle for longer periods, which made the whole operation less efficient.[19]

The cost of no-shows depended on the type of imaging used. Nuclear Medicine had the highest cost for each missed appointment, then MRI and CT came next. Even though MRI did not have the highest cost per case, its value was the highest overall, owing to its cost plus the substantial volume of cases. Nuclear Medicine had a lower number of cases, so its total impact was smaller. This shows that the focus of effort should be on both the cost per case and the number of cases.[19]

Radiology, in particular, has operational issues because of patients not showing up, the result of which is idle machines. Each missed appointment usually results in about 3.5 h of unused time for each case. Nuclear medicine and mammography had the highest idle time per case, but since they have fewer appointments, the greater focus for improvement on running the facility should be on MRI and CT.

There was a weak link between the background traits and missing appointments by the mostly adult, government-supported, and female group in the study. This matches earlier research in radiology that found that scheduling issues were more important than patient age or gender in predicting no-shows.[13] Important is the moderate link between lead time and operational inefficiency (r = 0.39). This shows that shortening booking windows is a clear means of improving efficiency by helping to get more people to attend and making better use of resources. This matches the goals of the Health Sector Transformation Program, which is part of the Saudi Vision 2030’s plan to increase efficiency.[24] From a value-based medicine point of view, the 14.83 million SAR of lost revenue shows that the money spent did not help the patient in any way. This is a big system problem that can be greatly improved with organized, data-based scheduling changes.[25]

This study has several limitations. The study was done in just one hospital, so the results might not be applicable to other hospitals or radiology departments. Second, the retrospective design helps to identify links, but is not able to show any direct causal effect of lead time, no-shows, and operational inefficiency on each other. Third, since patients were not involved, the specific reasons for some missed their appointments are unknown.[3,26] Fourth, the financial loss was calculated using the hospital’s own list of private service prices, which might not completely reveal the actual money received or the cost of missed opportunities for different types of paying customers.[7]

This study has several strengths. The study used a big real-world dataset, looking at missed appointments across different types of medical imaging tests from a large hospital. Besides tracking people who don’t show up, it also measured the money lost and the extra work caused, such as when machines were idle, and the use of personnel was inefficient. The analysis focused on specific types of services and found that, though some were heavily used, usage could be more efficient with focused management.

Future research should include prospective patient surveys to identify reasons for nonattendance, such as transportation issues, anxiety, communication problems, or lack of understanding of the importance of the appointment. Interventional studies are also needed to test reminder systems, call-center confirmation, shorter booking windows, and predictive models for high-risk patients.[26,27,28,29] Finally, multicenter studies would provide a broader national understanding of the burden of radiology no-shows and allow the comparison of different healthcare settings.

Findings of this study underscore the issue of critical financial and managerial burdens imposed by patient no-shows with real-world implications for radiology department administrators and hospital policymakers. The use of this data will help the responsible departments to identify the modalities, lead time patterns, and referring specialties most associated with the no-show burden.[6] Targeted interventions such as reminder systems and dynamic overbooking, and shorter booking windows[26] could thus be implemented to minimize operational and financial losses to accord with Saudi Vision 2030 healthcare efficiency goals.[30]

Conclusion

Outpatient radiology no-shows represented a measurable source of financial and operational burden, with an overall no-show rate of 17.9% and an estimated cumulative loss of 14.83 million SAR. The greatest total financial loss was the result of MRI no-shows, while nuclear medicine had the highest cost per missed appointment. Each no-show was associated with an average of 3.49 idle machine hours and equivalent staff underutilization hours. Longer appointment lead time was moderately associated with greater operational inefficiency. These findings support targeted, data-driven interventions focused on high-resource modalities and long booking intervals to improve radiology efficiency and patient access.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.