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Background: One of the major challenges facing global radiology services comes from delays connected to long waiting lists for magnetic resonance imaging (MRI) procedures. Such delays in diagnostic procedures could lead to poorer patient care outcomes. This study intended to estimate the rate of "No-Shows" or "Reschedule" MRI appointments. We also investigated the factors correlating No-Shows and Reschedule MRI appointments., Methods: A cross-sectional study was conducted in Saudi Arabia using data obtained via MRI schedule reviews and self-administrated questionnaires. Clinical and demographic data were also collected from the study participants. Stepwise binary logistic regression was used to analyze the data., Results: A total of 904 outpatients were asked to participate in the study, and we enrolled 121 outpatients who agreed to complete the study questionnaire. Of the 904 outpatients, the rate of No-Shows or Reschedule was 34.8% (95% Confidence Interval: 31.7-38.1%). Of the 121 outpatients studied, the rate of No-Shows or Reschedule was 49.6% (95% CI: 40.4-58.8%). Those of the female gender (OR = 6.238; 95% CI: 2.674-14.551, p-value = 0.001) and lack of education (OR = 2.799; 95% CI: 1.121-6.986, p-value = 0.027) were highly associated with No-Shows for the MRI appointments. There was no clarification of the MRI instructions (OR = 31.396; 95% CI: 3.427-287.644; p-value = 0.002), and family member drivers (OR = 15.530; 95% CI: 2.637-91.446, p-value = 0.002) were highly associated with rescheduling the MRI appointments., Conclusions: We noted higher rates of No-Shows and Rescheduling of MRI appointments in females, those with a lack of formal education, those who had not received the procedure instructions, and those who lacked transportation. We recommend setting targets and developing strategies and policies to improve more timely access to MRI, and thus reduce the waiting time.

Patient no-shows for scheduled primary care appointments are common. Unused appointment slots reduce patient quality of care, access to services and provider productivity while increasing loss to follow-up and medical costs. This paper describes patterns of no-show variation by patient age, gender, appointment age, and type of appointment request for six individual service lines in the United States Veterans Health Administration (VHA). This retrospective observational descriptive project examined 25,050,479 VHA appointments contained in individual-level records for eight years (FY07-FY14) for 555,183 patients. Multifactor analysis of variance (ANOVA) was performed, with no-show rate as the dependent variable, and gender, age group, appointment age, new patient status, and service line as factors. The analyses revealed that males had higher no-show rates than females to age 65, at which point males and females exhibited similar rates. The average no-show rates decreased with age until 75-79, whereupon rates increased. As appointment age increased, males and new patients had increasing no-show rates. Younger patients are especially prone to no-show as appointment age increases. These findings provide novel information to healthcare practitioners and management scientists to more accurately characterize no-show and attendance rates and the impact of certain patient factors. Future general population data could determine whether findings from VHA data generalize to others.

Background: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity., Objective: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows., Methods: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost., Results: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods., Conclusions: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient's show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.