Background and Objectives:Patient no-show in scheduled appointments is a major challenge for outpatient clinics. It negatively affects the efficiency, accessibility, and delivery of healthcare. This study was conducted to investigate the prevalence and potential predictors of patient no-show in outpatient clinics of a general and teaching hospital in Tehran, Iran. Methods: In this cross-sectional study, all outpatients who had scheduled appointments from March 20, 2016 to March 20, 2017 were included in the study (N = 148,077). Independent two-sample t-test and the Chi-square test were used for comparing the variables in the two groups-attending and no-show patients. Logistic regression was used to analyze predictors of no-show. Results: The no-show rate was 50.1%. General practice (80.3 %) and nephrology (40.1%) clinics had the highest and the lowest noshow rates, respectively. The mean lead time of appointments was 10.2 (± 14.7) days, while the average lead times for no-show and attending patients were 11.7 (± 15.6) and 8.8 (± 13.7) days respectively (P < 0.001). Lead-time of more than two weeks (OR = 1.80), web-based appointment system (OR = 1.71), interactive voice response appointment system (OR = 1.69), month of appointment (OR = 1.03), and clinic working shift (OR = 0.94) were the predictive variables of patient no-show. Conclusions: Findings indicate that appointment lead time is the main predictor of no show. Therefore, deploying strategies to reduce lead time, such as increasing the number of physicians, increasing working hours, or improving clinic efficiency can improve patient attendance. Other findings suggest that appointment reminders via text message, cancellation policy, and nurse-led telephone triage can be expected to decrease patients no-show.

Background and Purpose: Patient absconding from a hospital or leaving without permission is a major challenge with economic and social consequences for patients, healthcare staff, and society. Identification of the characteristics of absconding patients is essential to the prediction and prevention of such behaviors. Methods: This cross sectional study was performed in a large general teaching hospital in Tehran, Iran. Data of 460 absconding patients and 460 nonabsconding patients, admitted during March 2010 -March 2016, were extracted from the hospital information system. Backward stepwise logistic regression analysis was used to describe the importance and effect of each predictor. Results: The findings showed that 0.4% of the patients had left the hospital without permission or notice to the staff. Addiction

Background: While the ultimate goal of health care is to maintain or improve the health status, features of care, including the quality of hotel services, are important to enhance the quality of health care services. Due to the limited resources of the health system, it is important to recognize the strengths and weaknesses of health service providers for policy-making and allocating resources in line with customers’ expectations. Objectives: The current study aimed at ranking the dimensions of hospital hoteling services from patients’ perspectives using the importance-performance analysis (IPA) method in Tehran, Iran. Methods: In this cross-sectional study, 440 patients were enrolled through multi-stage random sampling. The data collection tool was a questionnaire consisting of 44 items in six dimensions. Its internal reliability was determined based on Cronbach’s alpha coefficient (α = 0.97). The IPA method was used to prioritize the dimensions. Data were analyzed using descriptive statistics and the paired t-test at a significance level of < 0.05. Results: In all aspects of hoteling, there was a significant difference between the performance of hospitals and the expectations of patients. The highest and lowest gaps between performance and expectation were related to the economic-financial and performance-process factors, respectively (P < 0.05). Based on the IPA matrix, the "physical-structural" and "economic-financial" factors were the major weaknesses, and the "need to allocate more resources" and "human-behavioral" factors were recognized as the most strong points. Considering that the dimensions of "cultural-religious” and "safety-security" were located in the third quadrant of the IPA matrix, these dimensions did not require additional resources. Conclusions: The existence of a gap between hospital performance and patient expectation in all the hoteling dimensions indicated that the performance of hospitals in providing hoteling services was not proportional to the importance of these factors from the viewpoints of patients. The "physical-structural" and "economic-financial" factors were the major weaknesses of the hospitals studied. Therefore, it is necessary to pay more attention and allocate resources to these dimensions. Efforts should be continued to maintain and upgrade the status quo regarding the human-behavioral factors to boost patient satisfaction and improve the quality of hoteling services.