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Background: This study aimed to identify behavioral factors that affect patient attendance in outpatient clinics, especially those related to rescheduling. Methods: A retrospective study was conducted on 20,386 appointment records for 6,252 patients in an outpatient specialty clinic at a women's and children's hospital in Singapore over 34 months (January 2012–October 2014). Multivariate logistic regression was used to analyze the influence of independent variables on appointment no-shows. Results: The average no-show rate of the study population was 28.87%. Patient historical behaviors were significantly associated with appointment attendance. In particular, a larger number of previous visits, more historical no-shows, more historical rescheduling events initiated by patients, and more reschedulings initiated by the clinic for the current appointment were positively associated with no-shows. Notably, the number of previous visits was found to exhibit a significantly diminishing marginal effect on no-show rates. Further analyses suggested that for appointments rescheduled by the clinic, subsequent rescheduling by patients was associated with a reduced risk of no-shows. Conclusions: No-shows were more common in patients who missed more historical appointments or had their appointments rescheduled more frequently. Encouraging patients to reschedule their own appointments could be an effective measure to reduce no-shows. However, the benefit of patient-rescheduling decreased if patients rescheduled appointments repeatedly. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to evaluate potential predictors of no-shows and late cancellations in an outpatient clinic within a large healthcare system serving vulnerable communities. Methods: Demographic data and appointment status were recorded for 537 consecutive patients scheduled for neuropsychological evaluation in an outpatient psychiatry clinic. Patients include 220 males and 317 females with an average formal education of 11.01 years (SD = 3.87) and age of 55.64 years (SD = 16.20). Results: The overall rate of no-shows or late cancellations was 20%. Of the 106 patients who no-showed/late cancelled, 41% rescheduled, and of those, 23% missed or late cancelled their second appointment. No-shows and late cancellations were associated with historical/prior no-show rate, while race/ethnicity and activation of MyChart had slight impacts. Conclusions: These data suggest that prior no-show rates and MyChart access may be targets for interventions to improve show rates. This is important for the patients' gaining access to care as well as minimizing financial strains for the system and increasing wait times/delays to care for other patients. [ABSTRACT FROM AUTHOR]

Any country's healthcare system is vital for its progress, quality of life, and long-term viability. During the pandemic, many developed countries encountered challenges of differing degrees in the administration of their healthcare systems. The overloading of healthcare services is common, leading to prolonged waiting times for medical services. Thus, the wastage of hospital resources must be taken seriously. In this paper, we examine the problem of no-shows and cancellations in outpatient clinics. By examining the literature and drawing from practical industry experience, we uncover the operational procedures of these clinics. We then suggest a robust optimization strategy for overbooking, incorporating both a conventional overbooking model and a resilient system approach. The proposed model seeks to address the substantial uncertainties in parameters encountered during the pandemic. Taking into account risk aversion, we develop an optimal overbooking policy that considers the associated costs. The primary contribution lies in introducing an alternative approach to manage the uncertainty of no-shows and cancellations through the utilization of an overbooking technique. [ABSTRACT FROM AUTHOR]

To assess prevalence of missed outpatient radiology appointments and sociodemographic factors associated with no-shows vs. cancellations. Adults with outpatient radiology appointments in 2022 and January 2023 at a single tertiary academic health center were included. Generalized estimating equation regression was used to evaluate sociodemographic factors associated with missed vs. completed appointments, no-shows vs. cancellations and time interval between cancellations and appointments. 19,262 (24.3%) examinations were either a cancellation (22.3%) or no-show (2.0%) among 9713 patients (mean age 60.8 ± 15.5; 67.1% female, 63.9% White, 20.0% Asian, 22.0% Hispanics). Among cancellations, 70.19% were patient-initiated. Age ≥ 65 significantly decreased the probability of missed appointments by 5.4% point (pp) (95% CI: 3.7–7.2) or no-shows (4.2 pp; 95% CI, 1.4–6.9), while being single increased probability of missed appointments (2.2 pp; 95% CI, 1.2–3.1) or no-shows (2.6 pp; 95% CI, 1.2–4.1). Those uninsured or with public insurance were 1.3–4.9 pp more likely to miss appointments than commercial insurance, and 2.2–7.6 pp more likely to have no-shows than cancellations. Living in disadvantaged neighborhoods 4.9 pp (95% CI, 3.9–6.0) increased likelihood of missing appointment and was associated with shorter time interval between cancellation and appointment. English speakers were 2.2 pp (95% CI, 1.1–3.3) more likely to miss their exam, while 2.7 pp (95% CI, 1.1–0.4.3) less likely to be a no-show than cancellation. Cancellations represented a significant portion of missed appointments. Specific sociodemographic subgroups exhibited higher tendencies for having missed appointments and no-shows. [ABSTRACT FROM AUTHOR]

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Non-attendance for radiology outpatient appointments is a global issue and is associated with adverse clinical outcomes and operational inefficiencies. This paper aims to understand the underlying factors influencing outpatient radiology attendances based on the Health Belief Model (HBM). Purposive sampling was used to recruit patients (n=30) for in-depth semi-structured telephone interviews. Inclusion criteria comprised participants who were above 21 years old and fluent in English, while participants reliant on third-party assistance (e.g., nursing homes and prison services), to attend the appointment were excluded. The interviews were recorded and transcribed verbatim. The COREQ (Consolidated Criteria for Reporting Qualitative Research) was utilised to guide the reporting of this study. The data analysis involved a hybrid thematic analysis approach using NVivo. Six key themes associated with appointment adherence in radiology were identified. These themes included: 1) prioritising health and acceptance of current medical conditions; 2) the impact of perceived severity on non-attendance; 3) perceived benefits of attending appointments; 4) perceived barriers to attendance; 5) influential prompts; and 6) confidence in attendance. This is the first study of its kind to utilise the HBM to examine factors influencing attendance adherence among radiology outpatients in Singapore. Costs, prompts, and the perceived severity of the condition play pivotal roles in shaping the health-seeking behaviours of these outpatients while professionalism of healthcare staff and barriers to attendance present opportunities for providers to address patients' lack of interest towards their appointments. Findings of this study will contribute to the development of personalised, targeted interventions for improving patient engagement and attendance in radiology settings. [ABSTRACT FROM AUTHOR]

In this work, we consider a joint sequencing and scheduling appointments problem with stochastic service times and no-shows in multi-stage service systems. The objective is to minimise the total expected weighted costs of customers' waiting times and service providers' idle times over multiple stages. For the problem, we first formulate it as a stochastic program and exploit the sample average approximation approach to reformulate it as a mixed-integer program in further. Then we transform the stochastic program into a two-stage optimisation problem and develop a standard Benders decomposition algorithm. To overcome the long running time of Benders decomposition, we simplify the master problem in the algorithm and propose a Benders decomposition-based algorithm to find a near-optimal solution. Finally, we conduct a series of numerical experiments to illustrate the efficiency of our proposed algorithm, examine the impact of the number of stages, stochastic service times and no-shows on the optimal job allowances and performance indicators (i.e. waiting times and idle times) and investigate two easy-to-implement sequence rules. The computational results show that both our proposed Benders decomposition-based algorithm and easy-to-implement sequence rules perform well. [ABSTRACT FROM AUTHOR]

No‐show behavior, a distinct form of absenteeism, is often problematic, particularly in the service industry. Given the growing relevance of the phenomenon in the industry, an increasing number of researchers from such different disciplines as educational economics, healthcare management research, and sports economics have begun examining both the potential antecedents and measures to counteract no‐show behavior, recently. Although the continuously growing body of literature on no‐show behavior has been reviewed before, most such previous attempts were neither interdisciplinary nor systematic, sometimes lacking both completeness and thoroughness. In this article, we address this gap in the still‐emerging literature by providing a systematic, interdisciplinary review of the literature. We observe that, although the potential antecedents are manifold and not always neatly comparable across the different industries, there still exist commonalities, most of which are related to product characteristics and the opportunity costs arising from physical attendance. In contrast, although we document a promising effect of exploiting reminders in reducing no‐show behavior, our understanding of the effectiveness of potential countermeasures is less pronounced. [ABSTRACT FROM AUTHOR]

Any country’s healthcare system is vital for its progress, quality of life, and long-term viability. During the pandemic, many developed countries encountered challenges of differing degrees in the administration of their healthcare systems. The overloading of healthcare services is common, leading to prolonged waiting times for medical services. Thus, the wastage of hospital resources must be taken seriously. In this paper, we examine the problem of no-shows and cancellations in outpatient clinics. By examining the literature and drawing from practical industry experience, we uncover the operational procedures of these clinics. We then suggest a robust optimization strategy for overbooking, incorporating both a conventional overbooking model and a resilient system approach. The proposed model seeks to address the substantial uncertainties in parameters encountered during the pandemic. Taking into account risk aversion, we develop an optimal overbooking policy that considers the associated costs. The primary contribution lies in introducing an alternative approach to manage the uncertainty of no-shows and cancellations through the utilization of an overbooking technique.

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. The utilization of AI in healthcare, particularly in dental clinics, has drawn attention to the issue of appointment no-shows. These no-shows have detrimental effects such as increased waiting times, limited-service access, and financial burden on healthcare providers. Therefore, optimizing the organization of dental clinics is crucial to effectively cater to a diverse patient population with varying dental needs, especially considering the projected rise in demand for dental care. To address the problem of appointment no-shows, the researchers proposed a programming model that harnesses machine learning algorithms. Three specific algorithms, namely Decision Trees, Random Forest, and Multilayer Perceptron, were employed, with the Multilayer Perceptron being used for the first time in this particular context. The researchers collected a dataset from five dental facilities specializing in nine areas and employed Explainable AI techniques to gain insights into the factors contributing to patient absences. The model's performance was evaluated using multiple metrics. The Decision Tree model exhibited favorable accuracy, achieving 79% precision, 94% recall, 86% F1-Score, and 84% AUC (Area Under the Curve). The Random Forest model demonstrated even higher accuracy, with 81% precision, 93% recall, 87% F1-Score, and 83% AUC. Similarly, the Multilayer Perceptron model attained an accuracy of 80% precision, 91% recall, 86% F1-Score, and 83% AUC.

Newsvendor models have been developed to determine the optimal overbooking level of hotel rooms to manage no-shows and cancellations. This research note extends the newsvendor model to a restaurant context by taking into account the "stretched capacity" of restaurants to determine the optimal overbooking levels for restaurant seats. Data were collected from a restaurant in Taiwan to illustrate the model. The percentage of no-shows per day in this restaurant ranged from 11% to 16%. Utilizing its stretched capacity, the restaurant can overbook up to five more seats than the estimated number of no-shows. The extended model will be most suitable for restaurants that largely depend on reservations (rather than walk-ins), such as luxury or fine-dining restaurants. Directions for future research on restaurant overbooking are provided. [ABSTRACT FROM AUTHOR]

Appointment no-shows in clinics can adversely impact patients and physicians alike. This study aimed to determine the rate and potential causes of missed appointments in oculoplastic clinics and compare a private practice and hospital-based academic setting. A retrospective review of patients who booked appointments for oculoplastic consultation, between August 2019 and January 2020 at two oculoplastic clinics was performed. Demographic and patient-specific characteristics of patients who failed to attend their appointment were identified. Data were analysed to determine and compare the no-show rates in both clinics and logistic regression was performed to determine factors associated with them. The rate of missed appointments was 3% and 17% at the oculoplastic clinics of Lions Eye Institute (LEI, private practice) and Albany Medical Center (AMC, academic hospital-based office), respectively. Patients at the AMC clinic were more likely to be male, younger, have a lower household income, not carry private insurance, and suffer from trauma. Logistic regression analysis showed lower patient age to significantly increase the likelihood of no-shows in both clinics (p =.01 for LEI, p =.003 for AMC), and lead appointment time greater than 90 days to be a significant risk factor for no-shows at LEI (p =.01). The no-show rate for oculoplastic appointments is 3% and 17% at LEI and AMC clinics, respectively. Our analysis shows that younger patients are more likely to miss appointments at both clinics, and an appointment lead time greater than 90 days is a significant risk factor for no-shows at LEI. [ABSTRACT FROM AUTHOR]

No-shows of customers are common in many appointment-based service systems that often cause severe idling and underutilization of capacity. This paper studies a new strategy that pre-charges customers for a deposit to lock up appointments, which differs from previous studies on overbooking strategies that reserve more appointments than its capacity to hedge against the loss of revenue due to customer no-shows. Moreover, the new strategy is more favorable in practice because it improves the satisfaction and loyalty of customers in the long-term running. We analyze two versions of the pre-charge strategy including (i) offering a partial or full refund for cancellation (to-refund policy), and (ii) offering no refund for cancellation (no-refund policy). We consider two settings of the appointment scheduling problem including a static setting in which appointment decisions are made at the beginning of the day, and a dynamic setting in which appointment decisions are made sequentially within the day. The two settings are formulated by an analytic model and a Markov decision process model, respectively. The analytical results reveal that the pre-charging strategy is consistently more profitable than the overbooking strategy even for a fully refundable deposit case, and the to-refund policy can be more profitable than the no-refund policy. Moreover, we show that the profitability of the to-refund policy continues to hold in a dynamic setting in which the service capacity is limited, the arrivals are dynamic, and the report of cancellation is uncertain. Finally, we implement a series of numerical experiments based on real data in the context of managing patient appointments for a large clinic and demonstrate the managerial implications. [ABSTRACT FROM AUTHOR]

Purpose: The COVID-19 pandemic continues to have major and long-lasting impacts on health care delivery and mental health. As health care shifted to telehealth, legislation was adjusted to expand telehealth allowances, creating a unique opportunity to elucidate outcomes. The aim of this study was to assess long-term patient and clinician satisfaction and outcomes with virtual behavioral health. Methods: Data were obtained over 16 months from surveys to patients and clinicians receiving/providing virtual treatment. Outcomes data also were collected from medical records of adults receiving in-person and virtual behavioral health treatment. Data were summarized using descriptive statistics. Groups were compared using various chi-squared tests for categorical variables, Likert response trends over time, and conditional independence, with Wilcoxon rank-sum or Jonckheere trend test used to assess continuous variables. P-values of ≤ 0.05 were considered statistically significant. Results: Patients gave high ratings to virtual treatment and indicated a preference for virtual formats. Both patient and clinician preference for virtual visits increased significantly with time, and many clinicians perceived virtual services to be equally effective to in-person. Virtual programs had higher completion rates, attendance rates, and number of treatment visits, suggesting that virtual behavioral health had equivalent or better outcomes to in-person treatment and that attitudes toward telehealth changed over time. Conclusions: If trends found in this study continue, telehealth may emerge as a preferred option long term This is important considering the increase in mental health needs associated with the COVID-19 pandemic and the eventuality that in-person restrictions ease as the pandemic subsides.

We study how to admit and schedule heterogeneous patients by using simple, interpretable, yet effective policies when capacity is scarce, no‐show behavior is patient‐ and time‐dependent, service duration and reward are deterministic but patient‐dependent, and overtime is costly. Our work is motivated by the aforementioned operational challenges that typically face adopters of new technologies in the healthcare sector. We anchor our study on a partnership with the proton therapy center of Massachusetts General Hospital (MGH), which offers a new radiation technology for cancer patients. We formulate the problem as a nonlinear integer optimization problem. However, as the solution to this formulation lacks both tractability and interpretability, to be relevant to practice, we limit our study to simple and interpretable policies. In particular, we propose a simple index‐based rule and derive analytical performance guarantees for it. We also calibrate our model using empirical data from our partner hospital, and conduct a series of experiments to evaluate the performance of our proposed policy under practical circumstances. The analytical performance guarantees and our numerical experiments demonstrate (a) the strong performance of the proposed policies, and (b) their robustness to various practical considerations (e.g., to potential misspecification of no‐show probabilities). Our results show that our proposed policy, despite being a simple and interpretable index‐based rule, is capable of improving performance by about 20% at an organization such as MGH, and of delivering results that are not far from being optimal across a wide range of parameters that might vary between organizations. This suggests that the proposed policy can be viewed as an effective "one‐fits‐all" capacity allocation rule that can be used in a variety of environments in which operational challenges such as no‐shows and overtime costs need to be navigated using simple and interpretable rules. [ABSTRACT FROM AUTHOR]

Background: Patients missing scheduled hospital appointments pose significant challenges, including resource waste and delayed patient care. This study evaluated the effectiveness of personalised reminder systems (phone calls and short message service (SMS)) in improving patient attendance rates at a radiology department., Methods: The study was conducted at a hospital facility in Saudi Arabia. The intervention involved reminding 493 patients of their radiology appointments using their preferred method (phone call or SMS). Demographic, clinical, and other factors were considered in analysing the impact of these reminders on appointment attendance. Attendance rates before the intervention were further compared with attendance rates after the intervention to assess the effectiveness of the studied strategies., Results: Patient reminders affected overall patient attendance, with a 5% improvement compared to the attendance rates before the intervention. Phone call reminders were found to be more effective than SMS, particularly among older patients (41-60 years). The attendance rate for patients receiving phone call reminders ranged from 35% to 85%, whereas those receiving SMS reminders had a 15-65% attendance range. The study indicated marital status and distance as key factors associated with attendance. Chi-square analysis also highlighted significant differences in attendance rates before and after the intervention, particularly among female patients, single and divorced individuals, and those with at least secondary education. Patients living more than 35 km from the hospital and those referred from other hospitals were more likely to miss appointments, irrespective of the intervention., Conclusion: Personalised phone call reminders seem to be more effective than SMS in reducing missed appointments, especially among older patients. This study highlighted the importance of considering patient demographics and preferences in designing reminder systems to enhance healthcare appointment adherence., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Faculty of Health Sciences Research Ethics Committee, Trinity College Dublin issued approval 220905. Prior to commencing the intervention phase, the researcher obtained the necessary approval to ensure adherence to ethical standards. The initial approval was granted by the Research Ethics Committee. Subsequently, this approval was presented to the Regional Ethics Committee, King Fahad Specialist Hospital, which then endorsed the intervention phase, affirming the study’s compliance with ethical guidelines as established by the Ethics Committee at the college. Throughout the study, key ethical considerations were diligently observed. The principle of beneficence was a guiding factor in the design and implementation of the intervention, prioritising no harm to participants while maximising potential benefits. Additionally, the principle of justice was rigorously adhered to, ensuring fair treatment and equal opportunity for all participants. This comprehensive ethical approach emphasised the study’s commitment to participant rights, welfare, and data integrity, reflecting dedication to upholding the highest standards of ethical research practice. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Alturbag et al.)

Objectives: Missed appointments negatively affect patients, providers, and health systems. This study aimed to (1) quantify the percentage of missed appointments across 14 pediatric subspecialties in a tertiary-care children's hospital and (2) identify patient characteristics associated with missed appointments in those subspecialties. Methods: We extracted patient characteristics from 267,151 outpatient appointments, between January 1, 2013, and December 31, 2018, across 14 subspecialty clinics. Medical complexity was categorized using the Pediatric Medical Complexity Algorithm. The primary outcome was appointment nonattendance. Cancellations, imaging/laboratory visits, patients older than 18 years, and duplicate visits were excluded. Characteristics associated with nonattendance were analyzed with chi-square tests and included in the multivariable model if p <.1. Missing data were addressed using random forest imputation, and assuming data were "missing at random." Variables were considered statistically significant if p <.05. Results: Of the 128,117 scheduled appointments analyzed, 23,204 (18.1%) were missed. In the multivariable model, clinical nutrition had the greatest subspecialty odds of missed appointments, whereas cardiology had the lowest. Patient characteristics most strongly associated with missed appointments were public insurance, history of >2 missed appointments, appointment lead time, lesser medical complexity, Black race/ethnicity, and fewer medications. Conclusions: Clinical characteristics including lesser medical complexity and fewer medications are associated with missed appointments in pediatric subspecialties. [ABSTRACT FROM AUTHOR]

Objectives: Extensive literature exists documenting disparities in access to healthcare for patients with lower socioeconomic status (SES). The objective of this study was to examine access disparities and differences in surgical wait times in children with the most common pediatric otolaryngologic surgery, tympanostomy tubes (TT). Methods: A retrospective cohort study was performed at a tertiary children's hospital. Children ages <18 years who received a first set of tympanostomy tubes during 2015 were studied. Patient demographics and markers of SES including zip code, health insurance type, and appointment no-shows were recorded. Clinical measures included risk factors, symptoms, and age at presentation and first TT. Results: A total of 969 patients were included. Average age at surgery was 2.11 years. Almost 90% were white and 67.5% had private insurance. Patients with public insurance, ≥1 no-show appointment, and who lived in zip codes with the median income below the United States median had a longer period from otologic consult and preoperative clinic to TT, but no differences were seen in race. Those with public insurance had their surgery at an older age than those with private insurance (P <.001) and were more likely to have chronic otitis media with effusion as their indication for surgery (OR: 1.8, 95% CI: 1.2-2.5, P =.003). Conclusions: Lower SES is associated with chronic otitis media with effusion and a longer wait time from otologic consult and preoperative clinic to TT placement. By being transparent in socioeconomic disparities, we can begin to expose systemic problems and move forward with interventions. Level of Evidence: 4 [ABSTRACT FROM AUTHOR]

Purpose/objectives: To evaluate patient attendance for moderate sedation (MS) at a university‐based pediatric dental clinic; to identify factors with negative impact on MS being completed and to assess for associations between no‐show appointments and patient variables Methods: The electronic health records of patients scheduled for MS appointments in a 22‐month period were assessed by a single investigator. Demographic and clinical data related to appointment attendance and MS procedure performance were collected and statistically analyzed using chi‐square, Spearman's rho correlation tests, and logistic regression (p < 0.05) Results: A total of 618 scheduled MS appointments were included. The MS appointment no‐show‐rate was 17.1 percent. Appointment confirmation (p = 0.001) and dmft score ≥ 9 (p = 0.039) had positive correlation with attendance, while "no‐shows" history (p = 0.024) and longer waiting time (p = 0.040) had negative impact on attendance. About 20% of attended MS were not completed, with main reasons of airway risk (32.3%), ongoing illness (28.4%), and violation of NPO guidelines (21.5%). Race, ethnicity, language spoken, child's behavior, and distance traveled had no significant impact on attendance Conclusion: Interventions to decrease non‐attendance rates should target patients who are unconfirmed, have a history of no‐show appointments, and are scheduled well before the sedation appointment. [ABSTRACT FROM AUTHOR]

Objectives: To test the feasibility and preliminary effectiveness of the NIATx model for organizational change to reduce appointment no‐shows in dental care settings. Methods: The NIATx Dental Pilot Study used a clustered prepost interventional design and a mixed‐methods approach. Five independent dental clinics serving Medicaid enrollees were recruited. Quantitative data on the impact of the NIATx model were complemented by qualitative assessment information collected from dental staff. The NIATx model was applied through a multisite learning collaborative that engaged participating organizations in adopting targeted no‐show best practices. The primary outcome measure was no‐show data collected at the preintervention phase (5 months), intervention phase (7 months), and postintervention phase (3 months). Results: Female patients were in the majority (median 54.5 percent). Mean age ranged from 8 to 50 (median 35.2) years. The median percentage for Hispanics was 11.0 percent, and the proportion of uninsured patients was 25 percent. Preintervention no‐show rates ranged from 14 percent (clinic B) to 38 percent (clinic E). All clinics had lower no‐show rates during the postintervention period, with decreases ranging from 1 percent (clinic B) to 13 percent (clinic E). Overall, the no‐show rates decreased in the study. Conclusions: The study demonstrated the feasibility of applying the NIATx model to reduce no‐show rates, with some difficulty observed with sustainability across dental practices during the postintervention period. [ABSTRACT FROM AUTHOR]

Dounia Marbouh,1 Iman Khaleel,1 Khawla Al Shanqiti,1 Maryam Al Tamimi,1 Mecit Can Emre Simsekler,1,2 Samer Ellahham,3 Deniz Alibazoglu,3 Haluk Alibazoglu4 1Research Center of Digital Supply Chain and Operations, Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates; 2School of Management, University College London, London, UK; 3Heart and Vascular Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates; 4Imaging Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab EmiratesCorrespondence: Mecit Can Emre SimseklerKhalifa University of Science and Technology, Department of Industrial and Systems Engineering, P.O. Box 127788, Abu Dhabi, United Arab EmiratesTel +9712 501 8410Fax +971 2 447 2442Email emre.simsekler@ku.ac.aePurpose: Patient no-shows are long-standing issues affecting resource utilization and posing risks to the quality of healthcare services. They also lead to loss of anticipated revenue, particularly in services where resources are expensive and in great demand.Methods: In order to address common reasons why patients miss appointments, this study reviews the current literature and investigates various tools and methods that have been implemented to mitigate such issues. Further, a case study is conducted to identify the rate of no-shows and underlying causes at a radiology department in one of the leading hospitals in the MENA region.Results: Our results show that the no-shows are high due to multiple factors, such as patient behavior, patients’ financial situation, environmental factors and scheduling policy.Conclusion: In conclusion, we generate a list of recommendations that can help in reducing the rate of patient no-shows, such as patient education, application of dynamic scheduling policies and effective appointment reminder systems to patients.Keywords: quality, no-shows, overbooking, resource utilization, scheduling policy, patient appointment, predictive analytics

Abstract Background Missed healthcare appointments (no-shows) are costly and operationally inefficient for health systems. No-show rates are particularly high for vulnerable populations, even though these populations often require additional care. Few studies on no-show behavior or potential interventions exist specifically for Federally Qualified Health Centers (FQHCs), which care for over 24 million disadvantaged individuals in the United States. The purpose of this study is to identify predictors of no-show behavior and to analyze the effects of a reminder intervention in urban FQHCs in order to design effective policy solutions to a protracted issue in healthcare. Methods This is a retrospective observational study using electronic medical record data from 11 facilities belonging to a New York City-based FQHC network between June 2017 to April 2018. This data includes 53,149 visits for 41,495 unique patients. Seven hierarchical generalized linear models and generalized additive models were used to predict no-shows, and multiple regression models evaluated the effectiveness of a reminder. All analyses were conducted in R. Results The strongest predictor of no-show rates in FQHCs is whether or not patients are assigned to empaneled providers (z = − 91.45, p

In this article, we contribute to the emerging literature on the potential determinants of football spectator no-show behaviour by analysing disaggregated data capturing season ticket holder (STH) behaviour outside the German market for the first time. Intriguingly, our empirical analysis of a unique panel dataset – containing distinct admission decisions made by 8,734 STHs of an established Swiss Super League club on 72 consecutive matchdays between 2013 and 2016 – suggests that the antecedents of such no-show behaviour might be contextual, despite some notable similarities across markets. For instance, although we observe a robust role of both a STH's emerging no-show habits and his/her geographical distance to the stadium in predicting spectator no-show behaviour that is in line with previous research, we also note a more nuanced, i.e. non-linear, relationship between a STH's age and the tendency to omit attendance on matchday. Here, using three-dimensional contour plots to represent potential interactions with age graphically, we also observe that STHs in the age cohort around 35 to 65 are more affected by barriers in the form of distance to the place of residence or temperature compared to people of retirement age, for whom there is a very robust pattern of habit. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives: Missed appointments are a persistent problem across healthcare settings, and result in negative outcomes for providers and patients. We aimed to review and evaluate the effectiveness of interventions designed to reduce missed appointments in safety net settings. Methods: We conducted a systematic review of interventions reported in three electronic databases. Data extraction and quality assessment were conducted according to PRISMA guidelines. Eligible studies were analyzed qualitatively to describe intervention types. A random effects model was used to measure the pooled relative risk of appointment adherence across interventions in the meta‐analysis. Results: Thirty‐four studies met inclusion criteria for the qualitative synthesis, and 21 studies reported sufficient outcome data for inclusion in the meta‐analysis. Qualitative analysis classified nine types of interventions used to increase attendance; however, application of each intervention type varied widely between studies. Across all study types (N = 12 000), RR was 1.08, (95% CI 1.03, 1.13) for any intervention used to increase appointment attendance. No single intervention was clearly effective: facilitated appointment scheduling [RR = 3.31 (95% CI: 0.30, 37.13)], financial incentives [RR = 1.88 (0.73, 4.82)] case management/patient navigator [RR = 1.09, (0.96, 1.24)], text messages [RR = 1.02 (0.96, 1.08)], transportation, [RR = 1.05 (0.98, 1.13)], telephone reminder calls [RR 1.12, (0.87, 1.45)], in‐person referrals, [RR = 1.01 (0.90, 1.13)], patient contracts [RR = 0.87 (0.52, 1.46)] or combined strategies, [RR = 1.16 (1.03, 1.32)]. No strategy was clearly superior to others, p interaction =.50. Conclusions: Strategies to improve appointment adherence in safety net hospitals varied widely and were only modestly effective. Further research harmonizing intervention delivery within each strategy and comparing strategies with the most potential for success is needed. [ABSTRACT FROM AUTHOR]

Introduction. Patients’ failure to keep medical appointments or to cancel them is becoming an increasing problem for the health care system. The phenomenon of missing medical appointments refers to patients of both public and private healthcare facilities. It has a negative impact on the functioning of the health care system, by contributing to longer waiting times for health services and generating financial losses. Aim. The aim of the study was to analyze no-shows in female patients aged 18-35. Material and method. The research tool used in the study was a survey that was conducted among 705 women aged 18-35. The survey questionnaire had been developed by the authors of the study and consisted of particulars and five thematic areas. The first area concerned cancellation of medical appointments and the reasons behind it. In the second area, patients’ punctuality was assessed. The third area focused on preferred methods of arranging and cancelling medical appointments. In the fourth area, women’s opinion on the idea of spreading lists of negligent patients among medical facilities was examined. The fifth area referred to patients’ solutions to the problem of not keeping medical appointments. The obtained data was subjected to statistical analysis using the ² test, where p < 0.05 was considered to be a statistically significant. Results. The study shows that nearly 75% of women have canceled their medical appointment at least once. The phenomenon of not keeping appointments and not cancelling them concerned in particular women aged 29-35 (33%). In each of the three age groups, the most frequent reasons for not attending an appointment and not cancelling it were forgetfulness, recovery from a health condition, other more important errands and personal situation. Over a quarter of respondents declared having been late for an appointment at least once (28%). Nearly half of the respondents inform medical facilities about the fact of being late (46%). Conclusions. It is necessary to monitor patients’ failure to keep medical appointments, introduce a general system of reminding patients about medical appointments and educate patients about the obligation to inform medical facilities about cancelling appointments.

Background: No-shows are a persistent and costly problem in all healthcare systems. Because forgetting is a common cause of no-shows, appointment reminders are widely used. However, qualitative research examining appointment reminders and how to improve them is lacking. Objective: To understand how patients experience appointment reminders as part of intervention development for a pragmatic trial of enhanced appointment reminders. Design: Qualitative content analysis Participants: Twenty-seven patients at a single Department of Veterans Affairs hospital and its satellite clinics Approach: We conducted five waves of interviews using rapid qualitative analysis, in each wave continuing to ask veterans about their experience of reminders. We double-coded all interviews, used deductive and inductive content analysis to identify themes, and selected quotations that exemplified three themes (limitations, strategies, recommendations). Key Results: Interviews showed four limitations on the usability of current appointment reminders which may contribute to no-shows: (1) excessive information within reminders; (2) frustrating telephone systems when calling in response to an appointment reminder; (3) missing or cryptic information about clinic logistics; and (4) reminder fatigue. Patients who were successful at keeping appointments often used specific strategies to optimize the usability of reminders, including (1) using a calendar; (2) heightening visibility; (3) piggybacking; and (4) combining strategies. Our recommendations to enhance reminders are as follows: (1) mix up their content and format; (2) keep them short and simple; (3) add a personal touch; (4) include specifics on clinic location and contact information; (5) time reminders based on the mode of delivery; and (6) hand over control of reminders to patients. Conclusions: Appointment reminders are vital to prevent no-shows, but their usability is not optimized for patients. There is potential for healthcare systems to modify several aspects of the content, timing, and delivery of appointment reminders to be more effective and patient-centered. [ABSTRACT FROM AUTHOR]

Non-attendance at hospital appointments is an extremely prevalent issue impacting healthcare systems on a daily basis. This phenomenon adversely affects patient health and healthcare providers, leading to delays in diagnosis and treatment, inefficient resource utilization, and increased healthcare expenses. The detrimental impact of non-attendance is not limited to patients who miss appointments, the knock-on effects of extended waiting times and reduced appointment availability are felt throughout healthcare systems. The purpose of this narrative review is to explore the factors underlying appointment non-attendance in hospital settings, to improve healthcare delivery and patient adherence. An extensive review of the existing global literature was conducted. Quantitative studies that explored the relationship between appointment non-attendance and patient characteristics, such as age, gender, marital status, education level, distance from the hospital, and source of referral, were included. Younger patients, males, individuals with lower levels of education, and those living farther from hospitals were more likely to miss appointments. Marital status was significant, with married patients showing better attendance, as was referral source, with general practitioner referrals associated with higher non-attendance. Qualitative studies identifying both patient-centered and hospital-specific reasons, such as forgetfulness, appointment time, protracted waiting times, patient-physician relationship, and patients' knowledge and perception of their health condition, were also included in the review. Lack of appointment reminders, difficulties in managing appointments, and inadequate patient-physician communication were significant hospital-specific reasons given for non-attendance. Patients' lack of awareness regarding the importance of attending appointments and limited understanding of their health conditions were also identified as patient-centered contributors. Non-attendance at hospital appointments is a multifaceted issue influenced by a range of socioeconomic, personal, and systemic factors. Addressing these factors requires a holistic approach that includes patient education, improved communication, and tailored healthcare delivery strategies, especially for vulnerable populations in rural areas. Enhanced reminder systems and streamlined appointment management could serve as pivotal interventions to reduce non-attendance rates, ultimately improving healthcare outcomes and resource utilization., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Alturbag et al.)

No-shows in medical centres cause under-utilisation of resources and increase waiting times in specialty health care services. Although this problem has been addressed in literature, behavioural issues associated with the patient's socio-demographic characteristics and diagnosis have not been widely studied. In this article, we propose a model that includes such behavioural issues in order to reduce impact of no-shows in medical services. The objective is maximising the health centre's expected revenue by using show-up probabilities estimated for each combination of patient and appointment slot. Additionally, the model considers the requirements imposed by both the health centre's management and the health authorities. An extension of the model allows overbooking in some appointment slots. Experimental results show that the proposed model can reduce the waiting list length by 13%, and to attain an increase of about 5% in revenue, when comparing to a model that assigns patients to the first available slot. [ABSTRACT FROM AUTHOR]

Background: Missed healthcare appointments (no-shows) are costly and operationally inefficient for health systems. No-show rates are particularly high for vulnerable populations, even though these populations often require additional care. Few studies on no-show behavior or potential interventions exist specifically for Federally Qualified Health Centers (FQHCs), which care for over 24 million disadvantaged individuals in the United States. The purpose of this study is to identify predictors of no-show behavior and to analyze the effects of a reminder intervention in urban FQHCs in order to design effective policy solutions to a protracted issue in healthcare.Methods: This is a retrospective observational study using electronic medical record data from 11 facilities belonging to a New York City-based FQHC network between June 2017 to April 2018. This data includes 53,149 visits for 41,495 unique patients. Seven hierarchical generalized linear models and generalized additive models were used to predict no-shows, and multiple regression models evaluated the effectiveness of a reminder. All analyses were conducted in R.Results: The strongest predictor of no-show rates in FQHCs is whether or not patients are assigned to empaneled providers (z = - 91.45, p < 10- 10), followed by lead time for appointments (z = 23.87, p < 10- 10). These effects were fairly stable across facilities. The reminder had minimal effects on no-show rates overall (No show rate before: 41.6%, after: 42.1%). For individuals with appointments before and after the reminder, there was a small decrease in no-shows of 2%.Conclusions: The limited effects of the reminder intervention suggest the need for more personalized behavioral interventions to reduce no-shows. We recommend that these begin with increasing the use of empaneled providers for preventive care appointments and reducing the lag time between setting the appointment and the actual date of the appointment, at least for individuals with a high rate of no-show. By complementing these with low-intensity, low-cost behavioral interventions, we would expect greater impacts for improved access to care, contributing to the well-being of vulnerable populations. [ABSTRACT FROM AUTHOR]

Despite a rich literature on the psychological responses to terrorist attacks, surprisingly little is known about the subsequent changes in citizen behavior. In this research note, we exploit a unique sequence of two rare events, the Paris attacks of 13 November 2015 and the cancelation of the international friendly between Germany and the Netherlands over terrorist threats only four days later, to compare the number of German football spectator no-shows, i.e., the number of ticket holders that have decided not to attend a particular football game, before and after these attacks. Although we observe a significant increase in the number of no-shows during the first two weeks after the incidents, this effect was not permanent. [ABSTRACT FROM AUTHOR]

Trisha M Crutchfield,1–3 Christine E Kistler2–4 1University of North Carolina Center for Health Promotion and Disease Prevention, 2University of North Carolina Lineberger Comprehensive Cancer Center, 3Cecil G. Sheps Center for Health Services Research, 4Department of Family Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Purpose: Between 23% and 34% of outpatient appointments are missed annually. Patients who frequently miss medical appointments have poorer health outcomes and are less likely to use preventive health care services. Missed appointments result in unnecessary costs and organizational inefficiencies. Appointment reminders may help reduce missed appointments; particular types may be more effective than other types. We used a survey with a discrete choice experiment (DCE) to learn why individuals miss appointments and to assess appointment reminder preferences.Methods: We enrolled a national sample of adults from an online survey panel to complete demographic and appointment habit questions as well as a 16-task DCE designed in Sawtooth Software’s Discover tool. We assessed preferences for four reminder attributes – initial reminder type, arrival of initial reminder, reminder content, and number of reminders. We derived utilities and importance scores.Results: We surveyed 251 adults nationally, with a mean age of 43 (range 18–83) years: 51% female, 84% White, and 8% African American. Twenty-three percent of individuals missed one or more appointments in the past 12 months. Two primary reasons given for missing an appointment include transportation problems (28%) and forgetfulness (26%). Participants indicated the initial reminder type (21%) was the most important attribute, followed by the number of reminders (10%). Overall, individuals indicated a preference for a single reminder, arriving via email, phone call, or text message, delivered less than 2 weeks prior to an appointment. Preferences for reminder content were less clear.Conclusion: The number of missed appointments and reasons for missing appointments are consistent with prior research. Patient-centered appointment reminders may improve appointment attendance by addressing some of the reasons individuals report missing appointments and by meeting patients’ needs. Future research is necessary to determine if preferred reminders used in practice will result in improved appointment attendance in clinical settings. Keywords: reminders, discrete choice experiment, no-show rates, text messaging, survey, preferences, appointment attendance, DNAs, conjoint analysis

Despite an extensive literature on stadium attendance demand, our understanding of those factors shaping spectator no-show behaviour is rudimentary at best. Here, we explore such behaviour by using a comprehensive two-step approach: First, we examine the determinants of no-show behaviour in the German Bundesliga between the four seasons 2014–15 and 2017–18; Second, because our initial results imply that spectator no-show behaviour is more prominent among season ticket holders (STHs), we exploit additional survey data to understand individual STH no-show appearances better. Our results suggest that club managers interested in reducing the no-show rate should rethink existing season ticket strategies. [ABSTRACT FROM AUTHOR]

Patient no‐shows and late cancellations lead to clinic inefficiency, high clinic costs and low patient satisfaction. The two main strategies clinics employed to alleviate the adverse effects of no‐shows are overbooking and patient appointment reminders. Developing effective overbooking schedules depends on accurately predicting each patient's no‐show probability, while developing effective reminder systems requires a patient‐level estimate of communication sensitivity. Current methods of estimating no‐show probabilities do not produce such patient‐level predictions. To remedy this, we develop a Bayesian nested logit model which utilizes appointment confirmation data and estimates individual‐level coefficients for patient‐specific predictors. A log‐likelihood comparison of model fit on 12 months of appointment data shows that the Bayesian model outperforms the standard logit model by about 30% improvement in model fit. Additionally, our Bayesian model allows categorization of patients based on their appointment confirmation behavior. Finally, using patient‐specific no‐show probabilities as an input to a simulated appointment scheduler we find that the Bayesian model improves clinic profit over the standard logit model. The benefit comes mainly from waiting cost reduction when no‐show probability is low and from physician overtime and idle time cost reduction when no‐show probability is high. Our study has two managerial implications. First, the Bayesian method allows customizing appointment reminder effort based on patient's confirmation behavior. Second, the Bayesian method also allows improved overbooking scheduling especially in clinics that experience large patient throughput. [ABSTRACT FROM AUTHOR]

Purpose: No-shows of patients to their scheduled appointments have a significant impact on healthcare systems, including lower clinical efficiency and higher costs. The purpose of this study was to investigate the factors associated with patient no-shows in a bariatric surgery clinic.Materials and Methods: We performed a retrospective study of 13,230 records for 2660 patients in a clinic located in Rio de Janeiro, Brazil, over a 17-month period (January 2015-May 2016). Logistic regression analyses were conducted to explore and model the influence of certain variables on no-show rates. This work also developed a predictive model stratified for each medical specialty.Results: The overall proportion of no-shows was 21.9%. According to multiple logistic regression, there is a significant association between the patient no-shows and eight variables examined. This association revealed a pattern in the increase of patient no-shows: appointment in the later hours of the day, appointments not in the summer months, post-surgery appointment, high lead time, higher no-show history, fewer numbers of previous appointments, home address 20 to 50 km away from the clinic, or scheduled for another specialty other than a bariatric surgeon. Age group, forms of payment, gender, and weekday were not significant predictors. Predictive models were developed with an accuracy of 71%.Conclusion: Understanding the characteristics of patient no-shows allows making improvements in management practice, and the predictive models can be incorporated into the clinic dynamic scheduling system, allowing the use of a new appointment policy that takes into account each patient's no-show probability. [ABSTRACT FROM AUTHOR]

To evaluate the effectiveness of patient-chosen appointment reminder methods (phone call, e-mail, or SMS text) in reducing no-show rates. Materials and Methods: This was a retrospective case study that determined the correlation between patient-chosen appointment reminder methods and no-show rates in a private orthodontic practice. This study was conducted in a single office location of a multioffice private orthodontic practice using data gathered in 2015. The subjects were patients who self-selected the appointment reminder method (phone call, e-mail, or SMS text). Patient appointment data were collected over a 6-month period. Patient attendance was analyzed with descriptive statistics to determine any significant differences among patient-chosen reminder methods. Results: There was a total of 1193 appointments with an average no-show rate of 2.43% across the three reminder methods. No statistically significant differences (P¼.569) were observed in the no-show rates between the three methods: phone call (3.49%), e-mail (2.68%), and SMS text (1.90%). Conclusions: The electronic appointment reminder methods (SMS text and e-mail) had lower noshow rates compared with the phone call method, with SMS text having the lowest no-show rate of 1.90%. However, since no significant differences were observed between the three patient-chosen reminder methods, providers may want to allow patients to choose their reminder method to decrease no-shows. [ABSTRACT FROM AUTHOR]

We develop a stochastic model for a carve-out appointment system for a single physician in a primary care facility that allocates certain slots for same-day requests while reserving the rest for appointments requested in advance. Several slots are double-booked to mitigate the negative impacts from no-shows. Our model takes into account the stochastic demands of both routine patients and same-day requests as well as no-shows and optimizes the overall system performance, which includes the primary care provider’s utility and patients’ waiting time. We derive the optimal number of open slots, the maximum number of patients allowed to be double-booked, and where these open slots and double-booked slots should be placed. A key contribution of our approach is that both capacity decisions and scheduling decisions are obtained simultaneously. Moreover, our approach allows the optimal schedules to be analytically characterized by the demand distribution of same-day requests, the no-show rate, and the ratio of overtime cost to waiting cost for the special case where it is optimal to leave one open slot and one double-booked slot. Numerical results are used for more general cases to provide managerial insights on the usage of open slots and double-booked slots. The optimal schedule should start with a few open slots. If the no-show rate is low, single-booked slots should be placed before the first double-booked slots. On the other hand, if the no-show rate is high, a double-booked slot should be placed immediately after the series of open slots. Additional open slots should be inserted either between two double-booked slots or between one double-booked slot and a series of single-booked slots. The strategic placement of open slots not only increases the physician’s utilization but also improves patients’ aggregated waiting time. [ABSTRACT FROM AUTHOR]

Delayed treatment and non-adherence are associated with inferior prostate cancer (CaP) outcomes. Missed clinic appointments (MA) are one form of non-adherence that may be preventable. We conducted a retrospective cohort study of 1341 scheduled clinic encounters for men referred to an academic urology clinic for evaluation of known or suspected CaP. Driving distance and public transit times were calculated using a Google Distance Matrix API algorithm. Zip code level data regarding socioeconomic status was obtained from the 2013 American Community Survey. Logistic regression multivariate analysis was used to identify MA predictors. Of scheduled clinic encounters, 14% were missed. Public health insurance was associated with MA (Private insurance 10%, Public insurance 19%), (p < 0.01) Calendar month was associated with MA with December showing the highest rate (21.2%) and June the lowest (5.3%) rates. (p = 0.02) Appointments for suspected CaP were more likely to be missed (19.3%) than those for known CaP (10.5%), p < 0.01. Driving distance was inversely associated with rate of MA (CA median 11.8 miles, MA median 10.4 miles, p = 0.04) while public transit times were not (66.7 min for CA, 65.3 min for MA, p = 0.36). Men that missed appointments were from areas with lower household incomes and educational attainment. Patient encounter type, insurance status, and reason for referral remained significantly associated with MA after multivariable adjusted analysis. By computing public transit time to the clinic using a mapping engine, we present a novel way to measure this parameter for studies of urban health care. [ABSTRACT FROM AUTHOR]

We investigate the scheduling practices of a multidisciplinary, multistage, outpatient health care program. Patients undergo a series of assessments before being eligible for elective surgery. Such systems often suffer from high rates of attrition and appointment no‐shows leading to capacity underutilization and treatment delays. We propose a new scheduling model where the clinic assigns patients to an appointment day but postpones the decision of which assessments patients undergo pending the observation of who arrives. In doing so, the clinic gains flexibility to improve system performance. We formulate the scheduling problem as a Markov decision process and use approximate dynamic programming to solve it. We apply our approach to a dataset collected from a bariatric surgery program at a large tertiary hospital in Toronto, Canada. We examine the quality of our solutions via structural results and compare them with heuristic scheduling practices using a discrete‐event simulation. By allowing multiple assessments, delaying their scheduling, and by optimizing over an appointment book, we show significant improvements in patient throughput, clinic profit, use of overtime, and staff utilization. [ABSTRACT FROM AUTHOR]

No-shows in child and adolescent psychiatry are a particularly concerning phenomenon that have ramifications beyond productivity and mental health outcomes. One might contend that children have a fundamental right to health care, and the failure to address the no-show phenomenon passively impedes that human right. In this study, a queue-controlled modified open access scheduling model (QCMOAS) was compared to a traditional scheduling model for the incidence of no-shows in a community mental health child psychiatric med check clinic. A six month period of QCMOAS was compared to the preceding six months of traditional scheduling and the six month period exactly one year prior to QCMOAS. Z test was used for statistical significance. For the six month period immediately after the implementation of OCMOAS, a 7.32% decrease in no-show rate was observed, and compared to the six month period exactly 1 year prior to QCMOAS, a decrease of 6.38% was noted. In both cases the result was statistically significant. Preliminary interpretation of this data suggests that the employment of OCMOAS significantly decreased the no-show rate in this community mental health center’s child and adolescent med check clinic. The findings are strengthened by the relatively large number of scheduled appointments reviewed and by the fact that statistical significance appeared to be unrelated to seasonal scheduling patterns. Limitations of this pilot study and recommendations for future investigation are discussed.