Patient No-shows Research Articles

Erratum to Impact of Technical Support and Information Provision on Patient No-Show Behavior

Erratum to Impact of Technical Support and Information Provision on Patient No-Show Behavior

Managing no-shows in healthcare: insights from an urban Health District in Northern Italy

Abstract Background Non-attendance at healthcare appointments, known as no-show, poses significant challenges to healthcare systems, disrupting patient care continuity, exacerbating waiting times, and straining resources. Addressing this issue is crucial for optimizing health care delivery and containing costs. This study explores the phenomenon of patient no-shows and aims to identify factors associated in a densely populated healthcare district in Turin, Northern Italy. Methods Data were extracted from the healthcare management system covering outpatient care appointments in the health district in 2022 and 2023. The analysis included various patient and service-related variables. Missing information on key variables were managed with listwise deletion. Descriptive statistics provided an overview of missed appointment rates, while multivariate analysis explored various patient and service-related variables. Results A 5.1% (n = 6198) no-show rate was observed out of 120,405 records. The total cost incurred due to missed appointment fees amounted to €127,244. Several factors were associated with a significantly increased likelihood of no-show (p < 0.05), including initial visit (OR = 1.11), deferred priority (OR = 1.22), non-exempt status from healthcare co-payments (OR = 1.36), exemption based on income (OR = 1.82), age under 18 (OR = 2.46) and under 65 (OR = 2.32), male gender (OR = 1.17), residency outside the Piedmont Region (OR = 1.31) and outside Italy (OR = 2.46), and longer waiting time (OR = 1.01). Conclusions This study sheds light on the widespread issue of patient no-shows and its economic implications. It provides clear direction for implementing tailored interventions in healthcare settings. As a response to these findings, a telephone recall project was launched. Its efficacy will be evaluated in subsequent assessments to determine if it can effectively alleviate the burden of no-shows, thus enhancing healthcare efficiency and ensuring continuity of care for patients. Key messages • The no-show phenomenon has important implications both organizationally and economically. • The magnitude and major determinants of patient no-shows must be recognized to guide the deployment of tailored interventions in a real-world setting.

Impact of Technical Support and Information Provision on Patient No-show Behavior

Patient no-shows pose a significant challenge in healthcare operations, disrupting appointment schedules and affecting overall efficiency. Effectively addressing the challenge of reducing patient no-shows is crucial for outpatient service management. This study evaluates how the design of appointment systems affects patient no-show behavior. Using a difference-in-differences methodology, we analyze the effects of two appointment system update events—technical support and information provision—at a Chinese hospital. Our analysis reveals that technical support and information provision are associated with average reductions of 22.40% and 10.91%, respectively. To investigate the mechanisms behind these effects, we conduct a randomized controlled experiment with 233 participants. Our findings reveal that perceived effort and credibility mediate the relationship between information provision and patient no-shows. However, for technical support, only perceived credibility acts as a mediator. This study provides valuable insights for healthcare operations, offering design recommendations to address no-show behavior in hospital appointment systems.

Predicting Patient No-Shows: Situated Machine Learning with Imperfect Data.

Patients who do not show up for scheduled appointments are a considerable cost and concern in healthcare. In this study, we predict patient no-shows for outpatient surgery at the endoscopy ward of a hospital in Denmark. The predictions are made by training machine leaning (ML) models on available data, which have been recorded for purposes other than ML, and by doing situated work in the hospital setting to understand local data practices and fine-tune the models. The best performing model (XGBoost with oversampling) predicts no-shows at sensitivity = 0.97, specificity = 0.66, and accuracy = 0.95. Importantly, the situated work engaged local hospital staff in the design process and led to substantial quantitative improvements in the performance of the models. We consider the results promising but acknowledge that they are from a single ward. To transfer the no-show models to other wards and hospitals, the situated work must be redone.

A retrospective cross-sectional analysis of predictors of patient no-shows in adult outpatient dermatology

A retrospective cross-sectional analysis of predictors of patient no-shows in adult outpatient dermatology

Revolutionizing Schedules: The Power of AI in Physician Practices.

Since the early 2000s, artificial intelligence (AI) has raised concerns regarding its use in healthcare to manage vast amounts of patient data, ensure proper handling, and maintain robust security measures. Nevertheless, contemporary healthcare organizations are exploring ways AI can safely enhance operational efficiency and support their patient populations. Successful, evidence-based utilization relies on a well-defined ambulatory strategy, and operational efficiency must be foundational to that strategy. Patient no-shows and appointment compliance, especially in the context of social determinants of health such as access, present inherent obstacles to patient and provider satisfaction, continuity of care, practice productivity, and the financial sustainability of an organization. To address these obstacles, Berkeley Research Group has been working with Phoebe Physician Group. Their shared objective is twofold: enhance patient encounter volume and the associated revenue. This article provides insights into the steps taken to integrate AI and machine learning to mitigate the problem of no-shows by automatically double-booking appointments for patients with a high probability of not showing up. A glimpse into the outcomes achieved and lessons learned throughout the process also is presented.

Reducing No-Show Appointments in Preanesthesia Screening

PurposePreanesthesia screening is critical to evaluate the patient’s medical and surgical history prior to any procedure or surgery to assess for risks and to optimize outcomes during the perioperative period. Missed appointments impede the ability to identify comorbidities, perform cardiac testing, review laboratory results and current medications. No-shows also result in a waste of clinical resources and impacts the schedules of Advance Practice Providers (APPs). The purpose of this quality improvement project was to decrease the number of missed appointments in the outpatient preanesthesia and surgical screening clinic and the impact on provider satisfaction. DesignThe design of this quality improvement project was pre and post design. Automated and live phone calls reminders were provided for patients scheduled in the outpatient preanesthesia. Data was collected to compare missed appointment rates from a 3-month period prior to the project implementation and a 3-month period afterwards. MethodsPre-data collection included the number of no-shows in the EHR system from the previous 3 months. Participants included all adult patients who are scheduled for a pre-anesthesia surgical screening appointment. Provider satisfaction was assessed using a 5-question survey, pre and post innovation. FindingsReminder systems had a statistically significant impact on reducing the number of no-shows in the preanesthesia and surgical screening clinic (PASS). No significant impact was shown in provider satisfaction. ConclusionImplementation of a reminder system can help to reduce no show rates in clinics. Patient no-shows overload the health system by reducing the productivity of providers and waste resources including use of clinic staff, longer wait times for other patients, and the timing providers put into chart preparation.

Psychosocial readiness assessment for heart transplant candidates

BackgroundPsychosocial evaluation for transplant suitability is required by the Centers for Medicare and Medicaid Services (CMS) as a condition of participation for transplant programs. There are no regulations regarding follow-up reassessment for transplant readiness after waitlisting. ObjectivesAn evidence-based pilot project was developed and implemented to evaluate the feasibility of psychosocial readiness assessments for waitlisted heart transplantation candidates. The primary aim was to test the feasibility of these assessments in practice from a patient and programmatic perspective. MethodsDuring a 12-week period, waitlisted outpatients underwent one assessment each. Socioeconomic elements of caregiver support, housing, transportation, and insurance coverage status were assessed by simple using “yes/no” questions. To assess mental health needs, the General Anxiety Disorder-7 questionnaire (GAD-7) and the Patient Health Questionnaire-8 (PHQ-8) tools were utilized. Rescheduled readiness visits and no-show rates were measured. A post-implementation Qualtrics survey was administered to measure team member perceptions of feasibility. ResultsA total of 57 patients were assessed during the 12-week period. The primary aim of feasibility was achieved with 93 % of visits performed with freedom from rescheduling or patient no-show to the visit. Additionally, 75 % of team members reported the readiness assessments were feasible to complete in practice. ConclusionsAddressing the non-medical and mental health needs of waitlisted heart transplant patients allows transplant programs to maintain candidates with necessary resources and care. The readiness assessments are feasible in practice and may serve to reduce untoward outcomes in the post-transplant phase by providing targeted care prior to the time of transplant.

Pattern of Virtual Consultations in the Kingdom of Saudi Arabia: An Epidemiological Nationwide Study

BackgroundIn the Kingdom of Saudi Arabia (KSA), little is known about the adoption of virtual consultations (VCs), with most studies being survey-based leading to varying results. This study aims to utilise secondary collected data on the use of both kinds of VCs currently available, and to epidemiologically describe the adoption of these consultations.MethodsThis retrospective study analysed data provided by the Ministry of Health between January 1st 2021 and June 30th 2022. For both the home-based and the hospital-based consultations, variables included the age and sex of patients, date of consultation, duration in minutes, closure status for the appointment and the governorate in which the patient is residing. A heat map was drawn to present patterns of utilisation across the country.ResultsThe total number of VCs for both types were 1,008,228. For both types, females were higher adopters (54.73%). Of the total number of consultations, 751,156 were hospital-based. Of these consultations, family medicine consultations were the most common (20.42%), followed by internal medicine. Maternity follow-up clinics were higher in home-based clinics. The proportion of patient no-shows was high overall (48.30%). Utilisation was high in urban governorates, and low in rural ones.ConclusionFindings have several implications on health policy. It provides further evidence of the importance of family medicine, where it was the most common speciality even in hospital-based settings. The high variability in the adoption of consultations across rural and urban areas as well as the extremely high number of patient-no-shows warrants further investigation.

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

BackgroundNo-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.MethodsIn this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%.ResultsFrom the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets.ConclusionThis is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

Social Determinants of Health Impacting the Experience of Young Adults With Cancer at a Single Community Urban Hospital: A Retrospective Cohort Study.

Adolescent and young adult (AYA) cancer patients receive palliative medicine consultation at a late stage and face diagnostic delays. Failure to address social determinants of health (SDOH) and AYA-specific needs can adversely impact patient experience. This retrospective observational cohort study used data from chart review to assess the frequency of SDOH impacting AYA patients and setting of initial diagnosis at a US urban safety-net hospital. The association of SDOH variables with delays in treatment, loss of follow-up, and no-shows was tested using Chi-square and t-tests. One hundred seventy five patient charts were reviewed. Sixty-two percent were diagnosed in acute care settings. Substance use disorders, financial, employment, and insurance issues were associated with delayed treatment, with weak to moderate effect sizes. Mental health diagnoses, substance use disorder, homelessness, and financial burdens were associated with patient no-shows, with moderate to large effect sizes. Twenty-five percent of patients received palliative medicine consultation; 70% of these occurred at end of life. This study demonstrates the impact of SDOH on AYA cancer care and the need for policy allowing for intervention on SDOH.

A multi-appointment patient scheduling system with machine learning and optimization

Appointment scheduling is critical to increasing resource utilization and operational performance in various industry domains, especially healthcare. Costs to care for several serious diseases are projected to grow due to the aging population and rising drug prices. Thus, there is an urgent need for efficient operational planning and scheduling to reduce expenses. This research explores ways to effectively schedule outpatient chemotherapy visits with multiple appointments requiring different resources. The study aims to assess the impact of patient no-shows and individual stochastic appointment durations in scheduling performance and determine if overbooking is viable to mitigate the adverse effects of patient no-shows. The study first applies artificial neural networks (ANN) to calculate patient no-show probabilities and individualized appointment durations. Then, it builds several optimization models that use the ANN models’ outcomes to schedule outpatient chemotherapy visits. The performance of patient schedules obtained from these optimization models is assessed using simulation analysis to identify the effectiveness of overbooking to combat patient no-shows and determine if individual stochastic appointment durations produce better key performance indicators.

Exploring the appointment factors affecting pediatric patients with swallow disorders: Implications for speech and language pathology attendance

IntroductionFeeding and swallowing disorders have become increasingly prevalent among children, necessitating effective management to prevent long-term complications. Speech and language pathology (SLP) services play a crucial role in diagnosing and treating these disorders. The objective of this study was to explore the factors that influence patient attendance to SLP appointments for swallow disorders. MethodsThis study was conducted at Boston Medical Center, involving 359 pediatric patients referred to SLP for swallow-related concerns. De-identified patient and appointment information was obtained from the electronic medical record. Various factors such as age, gender, race/ethnicity, primary language, appointment date/time, and COVID-19 lockdown status were analyzed to determine their impact on patient no-shows. Statistical analyses, including Chi-Square tests and binary logistic regression, were conducted using appropriate methodologies. Results355 individual patient records were included in the analysis. Lockdown status and appointment time of day did not significantly affect patient no-shows. However, appointments conducted through telemedicine showed a significant difference in attendance. Patient referral department, gender, race, language, and being born at the medical center did not significantly influence patient attendance. Notably, having a primary care provider (PCP) at the medical center significantly affected patient attendance. Furthermore, previous appointment cancellations made a patient more likely to no-show. ConclusionThis study provides valuable insights into the factors influencing patient attendance at SLP appointments for pediatric swallowing disorders. Having a PCP at the medical center and utilizing telemedicine appointments were associated with higher attendance rates. Addressing appointment cancellations and investigating underlying reasons behind missed appointments should be prioritized in future research. Understanding these factors will facilitate the development of interventions to optimize patient attendance and improve the delivery of SLP services in pediatric populations.

Behavioural economic interventions to reduce health care appointment non-attendance: a systematic review and meta-analysis

BackgroundAppointment non-attendance – often referred to as “missed appointments”, “patient no-show”, or “did not attend (DNA)” – causes volatility in health systems around the world. Of the different approaches that can be adopted to reduce patient non-attendance, behavioural economics-oriented mechanisms (i.e., psychological, cognitive, emotional, and social factors that may impact individual decisions) are reasoned to be better suited in such contexts – where the need is to persuade, nudge, and/ or incentivize patients to honour their scheduled appointment. The aim of this systematic literature review is to identify and summarize the published evidence on the use and effectiveness of behavioural economic interventions to reduce no-shows for health care appointments.MethodsWe systematically searched four databases (PubMed/Medline, Embase, Scopus, and Web of Science) for published and grey literature on behavioural economic strategies to reduce no-shows for health care appointments. Eligible studies met four criteria for inclusion; they were (1) available in English, Spanish, or French, (2) assessed behavioural economics interventions, (3) objectively measured a behavioural outcome (as opposed to attitudes or preferences), and (4) used a randomized and controlled or quasi-experimental study design.ResultsOur initial search of the five databases identified 1,225 articles. After screening studies for inclusion criteria and assessing risk of bias, 61 studies were included in our final analysis. Data was extracted using a predefined 19-item extraction matrix. All studies assessed ambulatory or outpatient care services, although a variety of hospital departments or appointment types. The most common behaviour change intervention assessed was the use of reminders (n = 56). Results were mixed regarding the most effective methods of delivering reminders. There is significant evidence supporting the effectiveness of reminders (either by SMS, telephone, or mail) across various settings. However, there is a lack of evidence regarding alternative interventions and efforts to address other heuristics, leaving a majority of behavioural economic approaches unused and unassessed.ConclusionThe studies in our review reflect a lack of diversity in intervention approaches but point to the effectiveness of reminder systems in reducing no-show rates across a variety of medical departments. We recommend future studies to test alternative behavioural economic interventions that have not been used, tested, and/or published before.

Effect of Standardized Reminder Calls on Trauma Patient No-Show Rate.

Most patients who sustain a traumatic injury require outpatient follow-up. A common barrier to outpatient postadmission care is patient failure to follow-up. One of the most significant factors resulting in failure to follow-up is age more than 35 years. Recent work has shown that follow-up telephone calls reduce readmission rates. Our aim was to decrease no-show appointments by 10% in 12 months. The electronic medical records at our level I and II trauma centers were queried for all outpatient appointments for trauma between July 1, 2020, and June 9, 2021, and whether the patient attended their follow-up appointment. Patients with visits scheduled after August 1, 2021, received 24- and 48-hour previsit reminder calls. Patients with visits scheduled between July 1, 2020, and August 1, 2021, did not receive previsit calls. Both groups were compared using multivariable direct logistic regression models. A total of 1,822 follow-up opportunities were included in the study. During the pre-implementation phase, there was a no-show rate of 30.9% (329 of 1,064 visits). Postintervention, a 12.2% reduction in overall no-show rate occurred. A statistically significant 11.2% decrease (p < 0.001) was seen in elderly patients. Multivariate analysis showed standardized calls resulted in significantly decreased odds of failing to keep an appointment (adjusted odds ratio = 0.610, p < 0.001). Reminder calls led to a 12.2% reduction in no-show rate and were an independent predictor of a patient's likelihood of attending their appointment. Other predictors of attendance included insurance status and abdominal injury.

Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system

BackgroundNo-show appointments pose a significant challenge for healthcare providers, particularly in rural areas. In this study, we developed an evidence-based predictive model for patient no-shows at the Marshfield Clinic Health System (MCHS) rural provider network in Wisconsin, with the aim of improving overbooking approaches in outpatient settings and reducing the negative impact of no-shows in our underserved rural patient populations.MethodsRetrospective data (2021) were obtained from the MCHS scheduling system, which included 1,260,083 total appointments from 263,464 patients, as well as their demographic, appointment, and insurance information. We used descriptive statistics to associate variables with show or no-show status, logistic regression, and random forests utilized, and eXtreme Gradient Boosting (XGBoost) was chosen to develop the final model, determine cut-offs, and evaluate performance. We also used the model to predict future no-shows for appointments from 2022 and onwards.ResultsThe no-show rate was 6.0% in both the train and test datasets. The train and test datasets both yielded 5.98. Appointments scheduled further in advance (> 60 days of lead time) had a higher (7.7%) no-show rate. Appointments for patients aged 21–30 had the highest no-show rate (11.8%), and those for patients over 60 years of age had the lowest (2.9%). The model predictions yielded an Area Under Curve (AUC) of 0.84 for the train set and 0.83 for the test set. With the cut-off set to 0.4, the sensitivity was 0.71 and the positive predictive value was 0.18. Model results were used to recommend 1 overbook for every 6 at-risk appointments per provider per day.ConclusionsOur findings demonstrate the feasibility of developing a predictive model based on administrative data from a predominantly rural healthcare system. Our new model distinguished between show and no-show appointments with high performance, and 1 overbook was advised for every 6 at-risk appointments. This data-driven approach to mitigating the impact of no-shows increases treatment availability in rural areas by overbooking appointment slots on days with an elevated risk of no-shows.

An Electronic Health Record-Based Automated Self-Rescheduling Tool to Improve Patient Access: Retrospective Cohort Study.

In many large health centers, patients face long appointment wait times and difficulties accessing care. Last-minute cancellations and patient no-shows leave unfilled slots in a clinician's schedule, exacerbating delays in care from poor access. The mismatch between the supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations. We evaluated an electronic health record (EHR)-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled. In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer. From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million. Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.

To charge or not to charge: reducing patient no-show

BackgroundIn order to reduce patient no-show, the Israeli government is promoting legislation that will allow Health Plans to require a co-payment from patients when reserving an appointment. It is hoped that this will create an incentive for patients to cancel in advance rather than simply not show up. The goal of this policy is to improve patient access to medical care and ensure that healthcare resources are utilized effectively. We explore this phenomenon to support evidence-based decision making on this issue, and to determine whether the proposed legislation is aligned with the findings of previous studies.Main bodyNo-show rates vary across countries and healthcare services, with several strategies in place to mitigate the phenomenon. There are three key stakeholders involved: (1) patients, (2) medical staff, and (3) insurers/managed care organizations, each of which is affected differently by no-shows and faces a different set of incentives. The decision whether to impose financial penalties for no-shows should take a number of considerations into account, such as the fine amount, service type, the establishment of an effective fine collection system, the patient’s socioeconomic status, and the potential for exacerbating disparities in healthcare access. The limited research on the impact of fines on no-show rates has produced mixed results. Further investigation is necessary to understand the influence of fine amounts on no-show rates across various healthcare services. Additionally, it is important to evaluate the implications of this proposed legislation on patient behavior, access to healthcare, and potential disparities in access.ConclusionIt is anticipated that the proposed legislation will have minimal impact on attendance rates. To achieve meaningful change, efforts should focus on enhancing medical service availability and improving the ease with which appointments can be cancelled or alternatively substantial fines should be imposed. Further research is imperative for determining the most effective way to address the issue of patient no-show and to enhance healthcare system efficiency.

Managing interruptions in appointment schedules via patient notification

Interruptions in appointment schedules are common in practice, yet many clinics use generic appointment scheduling templates and sub-optimal interruption management strategies that fail to address dynamics aspects of daily healthcare operations. In this study, we consider patient no-shows, patient punctuality, service time variability, random walk-in patient arrivals and other common appointment interruptions to determine optimal single-server, intra-day appointment schedules. Using a modified depth first search (DFS) algorithm that relies on a sample average approximation (SAA) technique for objective function evaluation, we minimize a weighted sum of expected waiting times for scheduled and walk-in patients, physician idle time, and physician overtime. Extensive experimentation shows the proposed algorithm solves realistic problems in reasonable time. Furthermore, we propose a sequential notification procedure (SNP) that serves as a dynamic correcting mechanism to address infrequent and unexpected interruptions with a relatively long duration. Considering possible patient responses to notification, SNP notifies scheduled patients in advance to prevent excessive patient waiting and physician overtime. Furthermore, we propose an easy-to-use patient notification heuristic (NH) that can be adopted by clinics using traditional appointment systems. Experimentation shows that both SNP and NH provide significant value, improving operational outcomes. We demonstrate the value of patient notification increases as interruption levels increase and when patients are responsive to notifications.

Intraday dynamic rescheduling under patient no-shows.

Patient no-shows are a major source of uncertainty for outpatient clinics. A common approach to hedge against the effect of no-shows is to overbook. The trade-off between patient's waiting costs and provider idling/overtime costs determines the optimal level of overbooking. Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intraday dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that allows us to apply a shortest path algorithm to solve for the optimal policy more efficiently. Based on a numerical study using parameter estimates from existing literature, we find that intraday dynamic rescheduling can reduce expected cost by 15% compared to static scheduling.