Patient No-shows Research Articles

Factors Contributing to Non-Attendance of GI Endoscopic Procedures in a Tertiary Care Center in the Middle East.

Introduction:The cancer burden in the Middle East is high and growing. Colorectal cancer (CRC) is the second most common cancer for both men and women in the UAE. Although early diagnosis of malignancy reduces morbidity and increases the survival rates, non-attendance of gastroenterology (GI) endoscopic procedures is a significant global problem, which can lead to delay in cancer diagnosis and treatment. Several factors have been found to contribute to non-attendance behavior, including socioeconomic, cultural, and organizational related barriers. The purpose of this study was to identify factors contributing to non-attendance behavior among outpatients scheduled for GI endoscopic procedures in a tertiary hospital in the United Arab Emirates. We conclude with recommendations that can help in reducing the rate of patient no-shows for GI endoscopic procedures in the region. Methods:In a tertiary medical center in the Middle East, we surveyed patients who did not attend their scheduled GI endoscopic procedures over a period of one year. The questionnaire sought to identify possible reasons for patient’s non-attendance. Descriptive measures including means, standard deviation, frequencies, and percentages were used to analyze the demographic characteristics of the study participants. The chi-square test was performed to analyze gender differences. Results:Of 314 outpatients who met study inclusion criteria, 168 agreed to participate (53.5% response rate). The majority of participants were women (n=96, 60.4 %), aged 18 to 73, with a mean of 42 years. The largest age group was between 35 and 44 (n=46, 28.9 %). Approximately equal numbers of non-attendance appointments were scheduled for combined colonoscopy and upper endoscopy (36.3 %), colonoscopy alone (31.3 %), or upper endoscopy alone (31.3 %). The most common causes for cancellation or non-attendance included concerns about the appointment (35.5%), inconvenient timing of the appointment (27.9%) and changes in medical status (26.4%). Gender differences were noted for non-attendance behaviors, with women significantly more likely than men to report feelings of embarrassment (Chi-square 6.261, df=1, p=.012). Conclusion:Our study has identified several barriers to patient attendance of endoscopic procedures, as well as opportunities to reduce the rate of patient no-shows, including patient education, scheduling options, and protocols to minimize discomfort and misconceptions around GI endoscopic procedures, particularly accommodating for same gender endoscopists, with the ultimate goal of increasing early cancer screening and prevention.

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 ​= ​286,503), and a validation set (N2 ​= ​95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N ​= ​42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations.

Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions.

ObjectivesChronic pain affects 50 million Americans and is often treated with non-pharmacologic approaches like physical therapy. Developing a no-show prediction model for individuals seeking physical therapy care for musculoskeletal conditions has several benefits including enhancement of workforce efficiency without growing the existing provider pool, delivering guideline adherent care, and identifying those that may benefit from telehealth. The objective of this paper was to quantify the national prevalence of no-shows for patients seeking physical therapy care and to identify individual and organizational factors predicting whether a patient will be a no-show when seeking physical therapy care.DesignRetrospective cohort study.SettingCommercial provider of physical therapy within the United States with 828 clinics across 26 states.ParticipantsAdolescent and adult patients (age cutoffs: 14–117 years) seeking non-pharmacological treatment for musculoskeletal conditions from January 1, 2016, to December 31, 2017 (n = 542,685). Exclusion criteria were a primary complaint not considered an MSK condition or improbable values for height, weight, or body mass index values. The study included 444,995 individuals.Primary and secondary outcome measuresPrevalence of no-shows for musculoskeletal conditions and predictors of patient no-show.ResultsIn our population, 73% missed at least 1 appointment for a given physical therapy care episode. Our model had moderate discrimination for no-shows (c-statistic:0.72, all appointments; 0.73, first 7 appointments) and was well calibrated, with predicted and observed no-shows in good agreement. Variables predicting higher no-show rates included insurance type; smoking-status; higher BMI; and more prior cancellations, time between visit and scheduling date, and between current and previous visit.ConclusionsThe high prevalence of no-shows when seeking care for musculoskeletal conditions from physical therapists highlights an inefficiency that, unaddressed, could limit delivery of guideline-adherent care that advocates for earlier use of non-pharmacological treatments for musculoskeletal conditions and result in missed opportunities for using telehealth to deliver physical therapy.

A Root Cause Analysis Using Text Mining for Patient No-Shows: A Case Study of Primary Care Centers Serving Rural Areas

Patient no-shows lead to a significant loss in the healthcare industry. Many researchers have focused on this problem in various healthcare settings using several methods. However, few studies used text mining to conduct a root cause analysis (RCA) and a Pareto analysis related to patient no-shows at outpatient primary care settings serving rural areas. This study uses a text-mining framework to investigate and analyze the factors and causes behind patient no-shows at Finger Lakes Community Health (FLCH), an outpatient primary care medical center serving rural areas in New York. The dataset used in this study was built from 5,400 attempted telephone interviews with patients who did not show up for their appointments. The text-mining framework was used to explore, analyze, and categorize information in the notes taken by interviewers. The novel framework uses lookup words that are weighted based on their order of appearance in a note to assign that note to a certain root cause. It was found that miscommunication, personal issues, and forgetfulness are key causes of patient no-shows. Also, it was found that almost 80% of the patient no-shows were caused by only five root causes. Based on this analysis, several interventions were proposed to reduce the incidence of patient no-shows.

Capacity management of CT department with service time differences and emergency nonpreemptive priority

In the presence of different service times of regular patients and nonpreemptive priority of emergency patients at the same day, this paper considers an advance appointment-scheduling problem of medical resources in which a finite amount of same-day service capacity should be allocated to different patients. This booking problem is modeled as a dynamic programming (DP) problem. The structural properties of DP model analytically reveal a booking limit policy. The booking limit numbers of regular patients do not monotonically increase with their revenues because of the service time differences. A numerical analysis is provided to test the performance of our booking limit policy by comparing polices in previous studies. Simulation results show that our policy provides a positive improvement in terms of the total reward of the service system, even when patients do not have perfect adherence to appointments (e.g., patient no-shows and unpunctuality). The numerical experiments demonstrate that the dedicated medical resource cannot improve emergency patients’ access to service but deteriorated their direct waiting time.

Optimising the booking horizon in healthcare clinics considering no-shows and cancellations

Patient no-shows and cancellations are a significant problem to healthcare clinics, as they compromise a clinic's efficiency. Therefore, it is important to account for both no-shows and cancellations into the design of appointment systems. To provide additional empirical evidence on no-show and cancellation behaviour, we assess outpatient clinic data from two healthcare providers in the USA and EU: no-show and cancellation rates increase with the scheduling interval, which is the number of days from the appointment creation to the date the appointment is scheduled for. We show the temporal cancellation behaviour for multiple scheduling intervals is bimodally distributed. To improve the efficiency of clinics at a tactical level of control, we determine the optimal booking horizon such that the impact of no-shows and cancellations through high scheduling intervals is minimised, against a cost of rejecting patients. Where the majority of the literature only includes a fixed no-show rate, we include both a cancellation rate and a time-dependent no-show rate. We propose an analytical queuing model with balking and reneging, to determine the optimal booking horizon. Simulation experiments show that the assumptions of this model are viable. Computational results demonstrate general applicability of our model by case studies of two hospitals.

Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment.

No-show appointments waste resources and decrease the sustainability of care. This study is an attempt to evaluate patient no-shows based on modes of appointment-making and types of appointments. We collected hospital information system data and appointment data including characteristics of patients, service providers, and clinical visits over a three-month period (1 September 2018 to 30 November 2018), at a large tertiary hospital in Seoul, Korea. We used multivariate logistic regression analyses to identify the factors associated with no-shows (Model 1). We further assessed no-shows by including the interaction term (“modes of appointment-making” X “type of appointment”) (Model 2). Among 1,252,127 appointments, the no-show rate was 6.12%. Among the modes of appointment-making, follow-up and online/telephone appointment were associated with higher odds of no-show compared to walk-in. Appointments for treatment and surgery had higher odds ratios of no-show compared to consultations. Tests for the interaction between the modes of appointment-making and type of appointment showed that follow-up for examination and online/telephone appointments for treatment and surgery had much higher odds ratios of no-shows. Other significant factors of no-shows include age, type of insurance, time of visit, lead time (time between scheduling and the appointment), type of visits, doctor’s position, and major diagnosis. Our results suggest that future approaches for predicting and addressing no-show should also consider and analyze the impact of modes of appointment-making and type of appointment on the model of prediction.

Constant Job-Allowance Policies for Appointment Scheduling: Asymptotic Optimality and Numerical Analysis

We consider the appointment scheduling problem, which determines the job allowance over the planning horizon. In particular, we study a simple but effective scheduling policy -- the so-called plateau policy, which allocates a constant job allowance for each appointment. Prior studies on appointment scheduling suggests a dome shape structure for the optimal job allowance over the planning horizon. This implies that job allowance does not vary significantly in the middle of the schedule sequence, but varies at the beginning and also at the end of the optimal schedule. Using a dynamic programming formulation, we derive an explicit performance gap between the plateau policy and the optimal schedule, and examine how this gap behaves as the number of appointments increases. We show that a plateau policy is asymptotically optimal when the number of appointments increases. We extend this result to a more general setting with multiple service types. Numerical experiments show that the plateau policy is near optimal even for a small number of appointments, which complements the theoretical results that we derived. Our result provides a justification and strong support for the plateau policy, which is commonly used in practice. Moreover, with minor modifications, the plateau policy can be adapted to more general scenarios with patient no-shows or heterogeneous appointment types.

Appointment Capacity Planning With Overbooking for Outpatient Clinics With Patient No-Shows

Outpatient clinics typically face a critical issue in patient adherence to their clinic appointments and suffer a huge backlog of patients waiting to be seen. The goal of this study is to enhance care provider productivity and patient access through effective clinic appointment scheduling. To achieve this goal, we introduce discrete-time bulk-service queues to model the backlog dynamics accounting for the no-show behavior of patients and consider different overbooking strategies to reduce backlogs at a minimum risk of working overtime. Numerically stable solution procedures and approximation methods are introduced to significantly reduce the computational burden, making the queueing models a practical tool for outpatient appointment template design. The insights obtained from this work will support capacity planning decision-making and are critical to improving the operational efficiency of outpatient clinics.

Patient-Controlled use of Non-Physician Providers: Appointment Scheduling in Mixed-Provider Settings

The aging population and increasing chronic disease load are rapidly changing the face of primary care delivery, with mid-level (e.g., nurse) practitioners providing growing proportion of patient care. Potential differences in the quality of care offered by physicians and nurse practitioners may affect patient preferences, thus leading to patient choice behavior. This paper focuses on the problem of appointment scheduling for the physician--nurse teams in the presence of patient choice and no-shows. We propose a novel model that accounts for decision-making by patients in a system with two provider types. We prove that the presence of patient choice, in general, breaks down the multimodularity of a medical practice's cost function, creating challenges for the numerical identification of optimal appointment schedules. At the same time, we derive sufficient conditions under which the multimodularity is restored. To counter the computational challenges arising in the general setting, we propose an easy-to-implement heuristic and prove its optimality in the absence of patient no-shows. Our numerical study shows how the ratio of qualities of care delivered by nurses and physicians affects the profitability of the medical practice, enabling the analysis of the trade-offs involved in hiring a nurse practitioner.

Socioeconomic factors that impact patient no-shows in the ambulatory urology clinic

This study seeks to identify patient socioeconomic factors that may be associated with no-shows to our academic urology clinics. This was a retrospective analysis of patient attendance to appointments scheduled at the main urology clinics of our academic center between April 2018 and April 2019. Attendance to scheduled appointments was computed and analyzed with respect to patients’ demographics and socioeconomic status, along with clinic site, type of provider scheduled with, and reason for visit. Chi-square test and simple and multiple logistic regression were performed in order to determine the relationship between patient no-show rates and the variables for appointment characteristics. The cohort of patients at highest risk for no-show was associated with those who were uninsured (OR = 30.00, 95% CI: 23.73-–7.92; p < 0.0001) and from our county’s urban sector (OR = 1.96, 95% CI:1.63–2.37; p < 0.0001), in which the population reported a household income of 30% below poverty level and was only one-third high-school educated. Further studies of this at-risk population who are likeliest to miss their urology clinic appointment may appropriately address these patients’ needs and thus curtail the number of no-shows to future outpatient appointments.

P406 SURVEY EXPLORING BARRIERS TO ALLERGY CLINIC VISITS IN AN URBAN PEDIATRIC SUBSPECIALTY CENTER

Patient no-show is a widespread problem which compromises patient well-being and results in loss of revenue and lost opportunities for learners in academic practice. Our goal was to identify patient/parent perspectives on barriers to clinic visits.

Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: A data analytics approach

BackgroundThe observed consultation length at specialty clinics, such as cardiology care, is represented by two underlying groups - one with zero service time due to patient no-shows, and the other characterized by positive values with high variance. This inconstancy affects the scheduler’s ability to accurately estimate consultation length, which, in turn, hinders effective utilization of the clinic’s resources and timely access to care. The objectives of this study were to: (i) predict the consultation length by accounting for its semicontinuous nature (i.e., zero in case of no-shows and positive otherwise), using machine learning (ML) algorithms, (ii) identify important features for predicting no-shows and non-zero consultation length, and (iii) assess the impact of integrating the ML-based prediction with the appointment scheduling system. MethodsWe used two-years of data extracted from the electronic medical records of a cardiology clinic. By leveraging 16 predictors pertaining to the patient, appointment, and doctor, a two-part ML-based approach was developed to handle the semicontinuous consultation length. Supervised classification models were employed to predict no-shows (i.e., categorize the consultation length as zero or positive), and regression algorithms were developed for estimating non-zero consultation lengths. Three algorithms, namely, random forests, stochastic gradient boosting, and deep neural networks, were individually employed for both no-show classification and positive consultation length prediction. Finally, the best performing classification and regression models were combined to establish the complete two-part model, and its prediction error on new data is benchmarked against the clinic’s current performance. The evaluation metrics for classification models were area under the receiver operating characteristic curve (AUC-ROC) and area under the precision-recall curve (AUC-PR). The prediction performance of regression algorithms was evaluated by mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). A simulation modeling approach was adopted to ascertain the effectiveness of using ML-based prediction for scheduling decisions as opposed to the clinic’s current strategy. ResultsAmong the classification models tested, stochastic gradient boosted classification tree (SGBCT) demonstrated best performance (AUC-ROC = 0.85, AUC-PR = 0.64). For positive consultation length prediction, deep neural network regressor (DNNR) resulted in lowest prediction error (MAE = 8.55, RMSE = 6.88, MAPE = 12.24). The complete two-part model (SGBCT + DNNR) outperformed the clinic’s approach to consultation length estimation by achieving 50 % and 52 % reduction in RMSE and MAE, respectively. Further adopting it for appointment scheduling could reduce the patient waiting time and doctor idle time by 56 % and 52 %, respectively. Besides, several clinical insights, along with critical features for no-show and consultation length prediction, were also identified from our proof-of-concept study. ConclusionThis study demonstrates that routine clinical tasks such as estimation of consultation length and no-shows can be accurately predicted using ML algorithms, and subsequently integrated into the clinical scheduling system to improve resource utilization and reduce patient waiting time.

Factors Associated with Increased Risk of Patient No-Show in Telehealth and Traditional Surgery Clinics

BackgroundWith the growing use of telehealth, understanding factors affecting patient follow-up in traditional and telehealth settings is important. Few data exist examining the use of telehealth compared with traditional settings. Bridging this gap is critical to optimizing telehealth use and reducing barriers.Study DesignThis is a retrospective cohort study of return and postoperative (electronic video [eClinic] and traditional) visits from January 2018 to March 2020 at single tertiary care center. There were 12,359 unique first-encounter patients with 903 eClinic and 11,456 traditional visits; 11,547 patients completed visits, while 812 patients did not show up. Multivariable logistic regression modeling was performed to identify factors associated with no-show. County-level mapping was used to identify patterns in no-show rates.ResultsPatients from the eClinic had twice the odds of no-show compared with those from a traditional clinic (p < 0.001). Age was inversely proportional to odds of no-show, with each additional decade associated with a 16% decrease in these odds (p < 0.001). African-American patients had greater odds of no-show compared to Caucasian patients (odds ratio [OR] 2.47; 95% CI 1.95–3.13, p < 0.001). Marital statuses of single and legal separation were associated with higher odds of no-show compared with married marital status (p < 0.001 and p = 0.04, respectively). Minimally invasive and endocrine surgery clinics had lower odds of no-show compared with acute care surgery clinic (p < 0.001 for both). County-level no-show rates demonstrate similar patterns between clinic settings.ConclusionsSeveral factors are associated with increased odds of no-show, including the visit being in eClinic. County-level analysis suggests no-show variation is not dependent on geographic location. Understanding these patterns allows for prospective identification of barriers and development of interventions to optimize access and patient care.

A Probabilistic Patient Scheduling Model with Time Variable Slots.

One of the current challenges faced by health centers is to reduce the number of patients who do not attend their appointments. The existence of these patients causes the underutilization of the center's services, which reduces their income and extends patient's access time. In order to reduce these negative effects, several appointment scheduling systems have been developed. With the recent availability of electronic health records, patient scheduling systems that incorporate the patient's no-show prediction are being developed. However, the benefits of including a personalized individual variable time slot for each patient in those probabilistic systems have not been yet analyzed. In this article, we propose a scheduling system based on patients' no-show probabilities with variable time slots and a dynamic priority allocation scheme. The system is based on the solution of a mixed-integer programming model that aims at maximizing the expected profits of the clinic, accounting for first and follow-up visits. We validate our findings by performing an extensive simulation study based on real data and specific scheduling requirements provided by a Spanish hospital. The results suggest potential benefits with the implementation of the proposed allocation system with variable slot times. In particular, the proposed model increases the annual cumulated profit in more than 50% while decreasing the waiting list and waiting times by 30% and 50%, respectively, with respect to the actual appointment scheduling system.

Modeling Patient No-Show History and Predicting Future Appointment Behavior at the Veterans Administration's Outpatient Mental Health Clinics: NIRMO-2.

No-shows are detrimental to both patients' health and health care systems. Literature documents no-show rates ranging from 10% in primary care clinics to over 60% in mental health clinics. Our model predicts the probability that a mental health clinic outpatient appointment will not be completed and identifies actionable variables associated with lowering the probability of no-show. We were granted access to de-identified administrative data from the Veterans Administration Corporate Data Warehouse related to appointments at 13 Veterans Administration Medical Centers. Our modeling data set included 1,206,271 unique appointment records scheduled to occur between January 1, 2013 and February 28, 2017. The training set included 846,668 appointment records scheduled between January 1, 2013 and December 31, 2015. The testing set included 359,603 appointment records scheduled between January 1, 2016 and February 28, 2017. The dependent binary variable was whether the appointment was completed or not. Independent variables were categorized into seven clusters: patient's demographics, appointment characteristics, patient's attendance history, alcohol use screening score, medications and medication possession ratios, prior diagnoses, and past utilization of Veterans Health Administration services. We used a forward stepwise selection, based on the likelihood ratio, to choose the variables in the model. The predictive model was built using the SAS HPLOGISTIC procedure. The best indicator of whether someone will miss an appointment is their historical attendance behavior. The top three variables associated with higher probabilities of a no-show were: the no-show rate over the previous 2 years before the current appointment, the no-show probability derived from the Markov model, and the age of the appointment. The top three variables that decrease the chance of no-showing were: the appointment was a new consult, the appointment was an overbook, and the patient had multiple appointments on the same day. The average of the areas under the receiver operating characteristic curves was 0.7577 for the training dataset, and 0.7513 for the test set. The National Initiative to Reduce Missed Opportunities-2 confirmed findings that previous patient attendance is one of the key predictors of a future attendance and provides an additional layer of complexity for analyzing the effect of a patient's past behavior on future attendance. The National Initiative to Reduce Missed Opportunities-2 establishes that appointment attendance is related to medication adherence, particularly for medications used for treatment of mood disorders or to block the effects of opioids. However, there is no way to confirm whether a patient is actually taking medications as prescribed. Thus, a low medication possession ratio is an informative, albeit not a perfect, measure. It is our intention to further explore how diagnosis and medications can be better captured and used in predictive modeling of no-shows. Our findings on the effects of different factors on no-show rates can be used to predict individual no-show probabilities, and to identify patients who are high risk for missing appointments. The ability to predict a patient's risk of missing an appointment would allow for both advanced interventions to decrease no-shows and for more efficient scheduling.

Patient No-Show Prediction: A Systematic Literature Review.

Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.

Evaluating the Impact of Patient No-Shows on Service Quality.

PurposePatient 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.MethodsIn 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.ResultsOur results show that the no-shows are high due to multiple factors, such as patient behavior, patients’ financial situation, environmental factors and scheduling policy.ConclusionIn 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.

Influence of the pandemic dissemination of COVID-19 on radiotherapy practice: A flash survey in Germany, Austria and Switzerland.

BackgroundThe COVID-19 pandemic has already changed our globalised world and its long-term impact is not yet known. It is apparent that businesses and institutions are increasingly affected. COVID-19 discussions often focus on intensive care units in hospitals. However, COVID-19 also effects life-saving and -prolonging radiotherapy for patients suffering from cancer.MethodWe have conducted a structured online survey among medical physicists in Germany, Austria and Switzerland from March 23rd to 26th 2020. In total 154 responses (82 completed, 72 partially completed) were analysed in the context of the COVID-19 dissemination.Results72.4% of the respondent’s state that their processes are affected due to COVID-19, while the top three answers are longer processing times (54.2%), patient no-shows (42.5%) and staff reduction (36.7%). 75.8% expect further unavailability of their personnel in the upcoming weeks. All participants have already taken several measures, especially providing information for patients at the entrance (89.6%) or over the phone (73.6%), restricting access for accompanying persons (77.4%) and providing disinfectant at the entrance (72.6%).DiscussionThe results presented in this article aim to support business continuity and risk management for radiotherapy centres to prepare for future challenges. The results show that most radiotherapy centres has implemented initial contingency measures, applying them pragmatically. The main problem however remains, that is the high risk of infection both for patients and medical personnel along with the associated risk of temporarily loss of personnel and ordered closure of business.

A service analytic approach to studying patient no-shows

Patients who fail to show up for an appointment are a major challenge to medical providers. Understanding no-shows and predicting them are keys to developing a proactive strategy in healthcare operations. In this study, we propose a data analytics framework to explore the underlying factors of no-shows via various machine learning models to predict whether a patient is a no-show. The analytics results reveal key patterns in no-show patients. We also propose a methodology to integrate the prediction model with a Bayesian inference system to create an overbooking decision support tool that allows variable overbooking rates in different time windows.