Effects of rescheduling on patient no-show behavior in outpatient clinics

This version includes the Appendices with the article.</p

Appointment reminder systems are effective but not optimal: results of a systematic review and evidence synthesis employing realist principles

Missed appointments are an avoidable cost and resource inefficiency which impact upon the health of the patient and treatment outcomes. Healthcare services are increasingly utilizing reminder systems to manage these negative effects. This study explores the effectiveness of reminder systems for promoting attendance, cancellations and rescheduling of appointments across all healthcare settings and for particular patient groups and the contextual factors which indicate that reminders are being employed sub-optimally. We used three inter-related reviews of quantitative and qualitative evidence. Firstly, using pre-existing models and theories, we developed a conceptual framework to inform our understanding of the Contexts and Mechanisms which influence reminder effectiveness. Secondly, we performed a review following Centre for Reviews & Dissemination (CRD) guidelines to investigate the effectiveness of different methods of reminding patients to attend health service appointments. Finally, to supplement the effectiveness information, we completed a review informed by realist principles to identify factors likely to influence non-attendance behaviors and the effectiveness of reminders. We found consistent evidence that all types of reminder systems are effective at improving appointment attendance across a range of health care settings and patient populations. Reminder systems may also increase cancellation and rescheduling of unwanted appointments. “Reminders plus”, which provide additional information beyond the reminder function, may be more effective than simple reminders at reducing non-attendance at appointments in particular circumstances. We identified six areas of inefficiency which indicate that reminder systems are being used sub-optimally. Unless otherwise indicated, all patients should receive a reminder to facilitate attendance at their healthcare appointment. The choice of reminder system should be tailored to the individual service. To optimize appointment and reminder systems, healthcare services need supportive administrative processes to enhance attendance, cancellation, rescheduling and re-allocation of appointments to other patients

Intra-day Dynamic Rescheduling under Patient No-shows

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 intra-day 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 can solve for the optimal policy more efficiently. Based on a numerical study, we estimate that using intra-day dynamic rescheduling can lead to a 5-7% decrease in expected cost when compared to static scheduling

Managing Waiting Times to Predict No-shows and Cancelations at a Children’s Hospital

Purpose: Since long waits in hospitals have been found to be related to high rates of no-shows and cancelations, managing waiting times should be considered as an important tool that hospitals can use to reduce missed appointments. The aim of this study is to analyze patients’ behavior in order to predict no-show and cancelation rates correlated to waiting times. Design/methodology/approach: This study is based on the data from a US children’s hospital, which includes all the appointments registered during one year of observation. We used the call-appointment interval to establish the wait time to get an appointment. Four different types of appointment-keeping behavior and two types of patients were distinguished: arrival, no-show, cancelation with no reschedule, and cancelation with reschedule; and new and established patients. Findings: Results confirmed a strong impact of long waiting times on patients’ appointment-keeping behavior, and the logarithmic regression was found as the best-fit function for the correlation between variables in all cases. The correlation analysis showed that new patients tend to miss appointments more often than established patients when the waiting time increases. It was also found that, depending on the patients’ appointment distribution, it might get more complicated for hospitals to reduce missed appointments as the waiting time is reduced. Originality/value: The methodology applied in our study, which combines the use of regression analysis and patients’ appointment distribution analysis, would help health care managers to understand the initial implications of long waiting times and to address improvement related to patient satisfaction and hospital performance.Peer Reviewe

Targeting the use of reminders and notifications for uptake by populations (TURNUP): a systematic review and evidence synthesis.

Background: Missed appointments are an avoidable cost and a resource inefficiency that impact on the health of the patient and treatment outcomes. Health-care services are increasingly utilising reminder systems to counter these negative effects. Objectives: This project explores the differential effect of reminder systems for different segments of the population for improving attendance, cancellation and rescheduling of appointments. Design: Three inter-related reviews of quantitative and qualitative evidence relating to theoretical explanations for appointment behaviour (review 1), the effectiveness of different approaches to reminding patients to attend health service appointments (review 2) and factors likely to influence non-attendance (review 3). Data sources: Database searches were conducted on Allied and Complementary Medicine, Cumulative Index to Nursing and Allied Health Literature Plus with Full Text, The Cochrane Library, EMBASE (via NHS Evidence from 1 January 2000 to January/February 2012), Health Management Information Consortium database, Institute of Electrical and Electronics Engineers Xplore, The King’s Fund Library Catalogue, Maternity and Infant Care, MEDLINE, Physiotherapy Evidence Database, PsycINFO, SPORTDiscus and Web of Science from 1 January 2000 to January/February 2012. Supplementary screening of references of included studies was conducted to identify additional potentially relevant studies. Conceptual papers were identified for review 1, randomised controlled trials (RCTs) and systematic reviews for review 2 and a range of quantitative and qualitative research designs for review 3. Methods: We conducted three inter-related reviews of quantitative and qualitative evidence, involving a review of conceptual frameworks of reminder systems and adherence behaviours, a review of the reminder effectiveness literature and a review informed by realist principles to explain the contexts and mechanisms that explain reminder effectiveness. A preliminary conceptual framework was developed to show how reminder systems work, for whom they work and in which circumstances. Six themes emerged that potentially influence the effectiveness of the reminder or whether or not patients would attend their appointment, namely the reminder–patient interaction, reminder accessibility, health-care settings, wider social issues, cancellation and rebookings, and distal/proxy attributes. Standardised review methods were used to investigate the effectiveness of reminders to promote attendance, cancellation or rebooking across all outpatient settings. Finally, a review informed by realist principles was undertaken, using the conceptualframework to explain the context and mechanisms that influence how reminders support attendance, cancellation and rebooking. Results: A total of 466 papers relating to 463 studies were identified for reviews 2 and 3. Findings from 31 RCTs and 11 separate systematic reviews (review 2 only) revealed that reminder systems are consistently effective at reducing non-attendance at appointments, regardless of health-care setting or patient subgroups. Simple reminders that provide details of timing and location of appointments are effective for increasing attendance at appointments. Reminders that provide additional information over and above the date, time and location of the appointment (‘reminder plus’) may be more effective than simple reminders at reducing non-attendance and may be particularly useful for first appointments and screening appointments; simple reminders may be appropriate thereafter for most patients the majority of the time. There was strong evidence that the timing of reminders, between 1 and 7 days prior to the appointment, has no effect on attendance; substantial numbers of patients do not receive their reminder; reminders promote cancellation of appointments; inadequate structural factors prevent patients from cancelling appointments; and few studies investigated factors that influence the effectiveness of reminder systems for population subgroups. Limitations: Generally speaking, the systematic review method seeks to provide a precise answer to a tightly focused question, for which there is a high degree of homogeneity within the studies. A wide range of population types, intervention, comparison and outcomes is included within the RCTs we identified. However, use of this wider approach offers greater analytical capability in terms of understanding contextual and mechanistic factors that would not have been evident in a more narrowly focused review and increases confidence that the findings will have relevance in a wide range of service settings. Conclusions: Simple reminders or ‘reminder plus’ should be sent to all patients in the absence of any clear contraindication. Other reminder alternatives may be relevant for key groups of patients: those from a deprived background, ethnic minorities, substance abusers and those with comorbidities and/or illnesses. We are developing a practice guideline that may help managers to further tailor their reminder systems for their service and client groups. We recommend future research activities in three main areas. First, more studies should routinely consider the potential for differential effects of reminder systems between patient groups in order to identify any inequalities and remedies. Second, ‘reminder plus’ systems appear promising, but there is a need for further research to understand how they influence attendance behaviour. Third, further research is required to identify strategies to ‘optimise’ reminder systems and compare performance with current approaches

Demand and Capacity Management for Medical Practices

This thesis on tactical demand and capacity management for medical practices consists of four main parts. In the first part, we analyze the general planning and control decisions that need to be taken by a practice manager when opening and then running a medical practice. We further present a best-case data set containing all relevant information on interactions between patient and practice. We compare several real-world appointment data sets to this best-case data set, commenting on the consequences of not collecting specific data. We discuss the fundamental problem of defining model parameters from data and give recommendations for modelers and practitioners to bridge the gap between theory and practice. In the second part, we present a flexible analytical queueing model to investigate the relationship between the physician\u27s daily capacity, the panel size, and the distribution of indirect waiting times of patients. Essential features of the basic model are the consideration of queue length-dependent parameters such as the appointment request rate, the no-show probability, and the rescheduling probability. We present several extensions to the basic model, including the consideration of queue length-dependent service times. Finally, we investigate the model behavior by conducting extensive numerical experiments. In the third part, we propose deterministic integer linear programs that decide on the intake of new patients into panels over time, considering the future panel development. Here, we minimize the deviation between the expected panel workload and the physician\u27s capacity over time. We classify panel patients and define transition probabilities from one class to another from one period to the next. Experiments are conducted with parameters based on real-world data. We use the programs to define upper bounds on the number of patients in a patient class to be accepted in a period through solving the programs several times with different demand inputs. When we use those upper bounds in a stochastic discrete-event environment, the expected differences between workload and capacity can be significantly reduced over time, considering several future periods instead of one in the optimization. Using a detailed classification of new patients decreases the expected differences further. In the last part, we present further integer linear programs to decide on the intake of new patients. For example, we consider several physicians with overlapping panels and capacities as decision variables. Last but not least, we investigate how the queueing model and the panel management programs could be combined

Exploring Strategies for Reducing Patient Failure to Keep Scheduled Appointments

High no-show rates in the ambulatory setting lead to underutilized resources, decreased clinic revenue, and lower productivity. The purpose of this single case study was to explore strategies that administrators used to maintain acceptable no-show rates and maintain the sustainability of the healthcare practice. The target population for this study included local chapter members of a professional healthcare organization that provided access to practice managers and administrators in the Las Vegas, Nevada regional area where there are a large number of practices that are not part of a health system; the sustainability of these practices is dependent on allocation of adequate resources. The conceptual framework for this study was Kotter\u27s 8-step change management model that uses 8 steps for successfully managing change within the organization and developing quality improvement initiatives. Data collection included semistructured interviews with 2 practice leaders, observation of the organization\u27s practice management and appointment scheduling systems, and a review of internal reports related to appointment trends and no-show rates. Based on the data analysis using deductive and open coding techniques, 3 distinctive themes emerged from the data: appointment booking strategies, appointment reminder strategies, and provider flexibility. The results of this study might positively affect positive social change by helping administrators improve access to care in an outpatient setting through improved appointment utilization and improve patient care outcomes with more appointment availability

The Impact of Digital Self-Scheduling on No-Show Event Rates in Outpatient Clinics

The failure of patients to keep scheduled appointments results in significant loss of revenue due to decreased administrative efficiency, expensive clinical resource time, disruptive continuity of care between the patient and the provider, and reduced quality of care. The purpose of this quantitative study was to explore the use of digital self-scheduling as an emerging alternative to traditional office-assisted scheduling methods and determine if its use has an impact on reducing the occurrence of no-show events. The theoretical framework used for the study was the Consumer Behavior Theory. Three years of de-identified secondary data were collected from a large, adult primary care clinic, part of an integrated academic health system in the northeastern United States, in order to probe differences in the clinic’s no-show rates, before and after the implementation of digital self-scheduling. Using chi-square tests for independence, the study revealed a decrease in the no-show rate after digital self-scheduling was implemented. In addition, the no-show rate was lower for appointments that were scheduled using digital self-scheduling versus appointments that were scheduled using traditional office-assisted scheduling. The result of this study contributes to positive social change by acknowledging that patients are consumers who thrive on the digital convenience that they already experience in their daily lives and contributes to enhancing patient and provider communications. The use of digital self-scheduling and other digital transformation tools contributes to the holistic improvement of the patient experience and in increasing provider and care setting support staff satisfaction

Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates