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

Patient-Centered Appointment Scheduling Using Agent-Based Simulation

Enhanced access and continuity are key components of patient-centered care. Existing studies show that several interventions such as providing same day appointments, walk-in services, after-hours care, and group appointments, have been used to redesign the healthcare systems for improved access to primary care. However, an intervention focusing on a single component of care delivery (i.e. improving access to acute care) might have a negative impact other components of the system (i.e. reduced continuity of care for chronic patients). Therefore, primary care clinics should consider implementing multiple interventions tailored for their patient population needs. We collected rapid ethnography and observations to better understand clinic workflow and key constraints. We then developed an agent-based simulation model that includes all access modalities (appointments, walk-ins, and after-hours access), incorporate resources and key constraints and determine the best appointment scheduling method that improves access and continuity of care. This paper demonstrates the value of simulation models to test a variety of alternative strategies to improve access to care through scheduling

Dynamics of Physician Practice Management

This VoiceThread is a highlight of the major learning experiences of my practicum. The most talk about were the cancellations and no-show project, and mitigation strategies to combat canellation and no-shows

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

Optimising cardiac services using routinely collected data and discrete event simulation

Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces

Managerial Intervention Strategies to Reduce Patient No-Show Rates

High patient no-show rates increase health care costs, decrease healthcare access, and reduce the clinical efficiency and productivity of health care facilities. The purpose of this exploratory qualitative single case study was to explore and analyze the managerial intervention strategies healthcare administrators use to reduce patient no-show rates. The targeted research population was active American College of Healthcare Executives (ACHE), Hawaii-Pacific Chapter healthcare administrative members with operational and supervisory experience addressing administrative patient no-show interventions. The conceptual framework was the theory of planned behavior. Semistructured interviews were conducted with 4 healthcare administrators, and appointment cancellation policy documents were reviewed. Interpretations of the data were subjected to member checking to ensure the trustworthiness of the findings. Based on the methodological triangulation of the data collected, 5 common themes emerged after the data analysis: reform appointment cancellation policies, use text message appointment reminders, improve patient accessibility, fill patient no-show slots immediately, and create organizational and administrative efficiencies. Sharing the findings of this study may help healthcare administrators to improve patient health care accessibility, organizational performance and the social well-being of their communities

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