The Impact of Probabilistic Classifiers on Appointment Scheduling with No-Shows

Appointment no-shows are common in outpatient clinics and increase clinic costs and patients’ dissatisfaction. We develop a framework to predict the no-show probabilities of a given set of patients, and to subsequently employ these predictions to find the optimal appointment schedule. Some existing work assumes that all patients have the same no-show probability (1-class approach); other work assumes that patients have either a low or a high no-show probability (2-class approach). In contrast, we utilize probabilistic classifiers to obtain the individual patients’ no-show probabilities (N-class approach). Our approach results in better-quality schedules, as measured by a weighted average of patient waiting time and provider overtime. We also find that a small increase in the prediction performance (measured by the Brier score) translates into a large decrease in the schedule cost. Our results are obtained through a large-scale computational study and validated on a real-world data set from an outpatient clinic

Organizing Multidisciplinary Care for Children with Neuromuscular Diseases

The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the `Children's Muscle Center Amsterdam' (CMCA). The CMCA diagnoses and treats children with neuromuscular diseases. These patients require care from a variety of clinicians. Through the establishment of the CMCA, children and their parents will generally visit the hospital only once a year, while previously they visited on average six times a year. This is a major improvement, because the hospital visits are both physically and psychologically demanding for the patients. This article describes how quantitative modelling supports the design and operations of the CMCA. First, an integer linear program is presented that selects which patients to invite for a treatment day and schedules the required combination of consultations, examinations and treatments on one day. Second, the integer linear program is used as input to a simulation to study to estimate the capacity of the CMCA, expressed in the distribution of the number patients that can be seen on one diagnosis day. Finally, a queueing model is formulated to predict the access time distributions based upon the simulation outcomes under various demand scenarios

Applying and integer Linear Programming Model to an appointment scheduling problem

Dissertação de Mestrado, Ciências Económicas e Empresariais (Economia e Políticas Públicas), 28 de fevereiro de 2022, Universidade dos Açores.A gestão de consultas ambulatórias pode ser um processo complexo, uma vez que envolve vários stakeholders com diferentes objetivos. Para os utentes poderá ser importante minimizar os tempos de espera. Simultaneamente, para os trabalhadores do setor da saúde, condições de trabalho justas devem ser garantidas. Assim, é cada vez mais necessário ter em conta o equilíbrio de cargas horárias e a otimização dos recursos disponíveis como principais preocupações no agendamento e planeamento de consultas. Nesta dissertação, uma abordagem com dois modelos para a criação de um sistema de agendamento de consultas é proposta. Esta abordagem é feita em programação linear, com dois modelos que têm como objetivo minimizar as diferenças de cargas horárias e melhorar o seu equilíbrio ao longo do planeamento. Os modelos foram estruturados e parametrizados de acordo com dados gerados aleatoriamente. Para isso, o desenvolvimento foi feito em Java, gerando assim os dados referidos. O Modelo I minimiza as diferenças de carga horária entre os quartos disponíveis. O Modelo II, por outro lado, propõe uma nova função objetivo que minimiza a diferença máxima observada, com um processo de decisão minxmax. Os modelos mostram resultados eficientes em tempos de execução razoáveis para instâncias com menos de aproximadamente 10 quartos disponíveis. Os tempos de execução mais altos são observados quando as instâncias ultrapassam este número de quartos disponíveis. Em relação ao equilíbrio da carga horária, observou-se que o número de especialidades disponíveis para atendimento e a procura por dia foram o que mais influenciou a minimização da diferença da carga horária. Os resultados do Modelo II mostram melhor tempo de execução e um maior número de soluções ótimas. Uma vez que as diferenças entre os dois modelos não são consideráveis, o Modelo I poderá representar um melhor conjunto de soluções para os decisores já que minimiza a diferença da carga horária total entre quartos em vez de apenas minimizar o valor máximo da diferença de carga horária entre quaisquer dois quartos.ABSTRACT: Outpatient appointment management can be a complex process since it involves many conflicting stakeholders. As for the patients it might be important to minimize waiting time. Simultaneously, for healthcare workers, fair working conditions must be guaranteed. Thus, it is increasingly necessary to have workload balance and resource optimization as the main concerns in the scheduling and planning of outpatient appointments. In this dissertation, a two-model approach for designing an appointment scheduling is proposed. This approach is formulated as two mathematical Integer Linear Programming models that integrate the objective of minimizing workload difference and improving workload balance. The models were structured and parameterized according to randomly generated data. For this, the work was developed in Java, generating said data. Model I minimizes the workload differences among rooms. Model II, on the other hand, proposes a new objective function that minimizes the maximum workload difference, with a minxmax decision process. The computational models behaves efficiently in reasonable run times for numerical examples with less than approximately 10 rooms available. Higher run times are observed when numerical examples surpass these number of available rooms. Regarding workload balance, it was observed that the number of specialties available for appointments and the demand for each day were the most influential in the minimization of workload difference. Model II results show a shorter model run time and more optimal solutions. As the differences between both Models are not considerable, Model I might propose a better set of solution for decision makers since it minimizes the total workload difference amongst rooms instead of only minimizing the maximum workload difference between any two rooms

Data Analytics and Modeling for Appointment No-show in Community Health Centers

Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions

Managing magnetic resonance imaging machines: support tools for scheduling and planning

We devise models and algorithms to estimate the impact of current and future patient demand for examinations on Magnetic Resonance Imaging (MRI) machines at a hospital radiology department. Our work helps improve scheduling decisions and supports MRI machine personnel and equipment planning decisions. Of particular novelty is our use of scheduling algorithms to compute the competing objectives of maximizing examination throughput and patient-magnet utilization. Using our algorithms retrospectively can help (1) assess prior scheduling decisions, (2) identify potential areas of efficiency improvement and (3) identify difficult examination types. Using a year of patient data and several years of MRI utilization data, we construct a simulation model to forecast MRI machine demand under a variety of scenarios. Under our predicted demand model, the throughput calculated by our algorithms acts as an estimate of the overtime MRI time required, and thus, can be used to help predict the impact of different trends in examination demand and to support MRI machine staffing and equipment planning

Simulation and Modeling for Improving Access to Care for Underserved Populations

Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

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

Improving patient access in oncology clinics using simulation

Purpose: Providing timely access is an important measure of patient satisfaction in specialty care clinicssuch as cancer centers. Excessive patient wait time to see an oncologist is very critical for cancer patients asthey often benefit from starting the treatment process as soon as possible. This paper addresses capacityplanning for both new and returning patients in cancer clinics. This research is motivated by a cancercenter in Texas that seeks to improve its clinical performance to decrease new patient wait time to see anoncologist.Design/methodology/approach: A simulation model is proposed to assess new patient access tooncologists when employing several tactical and operational policies such as resource flexibility,specialization flexibility, and reserving slots for new patients. The model utilizes two years of data collectedfrom a cancer center in Texas.Findings:The results suggest the best combination of operating policies in order to allocate patientdemand to providers. This study also determines the required capacity level to provide timely access fornew patients.Originality/value: Although the literature in outpatient scheduling and capacity planning is rich, newpatient access in oncology clinics has received limited attention. The few existing studies do not considerpatient no-shows and cancellations, and to the best of our knowledge, no study addresses individualoncologist clinic flexibility and the idea of reserving slots for new patientsPeer Reviewe