A Simulation Approach for Scheduling Patients in the Department of Radiation Oncology

Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules

BACKGROUND

Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. However, those whose symptoms significantly improve and remain stable do not need to conform to a regular follow-up schedule. Based on the 1-year postoperative outcomes, we aimed to use a machine-learning (ML) algorithm to predict 2-year postoperative outcomes.

METHODS

We enrolled 80 patients who underwent cervical laminoplasty for cervical myelopathy. The patients' Japanese Orthopedic Association (JOA) scores (range: 0-17) were analyzed at the 1-, 3-, 6-, and 12-month postoperative timepoints to evaluate their ability to predict the 2-year postoperative outcomes. The patient acceptable symptom state (PASS) was defined as a JOA score ≥ 14.25 at 24 months postoperatively and, based on clinical outcomes recorded up to the 1-year postoperative timepoint, eight ML algorithms were developed to predict PASS status at the 24-month postoperative timepoint. The performance of each of these algorithms was evaluated, and its generalizability was assessed using a prospective internal test set.

RESULTS

The long short-term memory (LSTM)-based algorithm demonstrated the best performance (area under the receiver operating characteristic curve, 0.90 ± 0.13).

CONCLUSIONS

The LSTM-based algorithm accurately predicted which group was likely to achieve PASS at the 24-month postoperative timepoint. Although this study included a small number of patients with limited available clinical data, the concept of using past outcomes to predict further outcomes presented herein may provide insights for optimizing clinical schedules and efficient medical resource utilization.

TRIAL REGISTRATION

This study was registered as a clinical trial (Clinical Trial No. NCT02487901), and the study protocol was approved by the Seoul National University Hospital Institutional Review Board (IRB No. 1505-037-670).

Efficient scheduling and attendance system for the ultrasound department under demand uncertainty during COVID-19

Scheduling and attendance management present huge challenges for hospitals, and the importance of both has become more critical as resource limitations and overwhelmingly uncertain demand are becoming more evident, especially during COVID-19. Important variables and factors need to be considered. When managers address this problem, they either use a manual approach or invest in expensive commercial tools. We propose a simple and flexible system that requires no extra investment. This system was developed using Ding Talk, Microsoft Excel and Visual C#. Ding Talk was used to collect vacation applications and clock information. A VBA-based Microsoft Excel program was developed to schedule shifts. A Windows Forms Application based on Visual C# was developed to complete the workload and attendance statistics. We focused on the design and implementation of the module of schedule generation and attendance management. Using the practical data of the Ultrasound Department, we compared the time spent on scheduling and attendance before and after the system was established. The results demonstrate that the system is feasible and efficient. Its high flexibility enables managers to quickly modify the schedule and attendance statistics to achieve dynamic management when dealing with inevitable demand changes during COVID-19.

A Predictive Decision Analytics Approach for Primary Care Operations Management: A Case Study of Double-Booking Strategy Design and Evaluation

Primary care plays a vital role for individuals and families in accessing care, keeping well, and improving quality of life. However, the complexities and uncertainties in the primary care delivery system (e.g., patient no-shows/walk-ins, staffing shortage, COVID-19 pandemic) have brought significant challenges in its operations management, which can potentially lead to poor patient outcomes and negative primary care operations (e.g., loss of productivity, inefficiency). This paper presents a decision analytics approach developed based on predictive analytics and hybrid simulation to better facilitate management of the underlying complexities and uncertainties in primary care operations. A case study was conducted in a local family medicine clinic to demonstrate the use of this approach for patient no-show management. In this case study, a patient no-show prediction model was used in conjunction with an integrated agent-based and discrete-event simulation model to design and evaluate double-booking strategies. Using the predicted patient no-show information, a prediction-based double-booking strategy was created and compared against two other strategies, namely random and designated time. Scenario-based experiments were then conducted to examine the impacts of different double-booking strategies on clinic's operational outcomes, focusing on the trade-offs between the clinic productivity (measured by daily patient throughput) and efficiency (measured by visit cycle and patient wait time for doctor). The results showed that the best productivity-efficiency balance was derived under the prediction-based double-booking strategy. The proposed hybrid decision analytics approach has the potential to better support decision-making in primary care operations management and improve the system's performance. Further, it can be generalized in the context of various healthcare settings for broader applications.

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.

A Simulation-Based Planning Methodology for Decreasing Patient Waiting Times in Pure Walk-In Clinics

This article presents a simulation-based planning methodology that aims to improve patient service quality in pure walk-in clinics. Capacity planning is one of the major challenges in walk-in clinics because of the uncertainty in both patient demand and arrival times. This work presents a discrete-event simulation model for walk-in clinics that takes into consideration patient behavior in terms of arrival times for capacity planning at the clinic level. The goal of the model is to provide a tool that will allow clinics to develop protocols that will reduce patient waiting times by scheduling doctor and medical assistants considering demand uncertainties. A case study is presented to illustrate the benefits of the methodology. The results of the computational study show that by allocating the right number of resources at particular times of the day, walk-in clinics can achieve operational steady state while providing services to patients with minimum waiting times. The tool can be adapted and used to support any walk-in clinic.

Operations research for resource planning and -use in radiotherapy: a literature review

Background

The delivery of radiotherapy (RT) involves the use of rather expensive resources and multi-disciplinary staff. As the number of cancer patients receiving RT increases, timely delivery becomes increasingly difficult due to the complexities related to, among others, variable patient inflow, complex patient routing, and the joint planning of multiple resources. Operations research (OR) methods have been successfully applied to solve many logistics problems through the development of advanced analytical models for improved decision making. This paper presents the state of the art in the application of OR methods for logistics optimization in RT, at various managerial levels.

Methods

A literature search was performed in six databases covering several disciplines, from the medical to the technical field. Papers included in the review were published in peer-reviewed journals from 2000 to 2015. Data extraction includes the subject of research, the OR methods used in the study, the extent of implementation according to a six-stage model and the (potential) impact of the results in practice.

Results

From the 33 papers included in the review, 18 addressed problems related to patient scheduling (of which 12 focus on scheduling patients on linear accelerators), 8 focus on strategic decision making, 5 on resource capacity planning, and 2 on patient prioritization. Although calculating promising results, none of the papers reported a full implementation of the model with at least a thorough pre-post performance evaluation, indicating that, apart from possible reporting bias, implementation rates of OR models in RT are probably low.

Conclusions

The literature on OR applications in RT covers a wide range of approaches from strategic capacity management to operational scheduling levels, and shows that considerable benefits in terms of both waiting times and resource utilization are likely to be achieved. Various fields can be further developed, for instance optimizing the coordination between the available capacity of different imaging devices or developing scheduling models that consider the RT chain of operations as a whole rather than the treatment machines alone.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0390-4) contains supplementary material, which is available to authorized users.

Computer modeling of lung cancer diagnosis-to-treatment process

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.