What are the views of Quebec and Ontario citizens on the tiebreaker criteria for prioritizing access to adult critical care in the extreme context of a COVID-19 pandemic?

BACKGROUND: The prioritization protocols for accessing adult critical care in the extreme pandemic context contain tiebreaker criteria to facilitate decision-making in the allocation of resources between patients with a similar survival prognosis. Besides being controversial, little is known about the public acceptability of these tiebreakers. In order to better understand the public opinion, Quebec and Ontario's protocols were presented to the public in a democratic deliberation during the summer of 2022. OBJECTIVES: (1) To explore the perspectives of Quebec and Ontario citizens regarding tiebreakers, identifying the most acceptable ones and their underlying values. (2) To analyze these results considering other public consultations held during the pandemic on these criteria. METHODS: This was an exploratory qualitative study. The design involved an online democratic deliberation that took place over two days, simultaneously in Quebec and Ontario. Public participants were selected from a community sample which excluded healthcare workers. Participants were first presented the essential components of prioritization protocols and their related issues (training session day 1). They subsequently deliberated on the acceptability of these criteria (deliberation session day 2). The deliberation was then subject to thematic analysis. RESULTS: A total of 47 participants from the provinces of Quebec (n = 20) and Ontario (n = 27) took part in the online deliberation. A diverse audience participated excluding members of the healthcare workforce. Four themes were identified: (1) Priority to young patients - the life cycle - a preferred tiebreaker; (2) Randomization - a tiebreaker of last resort; (3) Multiplier effect of most exposed healthcare workers - a median acceptability tiebreaker, and (4) Social value - a less acceptable tiebreaker. CONCLUSION: Life cycle was the preferred tiebreaker as this criterion respects intergenerational equity, which was considered relevant when allocating scarce resources to adult patients in a context of extreme pandemic. Priority to young patients is in line with other consultations conducted around the world. Additional studies are needed to further investigate the public acceptability of tiebreaker criteria.

Machine learning for the prediction of survival post-allogeneic hematopoietic cell transplantation: A single-center experience.

INTRODUCTION: Prediction of outcomes following allogeneic hematopoietic cell transplantation (HCT) remains a major challenge. Machine learning (ML) is a computational procedure that may facilitate the generation of HCT prediction models. We sought to investigate the prognostic potential of multiple ML algorithms when applied to a large single-center allogeneic HCT database. METHODS: Our registry included 2697 patients that underwent allogeneic HCT from January 1976 to December 2017, 45 pre-transplant baseline variables were included in the predictive assessment of each ML algorithm on overall survival (OS) as determined by area under the curve (AUC). Pre-transplant variables used in the EBMT machine learning study (Shouval et al, 2015) were used as a benchmark for comparison. RESULTS: On the entire dataset, the random forest (RF) algorithm performed best (AUC 0.71±0.04) compared to the second-best model, logistic regression (LR) (AUC=0.69±0.04) (p<0.001). Both algorithms demonstrated improved AUC scores using all 45 variables compared to the limited variables examined by the EBMT study. Survival at 100 days post-HCT using RF on the full dataset discriminated patients into different prognostic groups with different 2-year OS (p<0.0001). We then examined the ML methods that allow for significant individual variable identification, including LR and RF, and identified matched related donors (HR=0.49, p<0.0001), increasing TBI dose (HR=1.60, p=0.006), increasing recipient age (HR=1.92, p<0.0001), higher baseline Hb (HR=0.59, p=0.0002) and increased baseline FEV1 (HR=0.73, p=0.02), among others. CONCLUSION: The application of multiple ML techniques on single center allogeneic HCT databases warrants further investigation and may provide a useful tool to identify variables with prognostic potential.

On integrating patient appointment grids and technologist schedules in a radiology center.

Optimal patient appointment grid scheduling improves medical center performance and reduces pressure from excess demand. Appointment scheduling efficiency depends on resource management, and staff are a key resource. Personnel scheduling takes into account union rules, skills, contract types, training, leave, illness, etc. When combined with appointment scheduling constraints, the complexity of the problem increases. In this paper, we study the combination of the patient appointment grid and technologist scheduling. We present a well-detailed framework outlining our approach. We develop two versions of a mixed-integer programming model: integrated and sequential. In the first version, we elaborate the appointment grid and the technologist schedules simultaneously, while in the second version we generate them sequentially. We evaluate the proposed approach using real data from the MRI department of the Centre hospitalier de l'Université de Montréal (CHUM) radiology center. We study different scenarios by testing several technologist rules and planning construction methods. Obtained solutions are compared to the current CHUM scheduling approach.

A two-phase approach for the Radiotherapy Scheduling Problem.

The Radiotherapy Scheduling Problem (RTSP) focuses on optimizing the planning of radiotherapy treatment sessions for cancer patients. In this paper, we propose a two-phase approach for the RTSP. In the first phase, radiotherapy sessions are assigned to specific linear accelerators (linacs) and days. The second phase then decides the sequence of patients on each day/linac and the specific appointment times. For the first phase, an Integer Linear Programming (IP) model is proposed and solved using CPLEX. For the second phase, a Mixed Integer Linear Programming (MIP) and a Constraint Programming (CP) model are proposed. The test data is generated based on real data from CHUM, a large cancer center in Montréal, Canada, with an average of 3,500 new patients and 40,000 radiotherapy treatments per year. The results show that in the second phase, CP is better at finding good solutions quickly while MIP is better at closing optimality gaps with more run time. Lastly, a simulation is conducted to evaluate the impact of different scheduling strategies on the outcome of the scheduling. Preliminary results show that batch scheduling reduces patients' waiting time and overdue time.

Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife.

Objective. Despite the high-quality treatment, the long treatment time of the Cyberknife system is believed to be a drawback. The high flexibility of its robotic arm requires meticulous path-finding algorithms to deliver the prescribed dose in the shortest time.Approach. We proposed a Deep Q-learning based on Graph Neural Networks to find the subset of the beams and the order to traverse them. A complex reward function is defined to minimize the distance covered by the robotic arm while avoiding the selection of close beams. Individual beam scores are also generated based on their effect on the beam intensity and are incorporated in the reward function. Main results. The performance of the presented method is evaluated on three clinical cases suffering from lung cancer. Applying this approach leads to an average of 35% reduction in the treatment time while delivering the prescribed dose provided by the physicians.Significance. Shorter treatment times result in a better treatment experience for individual patients, reduces discomfort and the sides effects of inadvertent movements for them. Additionally, it creates the opportunity to treat a higher number of patients in a given time period at the radiation therapy centers.

Radiotherapy treatment scheduling considering time window preferences.

External-beam radiotherapy treatments are delivered by a linear accelerator (linac) in a series of high-energy radiation sessions over multiple days. With the increase in the incidence of cancer and the use of radiotherapy (RT), the problem of automatically scheduling RT sessions while satisfying patient preferences regarding the time of their appointments becomes increasingly relevant. While most literature focuses on timeliness of treatments, several Dutch RT centers have expressed their need to include patient preferences when scheduling appointments for irradiation sessions. In this study, we propose a mixed-integer linear programming (MILP) model that solves the problem of scheduling and sequencing RT sessions considering time window preferences given by patients. The MILP model alone is able to solve the problem to optimality, scheduling all sessions within the desired window, in reasonable time for small size instances up to 66 patients and 2 linacs per week. For larger centers, we propose a heuristic method that pre-assigns patients to linacs to decompose the problem in subproblems (clusters of linacs) before using the MILP model to solve the subproblems to optimality in a sequential manner. We test our methodology using real-world data from a large Dutch RT center (8 linacs). Results show that, combining the heuristic with the MILP model, the problem can be solved in reasonable computation time with as few as 2.8% of the sessions being scheduled outside the desired time window.

Chemotherapy appointment scheduling and daily outpatient-nurse assignment.

Chemotherapy planning and patient-nurse assignment problems are complex multiobjective decision problems. Schedulers must make upstream decisions that affect daily operations. To improve productivity, we propose a two-stage procedure to schedule treatments for new patients, to plan nurse requirements, and to assign the daily patient mix to available nurses. We develop a mathematical formulation that uses a waiting list to take advantage of last-minute cancellations. In the first stage, we assign appointments to the new patients at the end of each day, we estimate the daily requirement for nurses, and we generate the waiting list. The second stage assigns patients to nurses while minimizing the number of nurses required. We test the procedure on realistically sized problems to demonstrate the impact on the cost effectiveness of the clinic.

Integrating DVH criteria into a column generation algorithm for VMAT treatment planning.

Volumetric-modulated arc therapy (VMAT) treatment planning is an efficient treatment technique with a high degree of flexibility in terms of dose rate, gantry speed, and aperture shapes during rotation around the patient. However, the dynamic nature of VMAT results in a large-scale nonconvex optimization problem. Determining the priority of the tissues and voxels to obtain clinically acceptable treatment plans poses additional challenges for VMAT optimization. The main purpose of this paper is to develop an automatic planning approach integrating dose-volume histogram (DVH) criteria in direct aperture optimization for VMAT, by adjusting the model parameters during the algorithm. The proposed algorithm is based on column generation, an optimization technique that sequentially generates the apertures and optimizes the corresponding intensities. We take the advantage of iterative procedure in this method to modify the weight vector of the penalty function based on the DVH criteria and decrease the use of trial-and-error in the search for clinically acceptable plans. We evaluate the efficiency of the algorithm and treatment quality using a clinical prostate case and a challenging head-and-neck case. In both cases, we generate 15 random initial weight vectors to assess the robustness of the algorithm. In the prostate case, our methodology obtained clinically acceptable plans in all instances with only a 10% increase in the computational time, while simple VMAT optimization found just three acceptable plans. To have an idea with respect to the existing software, we compared the obtained DVH to a commercial software. The quality of the diagrams of the proposed method, especially for the healthy tissues, is significantly better while the computational time is less. In the head-and-neck case, 93.3% of the clinically acceptable plans are obtained while no plan was acceptable in simple VMAT. In sum, the results demonstrate the ability of the proposed optimization algorithm to obtain clinically acceptable plans without human intervention and also its robustness to weight parameters. Moreover, our proposed weight adjustment procedure proves to reduce the symmetry in the solution space and the time required for the post-optimization phase.

Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center.

With the growth of the population, access to medical care is in high demand, and queues are becoming longer. The situation is more critical when it concerns serious diseases such as cancer. The primary problem is inefficient management of patients rather than a lack of resources. In this work, we collaborate with the Centre Intégré de Cancérologie de Laval (CICL). We present a data-driven study based on a nonblock approach to patient appointment scheduling. We use data mining and regression methods to develop a prediction model for radiotherapy treatment duration. The best model is constructed by a classification and regression tree; its accuracy is 84%. Based on the predicted duration, we design new workday divisions, which are evaluated with various patient sequencing rules. The results show that with our approach, 40 additional patients are treated daily in the cancer center, and a considerable improvement is noticed in patient waiting times and technologist overtime.

Productivity-driven physician scheduling in emergency departments.

The objective of this study is two-fold: to propose an alternative approach for computing the productivity of physicians in emergency departments (EDs); and, to allocate productivity-driven schedules to ED physicians so as to align physician productivity with demand (patient arrivals), without decreasing fairness between physicians, in order to improve patient wait times. Historical data between 2008 and 2017 from the Sacré-Coeur Montreal Hospital ED is analysed and used to predict the demand and to estimate the productivity of each physician. These estimates are incorporated into a mathematical programming model that identifies feasible schedules to physicians that minimise the difference between patients' demand and physicians' productivity, along with the violation of physicians' preferences and fairness in the distribution of shifts. Results on real-world-based data show that when physician productivity is included in the allocation of schedules, demand under-covering is reduced by 10.85% and the fairness between physicians is maintained. However, physicians' preferences (e.g., sum of the differences between the number of wanted shifts and the number of allocated shifts) deteriorates by 7.61%. By incorporating the productivity of physicians in the scheduling process, we see a reduction in EDs overcrowding and an improvement in the overall quality of health-care services.

Simultaneous delivery time and aperture shape optimization for the volumetric-modulated arc therapy (VMAT) treatment planning problem.

In this paper, we propose a novel heuristic algorithm for the volumetric-modulated arc therapy treatment planning problem, optimizing the trade-off between delivery time and treatment quality. We present a new mixed integer programming model in which the multi-leaf collimator leaf positions, gantry speed, and dose rate are determined simultaneously. Our heuristic is based on column generation; the aperture configuration is modeled in the columns and the dose distribution and time restriction in the rows. To reduce the number of voxels and increase the efficiency of the master model, we aggregate similar voxels using a clustering technique. The efficiency of the algorithm and the treatment quality are evaluated on a benchmark clinical prostate cancer case. The computational results show that a high-quality treatment is achievable using a four-thread CPU. Finally, we analyze the effects of the various parameters and two leaf-motion strategies.

Online stochastic optimization of radiotherapy patient scheduling.

The effective management of a cancer treatment facility for radiation therapy depends mainly on optimizing the use of the linear accelerators. In this project, we schedule patients on these machines taking into account their priority for treatment, the maximum waiting time before the first treatment, and the treatment duration. We collaborate with the Centre Intégré de Cancérologie de Laval to determine the best scheduling policy. Furthermore, we integrate the uncertainty related to the arrival of patients at the center. We develop a hybrid method combining stochastic optimization and online optimization to better meet the needs of central planning. We use information on the future arrivals of patients to provide an accurate picture of the expected utilization of resources. Results based on real data show that our method outperforms the policies typically used in treatment centers.