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Background: Canadian health systems fare poorly in providing timely access to elective surgical care, which is crucial for quality, trust, and satisfaction. Methods: We conducted a cross-sectional analysis of surgical wait times for adults receiving non-urgent cataract surgery, knee arthroplasty, hip arthroplasty, gallbladder surgery, and non-cancer uterine surgery in Ontario, Canada, between 2013 and 2019. We obtained data from the Wait Times Information System (WTIS) database. Inter- and intra-hospital and surgeon variations in wait time were described graphically with caterpillar plots. We used non-nested 3-level hierarchical random effects models to estimate variation partition coefficients, quantifying the proportion of wait time variance attributable to surgeons and hospitals. Results: A total of 942,605 procedures at 107 healthcare facilities, conducted by 1,834 surgeons, were included in the analysis. We observed significant intra- and inter-provider variations in wait times across all five surgical procedures. Inter-facility median wait time varied between six-fold for gallbladder surgery and 15-fold for knee arthroplasty. Inter-surgeon variation was more pronounced, ranging from a 17-fold median wait time difference for cataract surgery to a 216-fold difference for non-cancer uterine surgery. The proportion of variation in wait times attributable to facilities ranged from 6.2% for gallbladder surgery to 23.0% for cataract surgery. In comparison, surgeon-related variation ranged from 16.0% for non-cancer uterine surgery to 28.0% for cataract surgery. Implications: There is extreme variability in surgical wait times for five common, high-volume, non-urgent surgical procedures. Strategies to address surgical wait times must address the variation between service providers through better coordination of supply and demand. Approaches such as single-entry models could improve surgical system performance. [ABSTRACT FROM AUTHOR]

Background: Dynamic treatment in Gamma Knife (GK) radiosurgery systems delivers radiation continuously with couch movement, as opposed to stationary step‐and‐shoot treatment where radiation is paused when moving between isocenters. Previous studies have shown the potential for dynamic GK treatment to give faster treatment times and improved dose conformity and homogeneity. However, these studies focused only on computational simulations and lack physical validation. Purpose: This study aims conduct dynamic treatment dosimetric validation with physical experimental measurements. The experiments aim to (1) address assumptions made with computational studies, such as the validity of treating a continuous path as discretised points, (2) investigate uncertainties in translating computed plans to actual treatment, and (3) determine ideal treatment planning parameters, such as interval distance for the path discretization, collimator change limitations, and minimum isocenter treatment times. Methods: This study uses a GK ICON treatment delivery machine, and a motion phantom custom‐made to attach to the machine's mask adapter and move in 1D superior‐inferior motion. Phantom positioning is first verified through comparisons against couch motion and computed doses. For dynamic treatment experiments, the phantom is moved through a program that first reads the desired treatment plan isocenters' position, time, and collimator sizes, then carries out the motion continuously while the treatment machine delivers radiation. Measurements are done with increasing levels of complexity: varying speed, varying collimator sizes, varying both speed and collimator sizes, then extends the same measurements to simulated 2D motion by combining phantom and couch motion. Dose comparisons between phantom motion radiation measurements and either couch motion measurements or dose calculations are analyzed with 2 mm/2% and 1 mm/2% gamma indices, using both local and global gamma index calculations. Results: Phantom positional experiments show a high accuracy, with global gamma indices for all dose comparisons ≥$\ge $99%. Discretization level to approximate continuous path as discrete points show the good dose matches with dose calculations when using 1 and 2‐mm gaps. Complex 1D motion, including varying speed, collimator sizes, or both, as well as 2D motion with the same complexities, all show good dose matches with dose calculations: the scores are ≥$\ge $92.0% for the strictest 1 mm/2% local gamma index calculation, ≥$\ge $ 99.8% for 2 mm/2% local gamma index, and ≥$\ge $97.0% for all global gamma indices. Five simulated 2D treatments with optimized plans scored highly as well, with all gamma index scores ≥$\ge $95.3% when compared to stationary treatment, and scores ≥$\ge $97.9% when compared to plan calculated dose. Conclusions: Dynamic treatment computational studies are validated, with dynamic treatment shown to be physically feasible and deliverable with high accuracy. A 2‐mm discretization level in treatment planning is proposed as the best option for shorter dose calculation times while maintaining dose accuracy. Our experimental method enables dynamic treatment measurements using the existing clinical workflow, which may be replicated in other centers, and future studies may include 2D or 3D motion experiments, or planning studies to further quantify potential indication‐specific benefits. [ABSTRACT FROM AUTHOR]

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 2,697 patients that underwent allogeneic HCT from January 1976 to December 2017. 45 pretransplant baseline variables were included in the predictive assessment of each ML algorithm on overall survival (OS) as determined by area under the curve (AUC). Pretransplant variables used in the EBMT ML 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. [ABSTRACT FROM AUTHOR]

Problem definition: Kidney paired donation programs allow patients registered with an incompatible donor to receive a suitable kidney from another donor, as long as the latter's co-registered patient, if any, also receives a kidney from a different donor. The kidney exchange problem (KEP) aims to find an optimal collection of kidney exchanges taking the form of cycles and chains. Methodology/results: We develop the first decomposition method that is able to consider long cycles and long chains for projected large realistic instances. Particularly, we propose a branch-and-price framework in which the pricing problems are solved (for the first time in packing problems in a digraph) through multivalued decision diagrams. We present a new upper bound on the optimal value of the KEP, obtained via our master problem. Computational experiments show superior performance of our method over the state of the art by optimally solving almost all instances in the PrefLib library for multiple cycle and chain lengths. Managerial implications: Our algorithm also allows the prioritization of the solution composition, for example, chains over cycles or vice versa, and we conclude, similar to previous findings, that chains benefit the overall matching efficiency and highly sensitized patients. Funding: This work was supported by NSERC Discovery Grant (RGPIN-2021-02609). Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0192. [ABSTRACT FROM AUTHOR]

Abstract. Operating rooms (ORs) play a substantial role in hospital profitability, and their optimal utilization is conducive to containing the cost of surgical service delivery, shortening surgical patient wait times, [...]

Like so many other fields, public health is poised to reap the benefits of data mining technologies. Given the recent concern over influenza pandemics (H1N1) and emergent avian flu strains, [...]