Dynamic multistage scheduling for patient-centered care plans.

We investigate the scheduling practices of multistage outpatient health programs that offer care plans customized to the needs of their patients. We formulate the scheduling problem as a Markov decision process (MDP) where patients can reschedule their appointment, may fail to show up, and may become ineligible. The MDP has an exponentially large state space and thus, we introduce a linear approximation to the value function.

Prediction of personal protective equipment use in hospitals during COVID-19.

Demand for Personal Protective Equipment (PPE) such as surgical masks, gloves, and gowns has increased significantly since the onset of the COVID-19 pandemic. In hospital settings, both medical staff and patients are required to wear PPE. As these facilities resume regular operations, staff will be required to wear PPE at all times while additional PPE will be mandated during medical procedures.

The importance of evaluating the complete automated knowledge-based planning pipeline.

We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained two dose prediction methods, a generative adversarial network (GAN) and a random forest (RF) with the same 130 treatment plans. The models were applied to 87 out-of-sample patients to create two sets of predicted dose distributions that were used as input to two optimization models.

Knowledge-based automated planning with three-dimensional generative adversarial networks.

Purpose: To develop a knowledge-based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three-dimensional (3D) dose.

Methods: Our knowledge-based automated planning (KBAP) pipeline consisted of a knowledge-based planning (KBP) method that predicts dose for a contoured computed tomography (CT) image followed by two optimization models that learn objective function weights and generate fluence-based plans, respectively. We developed a novel generative adversarial network (GAN)-based KBP approach, a 3D GAN model, which predicts dose for the full 3D CT image at once and accounts for correlations between adjacent CT slices.

Inventory management of reusable surgical supplies.

We investigate the inventory management practices for reusable surgical instruments that must be sterilized between uses. We study a hospital that outsources their sterilization services and model the inventory process as a discrete-time Markov chain. We present two base-stock inventory models, one that considers stockout-based substitution and one that does not.

Patient and operational factors affecting wait times in a bariatric surgery program in Toronto: a retrospective cohort study.

Background: Increasing rates of obesity have led to growing demand for bariatric surgery. This has implications for wait times, particularly in publicly funded programs. This study examined the impact of patient and operational factors on wait times in a multidisciplinary bariatric surgery program.

Analysis of patient attrition in a publicly funded bariatric surgery program.

Background: Obesity is a global epidemic, and several surgical programs have been created to combat this public health issue. Although demand for bariatric surgery has grown, so too has the attrition rate. In this study we identify patient characteristics and operational interventions that have contributed to high attrition in a multistage, multidisciplinary bariatric surgery program.