Determining pre-procedure fasting alert time using procedural and scheduling data.

Before a medical procedure requiring anesthesia, patients are required to not eat or drink non-clear fluids for 6 h and not drink clear fluids for 2 h. Fasting durations in standard practice far exceed these minimum thresholds due to uncertainties in procedure start time. The aim of this retrospective, observational study was to compare fasting durations arising from standard practice with different approaches for calculating the timepoint at which patients are instructed to stop eating and drinking. Scheduling data for procedures performed in the cardiac catheterization laboratory of an academic hospital in Canada (January 2020 to April 2022) were used. Four approaches utilizing machine learning (ML) and simulation were used to predict procedure start times and calculate when patients should be instructed to start fasting. Median fasting duration for standard practice was 10.08 h (IQR 3.5) for both food and clear fluids intake. The best performing alternative approach, using tree-based ML models to predict procedure start time, reduced median fasting from food/non-clear fluids to 7.7 h (IQR 2) and clear liquids fasting to 3.7 h (IQR 2.4). 97.3% met the minimum fasting duration requirements (95% CI 96.9% to 97.6%). Further studies are required to determine the effectiveness of operationalizing this approach as an automated fasting alert system.

Dynamic optimization of chemotherapy outpatient scheduling with uncertainty.

Chemotherapy outpatient scheduling is a complex, dynamic, uncertain problem. Chemotherapy centres are facing increasing demands and they need to increase their efficiency; however there are very few studies looking at using optimization technology on the chemotherapy scheduling problem. We address dynamic uncertainty that arises from requests for appointments that arrive in real time and uncertainty due to last minute scheduling changes. We propose dynamic template scheduling, a novel technique that combines proactive and online optimization and we apply it to the chemotherapy outpatient scheduling problem. We create a proactive template of an expected day in the chemotherapy centre using a deterministic optimization model and a sample of appointments. As requests for appointments arrive, we use the template to schedule them. When a request arrives that does not fit the template, we update the template online using the optimization model and a revised set of appointments. To accommodate last minute additions and cancellations to the schedule, we propose a shuffling algorithm that moves appointment start times within a predefined time limit. We test the use of dynamic template scheduling against the optimal offline solution and the actual performance of the cancer centre. We find improvements in makespan of up to 20 % when using dynamic template scheduling compared to current practice.

Treatment planning using tailored and standard cylindrical light diffusers for photodynamic therapy of the prostate.

Interstitial photodynamic therapy (PDT) has seen a rebirth, partially prompted by the development of photosensitizers with longer absorption wavelengths that enable the treatment of larger tissue volumes. Here, we study whether using diffusers with customizable longitudinal emission profiles, rather than conventional ones with flat emission profiles, improves our ability to conform the light dose to the prostate. We present a modified Cimmino linear feasibility algorithm to solve the treatment planning problem, which improves upon previous algorithms by (1) correctly minimizing the cost function that penalizes deviations from the prescribed light dose, and (2) regularizing the inverse problem. Based on this algorithm, treatment plans were obtained under a variety of light delivery scenarios using 5-15 standard or tailored diffusers. The sensitivity of the resulting light dose distributions to uncertainties in the optical properties, and the placement of diffusers was also studied. We find that tailored diffusers only marginally outperform conventional ones in terms of prostate coverage and rectal sparing. Furthermore, it is shown that small perturbations in optical properties can lead to large changes in the light dose distribution, but that those changes can be largely corrected with a simple light dose re-normalization. Finally, we find that prostate coverage is only minimally affected by small changes in diffuser placement. Our results suggest that prostate PDT is not likely to benefit from the use of tailored diffusers. Other locations with more complex geometries might see a better improvement.