Postpandemic Evaluation of the Eco-Efficiency of Personal Protective Equipment Against COVID-19 in Emergency Departments: Proposal for a Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic has had a profound impact on emergency department (ED) care in Canada and around the world. To prevent transmission of COVID-19, personal protective equipment (PPE) was required for all ED care providers in contact with suspected cases. With mass vaccination and improvements in several infection prevention components, our hypothesis is that the risks of transmission of COVID-19 will be significantly reduced and that current PPE use will have economic and ecological consequences that exceed its anticipated benefits. Evidence is needed to evaluate PPE use so that recommendations can ensure the clinical, economic, and environmental efficiency (ie, eco-efficiency) of its use. OBJECTIVE: To support the development of recommendations for the eco-efficient use of PPE, our research objectives are to (1) estimate the clinical effectiveness (reduced transmission, hospitalizations, mortality, and work absenteeism) of PPE against COVID-19 for health care workers; (2) estimate the financial cost of using PPE in the ED for the management of suspected or confirmed COVID-19 patients; and (3) estimate the ecological footprint of PPE use against COVID-19 in the ED. METHODS: We will conduct a mixed method study to evaluate the eco-efficiency of PPE use in the 5 EDs of the CHU de Québec-Université Laval (Québec, Canada). To achieve our goals, the project will include four phases: systematic review of the literature to assess the clinical effectiveness of PPE (objective 1; phase 1); cost estimation of PPE use in the ED using a time-driven activity-based costing method (objective 2; phase 2); ecological footprint estimation of PPE use using a life cycle assessment approach (objective 3; phase 3); and cost-consequence analysis and focus groups (integration of objectives 1 to 3; phase 4). RESULTS: The first 3 phases have started. The results of these phases will be available in 2023. Phase 4 will begin in 2023 and results will be available in 2024. CONCLUSIONS: While the benefits of PPE use are likely to diminish as health care workers' immunity increases, it is important to assess its economic and ecological impacts to develop recommendations to guide its eco-efficient use. TRIAL REGISTRATION: PROSPERO CRD42022302598; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=302598. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50682.

Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach.

BACKGROUND: Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS). METHODS: We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensus k -means clustering was performed independently on each derivation cohort, from which phenotypes' characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score. RESULTS: A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71). CONCLUSIONS: For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.

A simulation model to analyse automation scenarios in decontamination centers.

Decontamination centres provide sterilisation services (sort, disinfect, package, and sterilise) for reusable surgical instruments that have a vital impact on patient safety. The market trend is to increase the level of automation in the decontamination process, to increase productivity, and reduce the risk of human error and musculoskeletal injuries. The goal of this research is to study the use of automated guided vehicles (AGVs) in sterilisation departments, to improve safety and efficiency. A generic simulation model is created based on data gathering of various decontamination centres and is validated for a specific centre to analyse various aspects of applying AGVs to automate the internal transfer. Centre's potential to increase capacity through AGV application is analysed and a Design of Experiments is conducted to identify the most promising implementation scenarios. Results show reductions in treatment time and work in process, while ,maintaining the accessibility of medical instruments, and ensuring worker safety.

Automated screening of potential organ donors using a temporal machine learning model.

Organ donation is not meeting demand, and yet 30-60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949-0.981) for the neural network and 0.940 (0.908-0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.

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.

Prediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis.

The relationship between intraoperative nociception and acute postoperative pain is still not well established. The nociception level (NOL) Index (Medasense, Ramat Gan, Israel) uses a multiparametric approach to provide a 0-100 nociception score. The objective of the ancillary analysis of the NOLGYN study was to evaluate the ability of a machine-learning aglorithm to predict moderate to severe acute postoperative pain based on intraoperative NOL values. Our study uses the data from the NOLGYN study, a randomized controlled trial that evaluated the impact of NOL-guided intraoperative administration of fentanyl on overall fentanyl consumption compared to standard of care. Seventy patients (ASA class I-III, aged 18-75 years) scheduled for gynecological laparoscopic surgery were enrolled. Variables included baseline demographics, NOL reaction to incision or intubation, median NOL during surgery, NOL time-weighted average (TWA) above or under manufacturers' recommended thresholds (10-25), and percentage of surgical time spent with NOL > 25 or < 10. We evaluated different machine learning algorithms to predict postoperative pain. Performance was assessed using cross-validated area under the ROC curve (CV-AUC). Of the 66 patients analyzed, 42 (63.6%) experienced moderate to severe pain. NOL post-intubation (42.8 (31.8-50.6) vs. 34.8 (25.6-41.3), p = 0.05), median NOL during surgery (13 (11-15) vs. 11 (8-13), p = 0.027), percentage of surgical time spent with NOL > 25 (23% (18-18) vs. 20% (15-24), p = 0.036), NOL TWA < 10 (2.54 (2.1-3.0) vs. 2.86 (2.48-3.62), p = 0.044) and percentage of surgical time spent with NOL < 10 (41% (36-47) vs. 47% (40-55), p = 0.022) were associated with moderate to severe PACU pain. Corresponding ROC AUC for the prediction of moderate to severe PACU pain were 0.65 [0.51-0.79], 0.66 [0.52-0.81], 0.66 [0.52-0.79], 0.65 [0.51-0.79] and 0.67 [0.53-0.81]. Penalized logistic regression achieved the best performance with a 0.753 (0.718-0.788) CV-AUC. Our results, even if limited by the small number of patients, suggest that acute postoperative pain is better predicted by a multivariate machine-learning algorithm rather than individual intraoperative nociception variables. Further larger multicentric trials are highly recommended to better understand the relationship between intraoperative nociception and acute postoperative pain.Trial registration Registered on ClinicalTrials.gov in October 2018 (NCT03776838).

Identification of the intraoperative antinociceptive effect of intravenous fentanyl using the Nociception Level (NOL) index versus clinical parameters in patients undergoing gynecological laparoscopic surgery: A secondary analysis of the NOLGYN study.

BACKGROUND: While we typically assess nociception balance during general anesthesia through clinical parameters such as heart rate (HR) and mean arterial pressure (MAP) variation, these parameters are not specific to nociception. OBJECTIVE: We hypothesized that using the Nociception Level (NOL) index to assess the analgesic effect of a fentanyl bolus would be superior to standard clinical parameters. DESIGN: Ancillary study of the NOLGYN study, a randomized controlled trial comparing intraoperative NOL-guided administration of fentanyl (NOL group) versus standardized care (SC group). SETTING: University hospital in Montréal, Canada between November 2018, and December 2019. PATIENTS: Women undergoing gynecological laparoscopic surgery. INTERVENTION: In our evaluation of intraoperative nociception, we analyzed the analgesic effect of fentanyl using three parameters: MAP, HR, and the Nociception Level (NOL) index. All fentanyl injection events were extracted from the database. MAIN OUTCOME MEASURE: The primary endpoint was the difference between values before and after each injection. RESULTS: The median of the NOL index before fentanyl injection was 30.5 (IQR 19.4 to 40.7) versus 18.9 (IQR 11.5 to 27.4) after (P < 0.001). The median of MAP was 106.4 mmHg (IQR 99.9 to 113.4) before injection versus 103.2 mmHg (IQR 97.5-110.7) after (P < 0.001). The median of HR before injection was 74.2 (IQR 64.2-83.8) versus 72.4 (IQR 63.4-81.3) after (P < 0.001). CONCLUSIONS: The NOL index, HR, and MAP all statistically discriminated the analgesic effect of fentanyl but only the NOL index proved clinically relevant to identify the analgesic effect of one fentanyl injection. TRIAL REGISTRATION: www. CLINICALTRIALS: gov (NCT03776838) registered in October 2018.

Planning for resilience in screening operations using discrete event simulation modeling: example of HPV testing in Peru.

BACKGROUND: The World Health Organization (WHO) has called for the elimination of cervical cancer. Unfortunately, the implementation of cost-effective prevention and control strategies has faced significant barriers, such as insufficient guidance on best practices for resource and operations planning. Therefore, we demonstrate the value of discrete event simulation (DES) in implementation science research and practice, particularly to support the programmatic and operational planning for sustainable and resilient delivery of healthcare interventions. Our specific example shows how DES models can inform planning for scale-up and resilient operations of a new HPV-based screen and treat program in Iquitos, an Amazonian city of Peru. METHODS: Using data from a time and motion study and cervical cancer screening registry from Iquitos, Peru, we developed a DES model to conduct virtual experimentation with "what-if" scenarios that compare different workflow and processing strategies under resource constraints and disruptions to the screening system. RESULTS: Our simulations show how much the screening system's capacity can be increased at current resource levels, how much variability in service times can be tolerated, and the extent of resilience to disruptions such as curtailed resources. The simulations also identify the resources that would be required to scale up for larger target populations or increased resilience to disruptions, illustrating the key tradeoff between resilience and efficiency. Thus, our results demonstrate how DES models can inform specific resourcing decisions but can also highlight important tradeoffs and suggest general "rules" for resource and operational planning. CONCLUSIONS: Multilevel planning and implementation challenges are not unique to sustainable adoption of cervical cancer screening programs but represent common barriers to the successful scale-up of many preventative health interventions worldwide. DES represents a broadly applicable tool to address complex implementation challenges identified at the national, regional, and local levels across settings and health interventions-how to make effective and efficient operational and resourcing decisions to support program adaptation to local constraints and demands so that they are resilient to changing demands and more likely to be maintained with fidelity over time.

Supporting scale-up of COVID-19 RT-PCR testing processes with discrete event simulation.

Testing is critical to mitigating the COVID-19 pandemic, but testing capacity has fallen short of the need in the United States and elsewhere, and long wait times have impeded rapid isolation of cases. Operational challenges such as supply problems and personnel shortages have led to these bottlenecks and inhibited the scale-up of testing to needed levels. This paper uses operational simulations to facilitate rapid scale-up of testing capacity during this public health emergency. Specifically, discrete event simulation models were developed to represent the RT-PCR testing process in a large University of Maryland testing center, which retrofitted high-throughput molecular testing capacity to meet pandemic demands in a partnership with the State of Maryland. The simulation models support analyses that identify process steps which create bottlenecks, and evaluate "what-if" scenarios for process changes that could expand testing capacity. This enables virtual experimentation to understand the trade-offs associated with different interventions that increase testing capacity, allowing the identification of solutions that have high leverage at a feasible and acceptable cost. For example, using a virucidal collection medium which enables safe discarding of swabs at the point of collection removed a time-consuming "deswabbing" step (a primary bottleneck in this laboratory) and nearly doubled the testing capacity. The models are also used to estimate the impact of demand variability on laboratory performance and the minimum equipment and personnel required to meet various target capacities, assisting in scale-up for any laboratories following the same process steps. In sum, the results demonstrate that by using simulation modeling of the operations of SARS-CoV-2 RT-PCR testing, preparedness planners are able to identify high-leverage process changes to increase testing capacity.

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.

A stochastic tabu search algorithm to align physician schedule with patient flow.

In this study, we consider the pretreatment phase for cancer patients. This is defined as the period between the referral to a cancer center and the confirmation of the treatment plan. Physicians have been identified as bottlenecks in this process, and the goal is to determine a weekly cyclic schedule that improves the patient flow and shortens the pretreatment duration. High uncertainty is associated with the arrival day, profile and type of cancer of each patient. We also include physician satisfaction in the objective function. We present a MIP model for the problem and develop a tabu search algorithm, considering both deterministic and stochastic cases. Experiments show that our method compares very well to CPLEX under deterministic conditions. We describe the stochastic approach in detail and present a real application.

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.

High efficiency endocrine operation protocol: From design to implementation.

BACKGROUND: We developed a high efficiency endocrine operative protocol based on a mathematical programming approach, process reengineering, and value-stream mapping to increase the number of operations completed per day without increasing operating room time at a tertiary-care, academic center. METHODS: Using this protocol, a case-control study of 72 patients undergoing endocrine operation during high efficiency days were age, sex, and procedure-matched to 72 patients undergoing operation during standard days. The demographic profile, operative times, and perioperative complications were noted. RESULTS: The average number of cases per 8-hour workday in the high efficiency and standard operating rooms were 7 and 5, respectively. Mean procedure times in both groups were similar. The turnaround time (mean ± standard deviation) in the high efficiency group was 8.5 (±2.7) minutes as compared with 15.4 (±4.9) minutes in the standard group (P < .001). Transient postoperative hypocalcemia was 6.9% (5/72) and 8.3% (6/72) for the high efficiency and standard groups, respectively (P = .99). CONCLUSION: In this study, patients undergoing high efficiency endocrine operation had similar procedure times and perioperative complications compared with the standard group. The proposed high efficiency protocol seems to better utilize operative time and decrease the backlog of patients waiting for endocrine operation in a country with a universal national health care program.

The nurse scheduling problem in real-life.

The aim of this paper is to study the scheduling process for two types of nursing teams, regular teams from care units and the float team that covers for shortages in the hospital. When managers address this problem, they either use a manual approach or have to invest in expensive commercial tool. We propose a simple heuristic approach, flexible and easy enough to be implemented on spreadsheets, and requiring almost no investment. The approach leads to streamlined process and higher-quality schedules for nurses. The multi-objective model and heuristics are presented, and additional analysis is performed to compare the performance of the approach. We show that our approach compares very well with an optimization software (CPLEX solver) and may be implemented at no cost. It addresses the lack of choice between either manual solution method or a commercial package at a high cost.

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.