@article{ChanTCY.J141,

title = {Optimizing daily surgical scheduling improves operative time consumption: A retrospective study},

author = {A. Abbas and I. Saleh and P. Wong and J. Larouche and J. Abouali and S. Park and T. C. Y. Chan and V. Sarhangian and J. Toor},

doi = {10.1016/j.artd.2025.101926},

year  = {2026},

date = {2026-01-10},

urldate = {2026-01-10},

journal = {Arthroplasty Today},

abstract = {Background: Arthroplasty cases represent some of the highest volume and resource intensive orthopaedic procedures. Most cost containment efforts have focused on restricting access to operating room (OR) time rather than improving efficiency through scheduling optimization. However, approaches such as bin-packing optimization (BPO) can be used to generate more efficient schedules. This study compares manual scheduling with BPO to evaluate their impact on OR efficiency. Our hypothesis is that BPO scheduling produces more efficient OR schedules compared to manual scheduling.



Methods: Data was collected retrospectively for a mid-sized hospital performing elective orthopaedic surgery (majority hip and knee arthroplasty). Data included operative time, overtime, and number of OR days. BPO model was used to simulate an optimized scheduled and compared to the historic manual schedule by comparing the number of OR days required to complete the same surgical volume. The primary outcome was the total number of OR days required.



Results: All surgeons experienced a median 10% reduction (range: 9.2%–18.3%) in OR days required using BPO schedules (mean 183 days) versus historical scheduling (mean 207 days). Daily OR configurations differed from historical ones in 87% of cases, and the number of configurations per surgeon dropped from an average of six to four.



Discussion: BPO increased OR throughput by 10% without increasing operative time. This suggests potential for reduced costs, shorter wait lists, and improved surgeon satisfaction for hip and knee arthroplasty. Future work should incorporate predictive modeling using patient and procedure-specific data.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{ChanTCY.J135,

title = {Feasibility and optimization of a second-tier prehospital critical care response for major trauma in a North American urban and suburban area: A geospatial analysis and modelling study},

author = {R. Stephenson and V. Sarhangian and S. Cheskes and L. Turner and B. Nolan and I. Drennan and T. C. Y. Chan and J. von Vopelius-Feldt},

doi = {10.1016/j.ajem.2025.07.025},

year  = {2025},

date = {2025-07-09},

urldate = {2025-07-09},

journal = {American Journal of Emergency Medicine},

volume = {97},

pages = {35-44},

abstract = {Objective

Prehospital Critical Care Response Units (CCRUs) dispatched to the scene of major traumas can deliver advanced interventions at scene but are uncommon in North America. We sought to evaluate the feasibility of CCRU response to major trauma in a North American urban-suburban region.



Methods

We obtained ambulance record-level data from three paramedic services in Ontario, Canada (Toronto Paramedic Service, Peel Regional Paramedic Service, and Halton Region Paramedic Service) from January 2018 to December 2022 which we aggregated into calls and applied inclusion criteria targeting major trauma. We used mathematical modelling to determine the optimal placement of CCRU bases containing an RRV or RRV/helicopter for trauma response and evaluated their expected counterfactual coverage performance using simulation. Our primary metrics were the expected number of major traumas that could have been reached by CCRUs prior to EMS departure from the scene and the resulting expected average reduction in time to accessing critical care for those patients.



Results

We found the expected counterfactual coverage of two optimally placed RRV teams to be 80 % (N = 5092) of 6391 major trauma calls included. This corresponded to an expected average reduction in time to critical care of 30 min (from 47 to 17 min). We found only marginal improvement in total calls reached by CCRUs when an RRV team was replaced with an RRV/helicopter team.



Conclusions

Our analysis supports the feasibility of CCRU response to major trauma in a North American mixed urban-suburban region and motivates further investigation into CCRUs' clinical and cost effectiveness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J132,

title = {Optimizing placement of public-access naloxone kits using geospatial analytics: A modelling study},

author = {K. H. B. Leung and B. E. Grunau and M. K. Lee and J. A. Buxton and J. Helmer and S. van Diepen and J. Christenson and T. C. Y. Chan},

doi = {10.1503/cmaj.241228},

year  = {2025},

date = {2025-02-05},

urldate = {2025-02-05},

journal = {CMAJ},

volume = {197},

number = {10},

pages = {E258-E265},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J0130,

title = {Dynamic control of service systems with returns: Application to design of postdischarge hospital readmission prevention programs},

author = {T. C. Y. Chan and S. Y. Huang and V. Sarhangian},

doi = {10.1287/opre.2022.0066},

year  = {2025},

date = {2025-01-10},

urldate = {2025-01-10},

journal = {Operations Research},

volume = {73},

number = {4},

pages = {2242-2263},

abstract = {We study a control problem for queueing systems where customers may return for additional episodes of service after their initial service completion. At each service completion epoch, the decision maker can choose to reduce the probability of return for the departing customer but at a cost that is convex increasing in the amount of reduction in the return probability. Other costs are incurred as customers wait in the queue and every time they return for service. Our primary motivation comes from post-discharge Quality Improvement (QI) interventions (e.g., follow up phone-calls, appointments) frequently used in a variety of healthcare settings to reduce unplanned hospital readmissions. Our objective is to understand how the cost of interventions should be balanced with the reductions in congestion and service costs. To this end, we consider a fluid approximation of the queueing system and characterize the structure of optimal long-run average and bias-optimal transient control policies for the fluid model. Our structural results motivate the design of intuitive surge protocols whereby different intensities of interventions (corresponding to different levels of reduction in the return probability) are provided based on the congestion in the system. Through extensive simulation experiments, we study the performance of the fluid policy for the stochastic system and identify parameter regimes where it leads to significant cost savings compared to a fixed long-run average optimal policy that ignores holding costs and a simple policy that uses the highest level of intervention whenever the queue is non-empty. In particular, we find that in a parameter regime relevant to our motivating application, dynamically adjusting the intensity of interventions could result in up to 25.4% reduction in long-run average cost and 33.7% in finite-horizon costs compared to the simple aggressive policy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J0127,

title = {Got (optimal) milk? Pooling donations in human milk banks with machine learning and optimization},

author = {T. C. Y. Chan and R. Mahmood and D. L. O'Connor and D. Stone and S. Unger and R. K. Wong and I. Y. Zhu},

doi = {10.1287/msom.2022.0455},

year  = {2025},

date = {2025-01-05},

urldate = {2025-01-05},

journal = {Manufacturing & Service Operations Management},

volume = {26},

number = {6},

pages = {1721-1739},

abstract = {Problem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions.

Methodology/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time.

Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J0123,

title = {Evaluation of a nurse practitioner-led post-discharge transitional care program for patients with liver disease: A retrospective cohort study},

author = {M. Naghshbandi and S. Y. Huang and H. Shah and C. Yim and E. Lee and M. Caesar and A. Hope and T. C. Y. Chan and V. Sarhangian},

doi = {10.3138/canlivj-2024-0020},

year  = {2024},

date = {2024-08-25},

urldate = {2024-08-25},

journal = {Canadian Liver Journal},

volume = {7},

number = {4},

pages = {427-438},

abstract = {Background: We evaluated an outpatient, Nurse Practitioner (NP)-led transitional care program with respect to its efficacy in reducing unplanned readmission rates for liver disease patients.



Methods: We conducted a retrospective cohort study using data from an academic health system in Toronto, Ontario. The study included 803 admissions associated with an ICD10 code of R18 (ascites), I85.0 or I98.3 (variceal bleeding), or K70 K77 range (diseases of liver including hepatic encephalopathy). Patients were selected to receive the transitional care (intervention group) or not (no-intervention group) by discretion of the hepatologists. We used the competing risk proportional hazard model to estimate the associations between receiving the intervention, and marginal probability of 30-, 60-, and 90-day readmission in presence of death as a competing risk. We conducted sensitivity analyses to examine the robustness of our estimates to various sources of bias, including adjusting for propensity of receiving the intervention estimated using a logistic regression model.



Results: The intervention was associated with 49% reduction in risk of 30-day readmission (HR: 0.51, 95% CI, 0.30-0.85), 40% reduction in risk of 60-day readmission (HR:0.60, 95% CI, 0.40- 0.91), and 45% reduction in risk of 90-day readmission (HR:0.55, 95% CI, 0.37-0.83). The negative associations remained statistically significant under the sensitivity analyses, except for the propensity adjusted estimate for the 60-day outcome.



Conclusions: A NP-led transitional-care program could be effective in reducing the risk of readmission for liver disease patients. Future studies are needed to standardize the referral process and prospectively measure the effectiveness and financial value of the program.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J0122,

title = {Centre-specific variation in atrial fibrillation ablation rates in a universal single-payer health care system},

author = {C. Seo and S. Kushwaha and P. Angaran and P. Gozdyra and K. S. Allan and H. Abdel-Qadir and P. Dorian and T. C. Y. Chan},

doi = {10.1016/j.cjco.2024.08.008},

year  = {2024},

date = {2024-08-19},

urldate = {2024-08-19},

journal = {CJC Open},

volume = {6},

number = {11},

pages = {1355-1362},

abstract = {Background

Disparities in atrial fibrillation ablation rates have been previously studied, with focuses on patient characteristics and systems factors rather than geographic factors. The impact of electrophysiology centre practice patterns on ablation rates have not been well studied.



Methods

This was a population-based cohort study using linked administrative datasets covering physician billing codes, hospitalizations, prescriptions, and census data. The study population consisted of patients visiting an emergency department with a new diagnosis of atrial fibrillation between 2007-2016, in Ontario, Canada. Patient characteristics, including age, sex, medical history, comorbidities, socioeconomic factors, closest  electrophysiology centre within 20 km, and distance to nearest centre were used as predictors in multivariable logistic regression models to assess the relationship between living around specific electrophysiology centres and ablation rates.



Results

The cohort included 134,820 patients of whom 9,267 had an ablation during the study period. Patients undergoing ablation were younger, had a lower CHADS2 score, lived closer to electrophysiology centres, and had fewer comorbidities than people who did not receive ablation. There was wide variation in ablation rates, with adjacent census divisions having up to 2.6 times higher ablation rate. Multivariate regression revealed significant differences in ablation rates for patients living around certain electrophysiology centres. The odds ratios for living closest to specific centres ranged from 0.78 (95% CI: 0.68-0.89) to 1.60 (95% CI:1.34-1.90).



Conclusions

Living near specific electrophysiology centres may significantly affect a patient’s likelihood of ablation, regardless of factors such as age, gender, socioeconomic status, prior medical history, and distance to electrophysiology centres.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J0121,

title = {Disparities in surgery rates during the COVID-19 pandemic: Retrospective study},

author = {A. Sankar and T. A. Stukel and N. N. Baxter and D. N. Wijeysundera and S. W. Hwang and A. S. Wilton and T. C. Y. Chan and V. Sarhangian and A. N. Simpson and C. de Mestral and D. Pincus and R. J. Campbell and D. R. Urbach and J. Irish and D. Gomez},

doi = {10.1093/bjsopen/zrae088},

year  = {2024},

date = {2024-07-03},

urldate = {2024-07-03},

journal = {BJS Open},

volume = {8},

number = {4},

pages = {zrae088},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J0109,

title = {Evolution of the surgical procedure gap during and after the COVID-19 pandemic in Ontario, Canada: Cross-sectional and modeling study},

author = {R. Stephenson and V. Sarhangian and J. Park and A. Sankar and N. N. Baxter and T. A. Stukel and A. N. Simpson and D. N. Wijeysundera and A. S. Wilton and C. de Mestral and S. W. Hwang and D. Pincus and R. J. Campbell and D. R. Urbach and J. Irish and D. Gomez and T. C. Y. Chan},

doi = {10.1093/bjs/znad289},

year  = {2023},

date = {2023-08-14},

journal = {British Journal of Surgery},

volume = {110},

number = {12},

pages = {1887-1889},

abstract = {During the COVID-19 pandemic, many countries faced significant reductions in surgical capacity, leading to unprecedented surgical backlogs, or procedure gaps. This study developed a framework to estimate procedure gaps and project their future evolution. The framework was applied to data from Ontario, Canada to provide policy insights for a fair and effective surgical recovery. This study updates previous work that estimated the impacts of COVID on surgery rates and future surgical recovery by providing a more recent estimate, and adding modeling of major ongoing COVID impacts. In reality, COVID and its downstream effects have continued to impact healthcare systems into 2023, and can be reasonably expected to continue into the future.



Population-based weekly surgery count data was obtained for all scheduled adult surgical procedures in Ontario between January 1, 2017 and June 25, 2022, grouped by inpatient/outpatient and body system. Negative Binomial regression was used to estimate the expected size of the procedure gaps as of June 25, 2022, and Monte Carlo simulation was used to estimate their evolution over 10 years under future COVID and surgical capacity-increase scenarios (Figure S1). 



As of June 25, 2022, the total outpatient and inpatient procedure gaps were estimated to be 214 925 (95% CI 207 281-222 569) and 99 232 (95% CI 96 856-101 609), respectively (Table 1). Assuming no future impacts of COVID and 10% or 20% increases in surgical capacity, all procedure gaps were estimated to clear within 10 years. However, under scenarios in which COVID impacts persist, with a 0% or 10% increase in surgical capacity, no procedure gaps were expected to clear within 10 years. With a 20% increase, only three procedure gaps were expected to clear; several other gaps were expected to grow. 



Our results highlight the heterogeneous impact of the pandemic on different procedure groups. These differences are apparent in the growth of procedure gaps over the pandemic (Figure S4), and in their forecast evolution. For example, the two largest outpatient procedure gaps (Eye and Musculoskeletal), which have been the subject of pre-pandemic prioritization through added capacity and volume-based funding models, make up almost half of the total outpatient procedure gap. However, even if COVID impacts persist, their forecasted gaps are expected to drop significantly with a 10% increase in surgical capacity (Table 1). In contrast, the estimated inpatient Gynecology gap is currently much smaller, but even with a 20% increase in capacity, the gap is expected to more than double by 2032 (Table 1).



On February 2, 2023, the Ontario government released a plan to significantly increase cataract surgeries and hip and knee replacements through the use of for-profit centres, but without a clear plan to increase overall surgical capacity in hospitals. Our results suggest that other procedure groups (e.g., Gynecology and Otolaryngology) require targeted increases in surgical capacity, especially if those groups are predominantly funded through global hospital budgets. To avoid unfair patient experiences like extensive wait times, targeted investments considering both the current procedure gaps and their future evolution are necessary for surgical recovery plans that strike a balance between efficiency and equity of clearing the procedure gaps.



This study has two key takeaways. First, small increases in overall surgical capacity will have little impact on clearing the surgical procedure gap in the near term. Second, capacity increases should be targeted by considering not only current procedure gaps, but also their forecasted evolution. Indeed, procedure groups with the largest gaps currently may not be the ones most in need of increased capacity investments. The developed framework can be applied to other jurisdictions to provide insights for design of robust surgical recovery plans.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J0108,

title = {Constrained optimization for decision making in healthcare using Python: A tutorial},

author = {K. H. B. Leung and N. Yousefi and T. C. Y. Chan and A. M. Bayoumi},

doi = {10.1177/0272989X231188027},

year  = {2023},

date = {2023-06-11},

journal = {Medical Decision Making},

volume = {43},

number = {7-8},

pages = {760-773},

abstract = {Constrained optimization can be used to make decisions aimed at maximizing some quantity in the face of fixed limits, such as resource allocation problems in health where tradeoffs between alternatives are inherent, and has been applied in a variety of health-related applications. This tutorial guides the reader through the process of mathematially formulating a constrained optimization problem, providing intuitive explanations for each component within the problem. We discuss how constrained optimization problems can be implemented using software and provide instructions on how to set up a solution environment using Python and the Gurobi solver engine. We present 2 examples from the existing literature that illustrate different constrained optimization problems in health and provide the reader with Python code used to solve these problems as well as a discussion of results and sensitivity analyses. This tutorial can be used to help readers formulate constrained optimization problems in their own application domains.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J104,

title = {Priorities for artificial intelligence in primary care: A Canadian deliberative dialogue with patients, providers, and health system leaders},

author = {T. L. Upshaw and A. Craig-Neil and J. Macklin and C. Steele Gray and T. C. Y. Chan and J. Gibson and A. D. Pinto},

doi = {10.3122/jabfm.2022.220171R1},

year  = {2023},

date = {2023-01-07},

journal = {Journal of the American Board of Family Medicine},

volume = {36},

number = {2},

pages = {210-220},

abstract = {BACKGROUND: Artificial intelligence (AI) implementation in primary care is limited. Those set to be most impacted by AI technology in this setting should guide the application. We organized a national deliberative dialogue with primary care stakeholders from across Canada to explore how they thought AI should be applied in primary care.



METHODS: We conducted 12 virtual deliberative dialogues with 22 patients, 21 interprofessional primary care providers, and 5 health system leaders from eight Canadian provinces to identify shared priorities for applying AI in primary care. Dialogue data were thematically analyzed using interpretive description approaches.



RESULTS: Participants thought that AI should be first applied in primary care to documentation, practice operations, and triage tasks, in hopes of improving efficiency while maintaining person-centred delivery, relationships, and access. They viewed complex AI-driven clinical decision support and proactive care tools as impactful but recognized potential risks to patient safety, given numerous external limitations. Appropriate training and implementation support were the most important external enablers of safe, effective, and patient-centred use of AI in primary care settings.



INTERPRETATION: Our findings offer an agenda for the future application of AI in primary care that is grounded in the shared values of patients and providers. We propose that, from conception, AI developers should work with primary care stakeholders as co-design partners, developing tools that respond to their shared priorities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J100,

title = {Strategies for lung- and diaphragm-protective ventilation in acute hypoxemic respiratory failure: A physiological trial},

author = {J. Dianti and S. Fard and J. Wong and T. C. Y. Chan and L. Del Sorbo and E. Fan and M. B. Passos Amato and J. Granton and L. Burry and W. D. Reid and B. Zhang and D. Ratano and S. Keshavjee and A. S. Slutsky and L. J. Brochard and N. D. Ferguson and E. C. Goligher},

doi = {10.1186/s13054-022-04123-9},

year  = {2022},

date = {2022-08-08},

journal = {Critical Care},

volume = {26},

number = {Article No. 259},

abstract = {Background: Insufficient or excessive respiratory effort during acute hypoxemic respiratory failure (AHRF) increases the risk of lung and diaphragm injury. We sought to establish whether respiratory effort can be optimized to achieve lung- and diaphragm-protective (LDP) targets (esophageal pressure swing − 3 to − 8 cm H2O; dynamic transpulmonary driving pressure ≤ 15 cm H2O) during AHRF.



Methods: In patients with early AHRF, spontaneous breathing was initiated as soon as passive ventilation was not deemed mandatory. Inspiratory pressure, sedation, positive end-expiratory pressure (PEEP), and sweep gas flow (in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO)) were systematically titrated to achieve LDP targets. Additionally, partial neuromuscular blockade (pNMBA) was administered in patients with refrac- tory excessive respiratory effort.



Results: Of 30 patients enrolled, most had severe AHRF; 16 required VV-ECMO. Respiratory effort was absent in all at enrolment. After initiating spontaneous breathing, most exhibited high respiratory effort and only 6/30 met LDP targets. After titrating ventilation, sedation, and sweep gas flow, LDP targets were achieved in 20/30. LDP targets were more likely to be achieved in patients on VV-ECMO (median OR 10, 95% CrI 2, 81) and at the PEEP level associated with improved dynamic compliance (median OR 33, 95% CrI 5, 898). Administration of pNMBA to patients with refractory excessive effort was well-tolerated and effectively achieved LDP targets.



Conclusion: Respiratory effort is frequently absent under deep sedation but becomes excessive when spontaneous breathing is permitted in patients with moderate or severe AHRF. Systematically titrating ventilation and sedation can optimize respiratory effort for lung and diaphragm protection in most patients. VV-ECMO can greatly facilitate the delivery of a LDP strategy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J099,

title = {Inverse optimization on hierarchical networks: An application to breast cancer clinical pathways},

author = {T. C. Y. Chan and K. Forster and S. Habbous and C. Holloway and L. Ieraci and Y. Shalaby and N. Yousefi},

doi = {10.1007/s10729-022-09599-z},

year  = {2022},

date = {2022-05-12},

journal = {Health Care Management Science},

volume = {25},

pages = {590-622},

abstract = {Clinical pathways are standardized processes that outline the steps required for managing a specific disease. However, patient pathways often deviate from clinical pathways. Measuring the concordance of patient pathways to clinical pathways is important for health system monitoring and informing quality improvement initiatives. In this paper, we develop an inverse optimization-based approach to measuring pathway concordance in breast cancer, a complex disease. We capture this complexity in a hierarchical network that models the patient’s journey through the health system. A novel inverse shortest path model is formulated and solved on this hierarchical network to estimate arc costs, which are used to form a concordance metric to measure the distance between patient pathways and shortest paths (i.e., clinical pathways). Using real breast cancer patient data from Ontario, Canada, we demonstrate that our concordance metric has a statistically significant association with survival for all breast cancer patient subgroups. We also use it to quantify the extent of patient pathway discordances across all subgroups, finding that patients undertaking additional clinical activities constitute the primary driver of discordance in the population.},

howpublished = {under review for Health Care Management Science},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J098,

title = {An anesthesia block room is financially net positive for a hospital performing arthroplasty},

author = {J. Toor and I. Saleh and A. Abbas and J. Abouali and P. Wong and T. C. Y. Chan and V. Sarhangian},

doi = {10.5435/JAAOS-D-21-01217},

year  = {2022},

date = {2022-03-17},

journal = {Journal of the American Academy of Orthopaedic Surgeons},

volume = {30},

number = {15},

pages = {e1058-e1065},

abstract = {Introduction:  Regional anesthesia is increasingly used in total joint arthroplasty (TJA). It has shown efficiency benefits as it allows parallel processing of patients in a dedicated block room (BR). However, granular quantification of these benefits to hospital operations is lacking. The goal of this study was to determine the financial effect of establishing a BR using comprehensive operational modeling.



Methods:  A discrete-event simulation model of daily operating room (OR) patient flow for TJA procedures at a mid-sized hospital was developed. Two scenarios were tested: (1) without and (2) with a BR. Scenarios were compared according to staffing requirements, hours/day, and labor costs. The number of ORs and cases varied from 2 to 6 ORs performing 3 to 5 cases. These results were used as the inputs of a discounted cash flow (CF) model. Discounted CF model outputs were CF, net present value, internal rate of return, and return on investment.



Results:  Mean time savings of incorporating a BR were 68 min/d (range: 30 to 80 min/d), reducing the OR closing time by 1 hour. Incremental labor costs/day from nurse overtime pay ranged from $2,025 to $10,125 with no BR and $1,595 to $9,045 with a BR, which resulted in an increase in profit/day from $360 to $1,605. The CF/annum was $54,363, the net present value was $213,082, the internal rate of return was 12%, and the return on investment was 43.61%.



Discussion:  This study demonstrates that under all scenarios, a BR is more profitable than no BR to a hospital performing TJA via a bundled care or private payer remuneration model. A BR was shown to be financially net positive even when considering the necessary financial investment to establish it. In addition, this study demonstrates the potential of combining discrete-event simulation with financial analyses to assess various operational models of care to improve hospital efficiency, such as dedicated trauma rooms and swing rooms.



Level of evidence:  Level III},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J096,

title = {Drone network design for cardiac arrest response},

author = {J. J. Boutilier and T. C. Y. Chan},

doi = {10.1287/msom.2022.1092},

year  = {2022},

date = {2022-02-08},

journal = {Manufacturing and Service Operations Management},

volume = {24},

number = {5},

pages = {2407-2424},

abstract = {Problem definition: Our objective is to design a defibrillator-enabled drone network that augments the existing emergency medical services (EMS) system to rapidly respond to out-of-hospital cardiac arrest (OHCA).



Academic/practical relevance: OHCA claims more than 400,000 lives each year in North America and is one of the most time-sensitive medical emergencies. Drone-delivered automated external defibrillators (AEDs) have the potential to be a transformative innovation in the provision of emergency care for OHCA.



Methodology: We develop an integrated location-queuing model that incorporates existing EMS response times and is based on the p-median architecture, where each base constitutes an explicit M/M/d/d queue (i.e., Erlang loss). We then develop a reformulation technique that exploits the existing EMS response times, allowing us to solve real-world instances to optimality using an off-the-shelf solver. We evaluate our solutions using a tactical simulation model that accounts for the effects of congestion and dispatching, and we use a machine-learning model to translate our response-time reductions into survival estimates.



Results:Using real data from an area covering 26,000 square kilometers around Tor-onto, Canada, we find that a modest number of drones are required to significantly reduce response times in all regions. An objective function that minimizes average response time results in drone resources concentrated in cities, with little impact on the tail of the distribution. In contrast, optimizing for the tail of the response-time distribution produces larger and more geographically dispersed drone networks that improve response-time equity across the regions. We estimate that the response-time reductions achieved by the drone network are associated with between a 42% and 76% higher survival rate and up to 144 additional lives saved each year across the geographical region we consider.



Managerial implications: Overall, this paper provides a realistic framework that can be leveraged by system designers and/or EMS personnel seeking to investigate design questions associated with a drone network. An objective function focused on improving the tail of the response-time distribution is well-suited for use in practice because the model provides equitable solutions that reduce the entire response-time distribution and corresponds to the real-world metrics, on which EMS systems are most commonly evaluated.},

howpublished = {under third review for Manufacturing and Service Operations Management},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J092,

title = {Outcomes in patients with and without disability admitted to hospital with COVID-19: A retrospective cohort study},

author = {H. K. Brown and S. Saha and T. C. Y. Chan and A. M. Cheung and M. Fralick and M. Ghassemi and M. Herridge and J. Kwan and S. Rawal and L. Rosella and T. Tang and A. Weinerman and Y. Lunsky and F. Razak and A. A. Verma},

doi = {10.1503/cmaj.211277},

year  = {2022},

date = {2022-01-07},

journal = {Canadian Medical Association Journal},

volume = {194},

pages = {E112-121},

abstract = {Background: Disability-related considerations have largely been absent from the COVID-19 response, despite evidence that people with disabilities are at elevated risk for acquiring COVID-19. We evaluated clinical outcomes in patients who were admittedto hospital with COVID-19 with a disability compared with patients without a disability.



Methods: We conducted a retrospective cohort study that included adults with COVID-19 who were admitted to hospital and discharged between Jan. 1, 2020, and Nov. 30, 2020, at 7  hospitals in Ontario, Canada. We compared in-hospital death, admission to the intensive care unit (ICU), hospital length of stay and unplanned 30-day readmission among patients with and without a physical disability, hearing or vision impairment, traumatic brain injury, or intellectual or developmental disability, overall and stratified by age (≤ 64 and ≥ 65 yr) using multivariable regression, controlling for sex, residence in a long-term care facility and comorbidity.



Results: Among 1279 admissions to hospital for COVID-19, 22.3% had a disability. We found that patients with a

disability were more likely to die than those without a disability (28.1% v. 17.6%), had longer hospital stays (median 13.9 v. 7.8  d) and more readmissions (17.6% v. 7.9%), but had lower ICU admission rates (22.5% v. 28.3%). After adjustment, there were no statistically significant differences between those with and without disabilities for in-hospital death or admission to ICU. After adjustment, patients with a disability had longer hospital stays (rate ratio 1.36, 95% confidence interval [CI] 1.19–1.56) and greater risk of readmission (relative risk  1.77, 95% CI 1.14–2.75). In age-stratified analyses, we observed longer hospital stays among patients with a disability than in those without, in both younger and older subgroups; readmission risk was driven by younger patients with a disability.



Interpretation: Patients with a disability who were admitted to hospital with COVID-19 had longer stays and elevated readmission risk than those without disabilities. Disability-related needs should be addressed to support these patients in hospital and after discharge.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J091,

title = {Sparse flexible design: A machine learning approach},

author = {T. C. Y. Chan and D. Letourneau and B. Potter},

doi = {10.1007/s10696-021-09439-2},

year  = {2022},

date = {2022-01-06},

journal = {Flexible Services and Manufacturing Journal},

volume = {34},

pages = {1066-1116},

abstract = {For a general production network, state-of-the-art methods for constructing sparse flexible designs are heuristic in nature, typically computing a proxy for the quality of unseen networks and using that estimate in a greedy manner to modify a current design. This paper develops two machine learning-based approaches to constructing sparse flexible designs that leverage a neural network to accurately and quickly predict the performance of large numbers of candidate designs. We demonstrate that our heuristics are competitive with existing approaches and produce high-quality solutions for both balanced and unbalanced networks. Finally, we introduce a novel application of process flexibility in healthcare operations to demonstrate the effectiveness of our approach in a large numerical case study. We study the flexibility of linear accelerators that deliver radiation to treat various types of cancer. We demonstrate how clinical constraints can be easily absorbed into the machine learning subroutine and how our sparse flexible treatment networks meet or beat the performance of those designed by state-of-the-art methods.},

howpublished = {Flexible Services and Manufacturing Journal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J088,

title = {An inverse optimization approach to measuring clinical pathway concordance},

author = {T. C. Y. Chan and M. Eberg and K. Forster and C. Holloway and L. Ieraci and Y. Shalaby and N. Yousefi},

doi = {10.1287/mnsc.2021.4100},

year  = {2022},

date = {2022-01-03},

journal = {Management Science},

volume = {68},

pages = {1882-1903},

abstract = {Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, however, can deviate substantially from these pathways. Given the positive benefits of clinical pathways, it is important to measure the concordance of patient pathways so that variations in health system performance or bottlenecks in the delivery of care can be detected, monitored, and acted upon. This paper proposes the first data-driven inverse optimization approach to measuring pathway concordance in any problem context. Our specific application considers clinical pathway concordance for stage III colon cancer. We develop a novel concordance metric and demonstrate using real patient data from Ontario, Canada that it has a statistically significant association with survival. Our methodological approach considers a patient’s journey as a walk in a directed graph, where the costs on the arcs are derived by solving an inverse shortest path problem. The inverse optimization model uses two sources of information to find the arc costs: reference pathways developed by a provincial cancer agency (primary) and data from real-world patient-related activity from patients with both positive and negative clinical outcomes (secondary). Thus, our inverse optimization framework extends existing models by including data points of both varying “primacy” and “alignment.” Data primacy is addressed through a two-stage approach to imputing the cost vector, whereas data alignment is addressed by a hybrid objective function that aims to minimize and maximize suboptimality error for different subsets of input data.},

howpublished = {under third review at Management Science},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J081,

title = {Characteristics and outcomes of hospital admissions for COVID-19 and influenza in the Toronto area},

author = {A. A. Verma and T. Hora and H. Y. Jung and M. Fralick and S. L. Malecki and L. Lapointe-Shaw and A. Weinerman and T. Tang and J. L. Kwan and J. J. Liu and S. Rawal and T. C. Y. Chan and A. M. Cheung and L. C. Rosella and M. Ghassemi and M. Herridge and M. Mamdani and F. Razak},

doi = {10.1503/cmaj.202795},

year  = {2021},

date = {2021-01-28},

journal = {Canadian Medical Association Journal},

volume = {193},

number = {22},

pages = {E410-E418},

abstract = {BACKGROUND: Patient characteristics, clinical care, resource use and out- comes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described.



METHODS: We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical–surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed

risk scores to predict mortality among patients with COVID-19.



RESULTS: There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56–4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25–1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25– 1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70– 1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration.



INTERPRETATION: During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in- hospital mortality in patients with COVID-19 with good accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J071,

title = {Modelling resource requirements and physician staffing to provide virtual urgent medical care for residents of long-term care homes: A cross-sectional study},

author = {F. Razak and S. Shin and F. Pogacar and H. Y. Jung and L. Pus and A. Moser and L. Lapointe-Shaw and T. Tang and J. L. Kwan and A. Weinerman and S. Rawal and V. Kushnir and D. Mak and D. Martin and K. G. Shojania and S. Bhatia and P. Agarwal and G. Mukerji and M. Fralick and M. K. Kapral and M. Morgan and B. Wong and T. C. Y. Chan and A. A. Verma},

doi = {10.9778/cmajo.20200098},

year  = {2020},

date = {2020-08-20},

journal = {CMAJ Open},

volume = {8},

pages = {E514-E521},

abstract = {Background: The coronavirus disease 2019 (COVID-19) outbreak increases the importance of strategies to enhance urgent medical care delivery in long-term care (LTC) facilities that could potentially reduce transfers to emergency departments. The study objective was to model resource requirements to deliver virtual urgent medical care in LTC facilities.



Methods: We used data from all general medicine inpatient admissions at 7 hospitals in the Greater Toronto Area, Ontario, Canada, over a 7.5-year period (Apr. 1, 2010, to Oct. 31, 2017) to estimate historical patterns of hospital resource use by LTC residents. We estimated an upper bound of potentially avoidable transfers by combining data on short admissions (≤ 72 h) with historical data on the proportion of transfers from LTC facilities for which patients were discharged from the emergency department without admission. Regression models were used to extrapolate future resource requirements, and queuing models were used to estimate physician staffing requirements to perform virtual assessments.



Results: There were 235 375 admissions to general medicine wards, and residents of LTC facilities (age 16 yr or older) accounted for 9.3% (n = 21 948) of these admissions. Among the admissions of residents of LTC facilities, short admissions constituted 24.1% (n = 5297), and for 99.8% (n = 5284) of these admissions, the patient received laboratory testing, for 86.9% (n = 4604) the patient received plain radiography, for 41.5% (n = 2197) the patient received computed tomography and for 81.2% (n = 4300) the patient received intravenous medications. If all patients who have short admissions and are transferred from the emergency department were diverted to outpatient care, the average weekly demand for outpatient imaging per hospital would be 2.6 ultrasounds, 11.9 computed tomographic scans and 23.9 radiographs per week. The average daily volume of urgent medical virtual assessments would range from 2.0 to 5.8 per hospital. A single centralized virtual assessment centre staffed by 2 or 3 physicians would provide services similar in efficiency (measured by waiting time for physician assessment) to 7 separate centres staffed by 1 physician each.



Interpretation: The provision of acute medical care to LTC residents at their facility would probably require rapid access to outpatient diagnostic imaging, within-facility access to laboratory services and intravenous medication and virtual consultations with physicians. The results of this study can inform efforts to deliver urgent medical care in LTC facilities in light of a potential surge in COVID-19 cases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J060,

title = {Ambulance Emergency Response Optimization in developing countries},

author = {J. J. Boutilier and T. C. Y. Chan},

doi = {10.1287/opre.2019.1969},

year  = {2020},

date = {2020-01-08},

journal = {Operations Research},

volume = {68},

pages = {1315-1334},

abstract = {The lack of emergency medical transportation is viewed as the main barrier to the access and availability of emergency medical care in low- and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique data sets that inform our approach. These data are leveraged to estimate demand for emergency medical services in an LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our prediction-optimization framework with a simulation model and real data to provide an in-depth investigation into three policy- related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that the performance of the current system could be replicated using one third of the current outpost locations and one half of the current number of ambulances. Finally, we show that a fleet of small ambulances has the potential to significantly outperform tra- ditional ambulance vans. In particular, they are able to capture approximately three times more demand while reducing the median average response time by roughly 10%–18% over the entire week and 24%–35% during rush hour because of increased routing flexibility offered by more nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in developing countries.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ChanTCY.J042,

title = {Women's College Hospital uses Operations Research to create an ambulatory clinic schedule},

author = {B. K. Eagen and T. C. Y. Chan and M. W. Carter},

doi = {10.1287/serv.2018.0221},

year  = {2018},

date = {2018-04-01},

journal = {Service Science},

volume = {10},

pages = {230-240},

abstract = {Women's College Hospital (WCH) in Toronto, Canada, offers roughly 300 outpatient clinics every week. In April 2011, we started working with WCH to design a new schedule for their clinics, to accommodate a move to a new hospital building that was completed in May 2013. We developed an integer programming model to optimize the assignment of clinics to timeslots and locations, based on the desire to minimize changes from the historical schedule. In cooperation with senior leadership, we tested multiple scenarios that explored changes to space utilization policies at WCH and ultimately generated a new clinic schedule, which they implemented in May 2013. In this paper, we highlight the value our work has created for WCH and present the lessons we learned in development of the model and through our collaboration with the WCH team.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}