Publications Database
Welcome to the new Schulich Peer-Reviewed Publication Database!
The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:
- Faculty Member’s Name;
- Area of Expertise;
- Whether the Publication is Open-Access (free for public download);
- Journal Name; and
- Date Range.
At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.
If you have any questions or input, please don’t hesitate to get in touch.
Search Results
Adam Diamant, Anton Schevchenko, David Johnston, Fayez Quereshy (Forthcoming). "Consecutive Surgeries With Complications: The Impact of Scheduling Decisions", International Journal of Operations & Production Management.
Abstract
Purpose The authors determine how the scheduling and sequencing of surgeries by surgeons impacts the rate of post-surgical complications and patient length-of-stay in the hospital. Design/methodology/approach Leveraging a dataset of 29,169 surgeries performed by 111 surgeons from a large hospital network in Ontario, Canada, the authors perform a matched case-control regression analysis. The empirical findings are contextualized by interviews with surgeons from the authors’ dataset. Findings Surgical complications and longer hospital stays are more likely to occur in technically complex surgeries that follow a similarly complex surgery. The increased complication risk and length-of-hospital-stay is not mitigated by scheduling greater slack time between surgeries nor is it isolated to a few problematic surgery types, surgeons, surgical team configurations or temporal factors such as the timing of surgery within an operating day. Research limitations/implications There are four major limitations: (1) the inability to access data that reveals the cognition behind the behavior of the task performer and then directly links this behavior to quality outcomes; (2) the authors’ definition of task complexity may be too simplistic; (3) the authors’ analysis is predicated on the fact that surgeons in the study are independent contractors with hospital privileges and are responsible for scheduling the patients they operate on rather than outsourcing this responsibility to a scheduler (i.e. either a software system or an administrative professional); (4) although the empirical strategy attempts to control for confounding factors and selection bias in the estimate of the treatment effects, the authors cannot rule out that an unobserved confounder may be driving the results. Practical implications The study demonstrates that the scheduling and sequencing of patients can affect service quality outcomes (i.e. post-surgical complications) and investigates the effect that two operational levers have on performance. In particular, the authors find that introducing additional slack time between surgeries does not reduce the odds of back-to-back complications. This result runs counter to the traditional operations management perspective, which suggests scheduling more slack time between tasks may prevent or mitigate issues as they arise. However, the authors do find evidence suggesting that the risk of back-to-back complications may be reduced when surgical pairings are less complex and when the method involved in performing consecutive surgeries varies. Thus, interspersing procedures of different complexity levels may help to prevent poor quality outcomes. Originality/value The authors empirically connect choices made in scheduling work that varies in task complexity and to patient-centric health outcomes. The results have implications for achieving high-quality outcomes in settings where professionals deliver a variety of technically complex services.Onder, O., Cook, W., Kristal, M.M. (2021). "Does Quality Help the Financial Viability of Hospitals? A Data Envelopment Analysis Approach", Socio-Economic Planning Sciences.
Abstract
In this work, we analyze the financial viability of U.S. hospitals by investigating the impact of clinical and experiential quality as its determinants. We adopt Simar and Wilson's two-stage bootstrapped truncated regression approach. Specifically, we use data envelopment analysis (DEA) in the first stage to estimate efficiency scores. Then, we use truncated regression estimation with the double-bootstrap method to test the significance of the quality variables. Given the financial problems recently experienced by U.S. hospitals, we use readmission rates and costs as our outputs to investigate how well hospitals can lower readmission rates while minimizing their costs, since recent policy changes have tied a portion of hospital reimbursements to their readmission rates, making both variables crucial outcome goals. We find that both clinical and experiential quality are significantly associated with the higher financial viability of hospitals. Further, focusing on these two quality dimensions together has additional benefits.Adam Diamant (2021). "Dynamic Multistage Scheduling for Patient-Centered Care Plans", Health Care Management Science , 24(2021), 827-84.
Keywords
Abstract
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. We then formulate an approximate dynamic program (ADP) and implement a dual variable aggregation procedure. This reduces the size of the ADP while still producing dual cost estimates that can be used to identify favorable scheduling actions. We use our scheduling model to study the effectiveness of customized-care plans for a heterogeneous patient population and find that system performance is better than clinics that do not offer such plans. We also demonstrate that our scheduling approach improves clinic profitability, increases throughput, and decreases practitioner idleness as compared to a policy that mimics human schedulers and a policy derived from a deep neural network. Finally, we show that our approach is fairly robust to errors introduced when practitioners inadvertently assign patients to the wrong care plan.Diamant, A., Johnston, D. and Quereshy, F. (2019). "Why Do Surgeons Schedule Their Own Surgeries?", Journal of Operations Management, 63(5), 262-281.