Consecutive surgeries with complications: the impact of scheduling decisions
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".