Research Ideas and Outcomes :
Research Idea
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Corresponding author: Todd Vincent Prier (tvprier@live.com)
Academic editor: Editorial Secretary
Received: 09 Nov 2023 | Accepted: 19 Dec 2023 | Published: 15 Jan 2024
© 2024 Todd Prier, Kelly Yale-Suda, Hailey Westover, Ryan Corey
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Prier TV, Yale-Suda K, Westover H, Corey R (2024) Predictive modelling of total operating room time for Laparoscopic Cholecystectomy using pre-operatively known indicators to guide accurate surgical scheduling in a critical access hospital. Research Ideas and Outcomes 10: e115511. https://doi.org/10.3897/rio.10.e115511
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The financial margin of rural and critical access hospitals highly depends on their surgical volume. An efficient operating room is necessary to maximise profit and minimise financial loss. OR utilisation is a crucial OR efficiency metric requiring accurate case duration estimates. The patient's age, ASA, BMI, Mallampati score, previous surgery, the planned surgery, the surgeon, the assistant's level of experience and the severity of the patient's disease are also associated with operative duration. Although complex machine-learning models are accurate in operative prediction, they are not always available in resource-limited hospitals. Laparoscopic cholecystectomy (LC) is one of the most common surgical procedures performed and is one of the few procedures performed at critical access and rural hospitals. The accurate estimation of the operative duration of LC is essential for efficient OR utilisation. We hypothesise that a multivariate linear regression prediction model can be constructed from a set of pre-operatively known, easily collected variables to maximise OR utilisation and improve operative scheduling accuracy for LC. We further hypothesise that this model can be implemented in resource-limited environments, such as critical access hospitals.
operating room efficiency, operating room scheduling, procedure scheduling, laparoscopic cholecystectomy, multivariate regression prediction modelling, linear regression, critical access hospitals, rural hospitals, quality improvement
In rural hospitals, the ability to be profitable is directly associated with staying open (
OR utilisation is one of the most essential metrics for OR efficiency (
The non-endoscopic surgical volume in rural hospitals tends to be ambulatory and laparoscopic cholecystectomy (LC), hernia repair and appendectomy predominate (
We hypothesise that the TOT can be accurately predicted using information already collected by the system processes at our critical access hospital. We also hypothesise that there is no difference between TOT and operative time (the time from the incision to completion of placing the dressings; OT) predicted in this fashion. Specifically, we hypothesise that patient characteristics, surgeon and assistant and pre-operatively determined diagnoses associated with the severity of the pathology directly influence the OT and TOT and allow for accurate prediction of operative duration that lead to improved utilisation of OR time with less over- or underutilisation.
Although there are many predictive models for LC scheduling, the difference in our proposed model is that it uses data already collected in every OR, is easily accessible and does not increase the workload of the involved staff. Previous predictive models for LC have used laboratory and radiographic data indicating that the complexity of the pathology predicts a longer operative time. We hypothesise that, if we simplify the data collection process and instead use the known clinical diagnosis that indicates the increasing complexity of the surgical pathology, the model will be as accurate and easier to construct. We also hypothesise that the TOT will be as precise as the OT in the prediction models, further simplifying the scheduling processes. Our proposed model that minimises the workload to the staff, is simple to implement, is accurate and can maximise OR utilisation has the potential to directly impact the hospital's financial margin.
After obtaining approval via the hospital's Institutioinal Review Board, we plan to initiate an observational cohort study by performing a retrospective chart review of all LC from a single surgeon at a single institution from July 2008 to July 2022, thus controlling for the surgeon. LC is only performed under general anaesthesia, allowing for the controlling for the type of anaesthesia. The anaesthesiologists were not included in the model, as evidence supports that the variability imposed by the anaesthesiologist is a non-significant contributor to TOT (
The patient's age, gender, BMI, American Society of Anesthesology Physical Status Score (ASA), Mallampati score, previous history of upper abdominal surgery, elective, inpatient, or emergent surgery and diagnosis leading to surgery as defined by the surgeon in the chart will be recorded. The diagnosis categories are defined as biliary dyskinesia, biliary hyperkinesis, biliary colic, chronic cholecystitis, acute cholecystitis, biliary pancreatitis, choledocholithiasis and gangrenous cholecystitis. The pre-operative plan for LC with intra-operative cholangiography or biliary ultrasonography will also be noted. From the operative record, the presence of an assistant and their level, either advanced practice provider (APP) or surgeon, the time from room entrance to exiting the operating room (TOT) and the operative time (OT) -- the time elapsed from skin incision to dressing placement -- will also be recorded. The times will be those within the operative record.
Data analysis using ANOVA and linear regression will test the null hypothesis that there is no difference within the OT and TOT groups for LC for the different diagnoses. Multivariate linear regression will be used to build a prediction model from the OT and a separate model of TOT using all of the variables as predictors from the data collected. As the TOT includes time in the OR that is non-surgeon dependent, it may be more variable and, therefore, be less accurately predicted with our model. The two models will be compared using likelihood ratio testing.
Missing data will be excluded from the analysis as long as it is missing completely at random. Original bootstrapping with replacement will be utilised for internal model validation to ensure maximal usage of the dataset for model development.
Minimum sample size calculations, based on traditional prediction modelling approaches, require ten events per predictor (