Efficiency in the operating room: optimizing patient throughput

Robertson, Amy; Kla, Koffi M.; Yaghmour, Edward

doi:10.1097/aia.0000000000000333

Cited by 3 publications

(3 citation statements)

References 57 publications

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“…When considering these three studies and advances in peri‐operative care, discharging directly to the ward and bypassing PACU to reduce resources for only patients in need seems a rational next step. Bypassing the PACU can potentially increase the logistic workflow and decrease the risk for PACU bottlenecks, and ultimately allow for further advances in early rehabilitation on the ward [ 6 , 7 , 21 ]. However, these effects and further safety data need to be substantiated in future studies, and our study might serve as a basis for future randomised controlled trials.…”

Section: Discussionmentioning

confidence: 99%

Bypassing the post‐anaesthesia care unit after elective hip and knee arthroplasty: a prospective cohort safety study

et al. 2022

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Summary Following knee and hip arthroplasty, transfer to a recovery area immediately following surgery and before going to ward might be unnecessary in low‐risk patients. Avoiding the recovery area in this way could allow for more targeted use of resources for higher risk patients, which may improve operating theatre flow and productivity. A prospective single‐centre cohort study on the safety of criteria for bypassing the post‐anaesthesia care unit in elective hip and knee arthroplasty was designed. Criteria were: ASA physical status < 3; peri‐operative bleeding < 500 ml; low postoperative discharge‐score (modified Aldrete‐score); and an uncomplicated surgical and neuraxial anaesthesia procedure. The primary outcome was the number of patients in need of secondary readmission to the post‐anaesthesia care unit. Events within the first 24 postoperative hours were recorded, along with readmission and complication rates. A total of 696 patients were included, with 287 (41%) undergoing total hip arthroplasty, 274 (39%) undergoing total knee arthroplasty and 135 (19%) undergoing unicompartmental knee‐arthroplasty. Of these, 207 (44%) bypassed the post‐anaesthesia care unit. Patients all received multimodal analgesia without peripheral nerve blockade. Only one patient in the ward group required secondary readmission to the post‐anaesthesia care unit. Within 24 h, 151 events were reported, with 41 (27%) in the ward group and 110 (73%) in the post‐anaesthesia care unit group. Two events in each group occurred within 2 hours of surgery. No complications were attributed to bypassing the post‐anaesthesia care unit. The use of simple pragmatic criteria for bypassing the post‐anaesthesia care unit for patients undergoing knee and hip arthroplasty with spinal anaesthesia is possible and associated with significant reduction of post‐anaesthesia care unit admission and without apparent safety issues. Confirmation is needed from other studies and external validity should be interpreted cautiously in centres with different peri‐operative regimens, organisational and staffing structures.

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Section: Discussionmentioning

confidence: 99%

Bypassing the post‐anaesthesia care unit after elective hip and knee arthroplasty: a prospective cohort safety study

et al. 2022

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“…Current Procedural Terminology (CPT) codes have been used for over 25 years to post or create surgical cases 11 . Most hospitals use surgeon-estimated and/or historical median case length to schedule surgical cases, which both have uncertainties and are inaccurate 1,4–6,10,12,13 . Some hospitals also use their electronic health record (EHR) system-generated time, which is a summary (median or average) of past similar cases lengths based on the surgeon, platform, and combination of CPT codes and is shown to be unreliable due to preoperative data variations 5,6,13 .…”

mentioning

confidence: 99%

“…11 Most hospitals use surgeon-estimated and/or historical median case length to schedule surgical cases, which both have uncertainties and are inaccurate. 1,[4][5][6]10,12,13 Some hospitals also use their electronic health record (EHR) system-generated time, which is a summary (median or average) of past similar cases lengths based on the surgeon, platform, and combination of CPT codes and is shown to be unreliable due to preoperative data variations. 5,6,13 Many groups have incorporated patient, procedural, and operational factors to create machine-learning models and predict surgical case length.…”

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confidence: 99%

Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation

Zaribafzadeh,

Webster,

Vail

et al. 2023

Annals of Surgery

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Objective: To implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations. Background: The operating room is one of the most expensive resources in a health system, estimated to cost $22 to $133 per minute and generate about 40% of hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the operating room and other resources. Methods: We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient-boosting machine learning model. The model loss function was customized to improve the balance between over-and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution. Results: The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the schedulerpredicted case length from August to December 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer underpredicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only overpredicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer underpredicted cases. Conclusions: We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.

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Developing a model for quantifying staffing requirements in the post-anaesthesia care unit

Bagstaff

2023

Nursing Management

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Efficiency in the operating room: optimizing patient throughput

Cited by 3 publications

References 57 publications

Bypassing the post‐anaesthesia care unit after elective hip and knee arthroplasty: a prospective cohort safety study

Bypassing the post‐anaesthesia care unit after elective hip and knee arthroplasty: a prospective cohort safety study

Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation

Developing a model for quantifying staffing requirements in the post-anaesthesia care unit

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