Dataset Information

Complex Cancer Surgery in the Outpatient Setting: The Josie Robertson Surgery Center.

ABSTRACT: Minimally invasive operative techniques and enhanced recovery after surgery (ERAS) protocols have transformed clinical practice and made it possible to perform increasingly complex oncologic procedures in the ambulatory setting, with recovery at home after a single overnight stay. Capitalizing on these changes, Memorial Sloan Kettering Cancer Center's Josie Robertson Surgery Center (JRSC), a freestanding ambulatory surgery facility, was established to provide both outpatient procedures and several surgeries that had previously been performed in the inpatient setting, newly transitioned to this ambulatory extended recovery (AXR) model. However, the JRSC core mission goes beyond rapid recovery, aiming to be an innovation center with a focus on superlative patient experience and engagement, efficiency, and data-driven continuous improvement. Here, we describe the JRSC genesis, design, care model, and outcome tracking and quality improvement efforts to provide an example of successful, patient-centered surgical care for select patients undergoing relatively complex procedures in an ambulatory setting.

SUBMITTER: Tokita H

PROVIDER: S-EPMC8285049 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Complex Cancer Surgery in the Outpatient Setting: The Josie Robertson Surgery Center.

Tokita Hanae H Twersky Rebecca R Laudone Vincent V Levine Marcia M Stein Daniel D Scardino Peter P Simon Brett A BA

Anesthesia and analgesia 20200901 3

Minimally invasive operative techniques and enhanced recovery after surgery (ERAS) protocols have transformed clinical practice and made it possible to perform increasingly complex oncologic procedures in the ambulatory setting, with recovery at home after a single overnight stay. Capitalizing on these changes, Memorial Sloan Kettering Cancer Center's Josie Robertson Surgery Center (JRSC), a freestanding ambulatory surgery facility, was established to provide both outpatient procedures and sever ...[more]

PMID: 32224721

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Project description:BackgroundDays before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression.MethodsData were collected from patients at an ambulatory surgery center. The primary outcome measurement was determined to have a value of 1 (versus 0) if they met both criteria: (1) surgery ends by 5 pm and (2) patient is discharged from the recovery room by 7 pm. We developed models to determine if a procedure would meet both criteria if it were scheduled at 1 pm, 2 pm, 3 pm, or 4 pm. We implemented regression, random forest, balanced random forest, balanced bagging, neural network, and support vector classifier, and included the following features: surgery, surgeon, service line, American Society of Anesthesiologists score, age, sex, weight, and scheduled case duration. We evaluated model performance with Synthetic Minority Oversampling Technique (SMOTE). We compared the following performance metrics: F1 score, area under the receiver operating characteristic curve (AUC), specificity, sensitivity, precision, recall, and Matthews correlation coefficient.ResultsAmong 13,447 surgical procedures, the median total perioperative time (actual case duration and PACU length stay) was 165 minutes. When SMOTE was not used, when predicting whether surgery will end by 5 pm and patient will be discharged by 7 pm, the average F1 scores were best with random forest, balanced bagging, and balanced random forest classifiers. When SMOTE was used, these models had improved F1 scores compared to no SMOTE. The balanced bagging classifier performed best with F1 score of 0.78, 0.80, 0.82, and 0.82 when predicting our outcome if cases were to start at 1 pm, 2 pm, 3 pm, or 4 pm, respectively.ConclusionsWe demonstrated improvement in predicting the outcome at a range of start times when using ensemble learning versus regression techniques. Machine learning may be adapted by operating room management to allow for a better determination whether an add-on case at an outpatient surgery center could be appropriately booked.

Dataset Information

Complex Cancer Surgery in the Outpatient Setting: The Josie Robertson Surgery Center.

Publications

Complex Cancer Surgery in the Outpatient Setting: The Josie Robertson Surgery Center.

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