Dataset Information

Sepsis in the Operating Room: A Simulation Case for Perioperative Providers.

ABSTRACT:

Introduction

Sepsis should be included in the differential of any patient with unexplained organ dysfunction, whether or not an obvious infection is initially detected. Perioperative providers frequently care for patients with sepsis. This simulation case challenges participants to recognize and manage a presentation of postoperative sepsis, providing an opportunity to discuss the rationale behind sepsis management during debriefing.

Methods

Assuming the role of an anesthesia provider, the participant takes over the care of a 62-year-old female who has just undergone cystoscopy and is extubated in the operating room (OR). The participant receives a brief handoff from the outgoing anesthesiologist while the patient awaits a postanesthesia care unit slot. The case has been uneventful, aside from intermittent hypotension responsive to IV fluids and boluses of phenylephrine. Within minutes of the handoff, the patient becomes somnolent and hypotensive. Efforts to treat hypotension eventually precipitate hypoxemia. Trainees must recognize and manage this cardiopulmonary decompensation. The scenario benefits from an OR simulation environment containing an anesthetic ventilator, anesthesia drugs and equipment, and a mannequin on an OR table.

Results

Twelve residents completed the simulation scenario. Formal feedback was collected via email questionnaire from faculty instructors within 30 days of teaching each session.

Discussion

Sepsis presents a diagnostic dilemma in part because no single diagnostic test rules the syndrome in or out. Multiple operational definitions of sepsis in the academic literature add to the confusion for clinicians. Our case simulation challenges perioperative providers to make a timely diagnosis and initiate appropriate treatment of sepsis.

SUBMITTER: Bronshteyn YS

PROVIDER: S-EPMC6342055 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Sepsis in the Operating Room: A Simulation Case for Perioperative Providers.

Bronshteyn Yuriy S YS Lemm John J Malinzak Elizabeth E Ghadimi Nada N Udani Ankeet D AD

MedEdPORTAL : the journal of teaching and learning resources 20170330

<h4>Introduction</h4>Sepsis should be included in the differential of any patient with unexplained organ dysfunction, whether or not an obvious infection is initially detected. Perioperative providers frequently care for patients with sepsis. This simulation case challenges participants to recognize and manage a presentation of postoperative sepsis, providing an opportunity to discuss the rationale behind sepsis management during debriefing.<h4>Methods</h4>Assuming the role of an anesthesia prov ...[more]

PMID: 30800765

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Project description:BackgroundSince operating rooms are a major bottleneck resource and an important revenue driver in hospitals, it is important to use these resources efficiently. Studies estimate that between 60 and 70% of hospital admissions are due to surgeries. Furthermore, staffing cannot be changed daily to respond to changing demands. The resulting high complexity in operating room management necessitates perpetual process evaluation and the use of decision support tools. In this study, we evaluate several management policies and their consequences for the operating theater of the University Hospital Augsburg.MethodsBased on a data set with 12,946 surgeries, we evaluate management policies such as parallel induction of anesthesia with varying levels of staff support, the use of a dedicated emergency room, extending operating room hours reserved as buffer capacity, and different elective patient sequencing policies. We develop a detailed simulation model that serves to capture the process flow in the entire operating theater: scheduling surgeries from a dynamically managed waiting list, handling various types of schedule disruptions, rescheduling and prioritizing postponed and deferred surgeries, and reallocating operating room capacity. The system performance is measured by indicators such as patient waiting time, idle time, staff overtime, and the number of deferred surgeries.ResultsWe identify significant trade-offs between expected waiting times for different patient urgency categories when operating rooms are opened longer to serve as end-of-day buffers. The introduction of parallel induction of anesthesia allows for additional patients to be scheduled and operated on during regular hours. However, this comes with a higher number of expected deferrals, which can be partially mitigated by employing additional anesthesia teams. Changes to the sequencing of elective patients according to their expected surgery duration cause expectable outcomes for a multitude of performance indicators.ConclusionsOur simulation-based approach allows operating theater managers to test a multitude of potential changes in operating room management without disrupting the ongoing workflow. The close collaboration between management and researchers in the design of the simulation framework and the data analysis has yielded immediate benefits for the scheduling policies and data collection efforts at our practice partner.

Project description:BACKGROUND:Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set to improve estimation of case-time duration relative to current standards. STUDY DESIGN:We developed models to predict case-time duration using linear regression and supervised machine learning. For each of these models, we generated an all-inclusive model, service-specific models, and surgeon-specific models. In the latter 2 approaches, individual models were created for each surgical service and surgeon, respectively. Our data set included 46,986 scheduled operations performed at a large academic medical center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared with our institutional standard of using average historic procedure times and surgeon estimates. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration < predicted - 10%), and the predictive capability of being within a 10% tolerance threshold. RESULTS:The machine learning algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracy, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the machine learning surgeon-specific model. CONCLUSIONS:Our study is a notable advancement toward statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches can improve case duration estimations, enabling improved operating room scheduling, efficiency, and reduced costs.

Dataset Information

Sepsis in the Operating Room: A Simulation Case for Perioperative Providers.

Introduction

Methods

Results

Discussion

Publications

Sepsis in the Operating Room: A Simulation Case for Perioperative Providers.

Similar Datasets

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