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Sizing of airborne particles in an operating room.

ABSTRACT: Medical procedures that produce aerosolized particles are under great scrutiny due to the recent concerns surrounding the COVID-19 virus and increased risk for nosocomial infections. For example, thoracostomies, tracheotomies and intubations/extubations produce aerosols that can linger in the air. The lingering time is dependent on particle size where, e.g., 500 μm (0.5 mm) particles may quickly fall to the floor, while 1 μm particles may float for extended lengths of time. Here, a method is presented to characterize the size of <40 μm to >600 μm particles resulting from surgery in an operating room (OR). The particles are measured in-situ (next to a patient on an operating table) through a 75mm aperture in a ∼400 mm rectangular enclosure with minimal flow restriction. The particles and gasses exiting a patient are vented through an enclosed laser sheet while a camera captures images of the side-scattered light from the entrained particles. A similar optical configuration was described by Anfinrud et al.; however, we present here an extended method which provides a calibration method for determining particle size. The use of a laser sheet with side-scattered light provides a large FOV and bright image of the particles; however, the particle image dilation caused by scattering does not allow direct measurement of particle size. The calibration routine presented here is accomplished by measuring fixed particle distribution ranges with a calibrated shadow imaging system and mapping these measurements to the in-situ imaging system. The technique used for generating and measuring these particles is described. The result is a three-part process where 1) particles of varying sizes are produced and measured using a calibrated, high-resolution shadow imaging method, 2) the same particle generators are measured with the in-situ imaging system, and 3) a correlation mapping is made between the (dilated) laser image size and the measured particle size. Additionally, experimental and operational details of the imaging system are described such as requirements for the enclosure volume, light management, air filtration and control of various laser reflections. Details related to the OR environment and requirements for achieving close proximity to a patient are discussed as well.

SUBMITTER: Tkacik PT

PROVIDER: S-EPMC8021156 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Importance:Increasing value requires improving quality or decreasing costs. In surgery, estimates for the cost of 1 minute of operating room (OR) time vary widely. No benchmark exists for the cost of OR time, nor has there been a comprehensive assessment of what contributes to OR cost. Objectives:To calculate the cost of 1 minute of OR time, assess cost by setting and facility characteristics, and ascertain the proportion of costs that are direct and indirect. Design, Setting, and Participants:This cross-sectional and longitudinal analysis examined annual financial disclosure documents from all comparable short-term general and specialty care hospitals in California from fiscal year (FY) 2005 to FY2014 (N = 3044; FY2014, n = 302). The analysis focused on 2 revenue centers: (1) surgery and recovery and (2) ambulatory surgery. Main Outcomes and Measures:Mean cost of 1 minute of OR time, stratified by setting (inpatient vs ambulatory), teaching status, and hospital ownership. The proportion of cost attributable to indirect and direct expenses was identified; direct expenses were further divided into salary, benefits, supplies, and other direct expenses. Results:In FY2014, a total of 175 of 302 facilities (57.9%) were not for profit, 78 (25.8%) were for profit, and 49 (16.2%) were government owned. Thirty facilities (9.9%) were teaching hospitals. The mean (SD) cost for 1 minute of OR time across California hospitals was $37.45 ($16.04) in the inpatient setting and $36.14 ($19.53) in the ambulatory setting (P = .65). There were no differences in mean expenditures when stratifying by ownership or teaching status except that teaching hospitals had lower mean (SD) expenditures than nonteaching hospitals in the inpatient setting ($29.88 [$9.06] vs $38.29 [$16.43]; P = .006). Direct expenses accounted for 54.6% of total expenses ($20.40 of $37.37) in the inpatient setting and 59.1% of total expenses ($20.90 of $35.39) in the ambulatory setting. Wages and benefits accounted for approximately two-thirds of direct expenses (inpatient, $14.00 of $20.40; ambulatory, $14.35 of $20.90), with nonbillable supplies accounting for less than 10% of total expenses (inpatient, $2.55 of $37.37; ambulatory, $3.33 of $35.39). From FY2005 to FY2014, expenses in the OR have increased faster than the consumer price index and medical consumer price index. Teaching hospitals had slower growth in costs than nonteaching hospitals. Over time, the proportion of expenses dedicated to indirect costs has increased, while the proportion attributable to salary and supplies has decreased. Conclusions and Relevance:The mean cost of OR time is $36 to $37 per minute, using financial data from California's short-term general and specialty hospitals in FY2014. These statewide data provide a generalizable benchmark for the value of OR time. Furthermore, understanding the composition of costs will allow those interested in value improvement to identify high-yield targets.

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.

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Sizing of airborne particles in an operating room.

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