Demystifying the role of anaesthetists in clinical coding in the Australian healthcare system.

Despite the self-evident importance of hospital funding, many anaesthetists remain unsure of exactly how their daily work relates to hospital reimbursement. A lack of awareness of the nuances of the Australian hospital activity-based funding system has the potential to affect anaesthetic department reimbursement and thus resourcing. Activity-based funding relies on clinical coders reviewing clinical documentation and quantifying the care given to a patient during an admission. Errors in funding allocation may arise when there is a disconnect between the work performed and the information coded. In anaesthesia, there are several factors impeding this process, including clinical understanding of coding, system setup and coders' understanding of anaesthesia. This article explores these factors from the clinical anaesthetist's point of view and suggests solutions, such as awareness and education, clinician-coder cooperation and redesign of documentation systems at a systems level that anaesthetic departments can incorporate.

2021 Update on airway management from the Anaesthesia Continuing Education Airway Management Special Interest Group.

Airway Management is the key for anaesthetists dealing with patients undergoing diagnostic procedures and surgical interventions. The present coronavirus pandemic underpins even more how important safe airway management is. It also highlights the need to apply stringent precautions to avoid infection and ongoing transmission to patients, anaesthetists and other healthcare workers (HCWs). In light of this extraordinary global situation the aim of this article is to update the reader on the varied aspects of the ever-changing tasks anaesthetists are involved in and highlight the equipment, devices and techniques that have evolved in response to changing technology and unique patient and surgical requirements.

Frequency and clinical spectrum of in-flight medical incidents during domestic and international flights.

Most medical practitioners are not specifically trained to diagnose or manage in-flight medical incidents, yet there may occur a moral obligation to do so when least expected. We felt that knowledge of the frequency of emergency versus non-serious medical incidents, in addition to the clinical spectrum of incidents most often encountered, would be of interest to medical practitioners and, in particular, critical care physicians, who happen to find themselves asked to assist with such events. To this end we collaborated with the Department of Medical Services of a major Australian airline to define the incidence, severity, and type of all in-flight medical events encountered over the course of a year's flights. We audited in-flight medical data collected over a continuous 12-month period, which covered 131,890 international and domestic flight sectors transporting more than 27 million passengers. There was an average of 296 medical events per month (3555 in total) making the per-flight incidence of a medical event approximately 1:40 (2.7%). Of these in-flight incidents, 915 (26%) were graded as emergencies, with the commonest descriptors of such incidents being either loss of consciousness (37%) or a suspected cardiovascular event (12%). Six of these 915 emergencies proved fatal. Twenty-one flights were diverted due to medical incidents (<0.016% of all flights), with 52% of these attributed to suspected cardiac events. In this series, medical in-flight events were recorded in approximately one in 40 flights, whereas medical emergencies occurred in approximately one in 150 flights.

Videolaryngoscopes differ substantially in illumination of the oral cavity: A manikin study.

BACKGROUND AND AIMS: Insufficient illumination of the oral cavity during endotracheal intubation may result in suboptimal conditions. Consequently, suboptimal illumination and laryngoscopy may lead to potential unwanted trauma to soft tissues of the pharyngeal mucosa. We investigated illumination of the oral cavity by different videolaryngoscopes (VLS) in a manikin model. METHODS: We measured light intensity from the mouth opening of a Laerdal intubation trainer comparing different direct and indirect VLS at three occasions, resembling optimal to less-than-optimal intubation conditions; at the photographer's dark room, in an operating theatre and outdoors in bright sunlight. RESULTS: Substantial differences in luminance were detected between VLS. The use of LED light significantly improved light production. All VLS produced substantial higher luminance values in a well-luminated environment compared to the dark photographer's room. The experiments outside-in bright sunlight-were interfered with by direct sunlight penetration through the synthetic material of the manikin, making correct measurement of luminance in the oropharynx invalid. CONCLUSION: Illumination of the oral cavity differs widely among direct and indirect VLS. The clinician should be aware of the possibility of suboptimal illumination of the oral cavity and the potential risk this poses for the patient.

Mathematical method to build an empirical model for inhaled anesthetic agent wash-in.

BACKGROUND: The wide range of fresh gas flow - vaporizer setting (FGF - FD) combinations used by different anesthesiologists during the wash-in period of inhaled anesthetics indicates that the selection of FGF and FD is based on habit and personal experience. An empirical model could rationalize FGF - FD selection during wash-in. METHODS: During model derivation, 50 ASA PS I-II patients received desflurane in O2 with an ADU® anesthesia machine with a random combination of a fixed FGF - FD setting. The resulting course of the end-expired desflurane concentration (FA) was modeled with Excel Solver, with patient age, height, and weight as covariates; NONMEM was used to check for parsimony. The resulting equation was solved for FD, and prospectively tested by having the formula calculate FD to be used by the anesthesiologist after randomly selecting a FGF, a target FA (FAt), and a specified time interval (1 - 5 min) after turning on the vaporizer after which FAt had to be reached. The following targets were tested: desflurane FAt 3.5% after 3.5 min (n = 40), 5% after 5 min (n = 37), and 6% after 4.5 min (n = 37). RESULTS: Solving the equation derived during model development for FD yields FD=-(e(-FGF*-0.23+FGF*0.24)*(e(FGF*-0.23)*FAt*Ht*0.1-e(FGF*-0.23)*FGF*2.55+40.46-e(FGF*-0.23)*40.46+e(FGF*-0.23+Time/-4.08)*40.46-e(Time/-4.08)*40.46))/((-1+e(FGF*0.24))*(-1+e(Time/-4.08))*39.29). Only height (Ht) could be withheld as a significant covariate. Median performance error and median absolute performance error were -2.9 and 7.0% in the 3.5% after 3.5 min group, -3.4 and 11.4% in the 5% after 5 min group, and -16.2 and 16.2% in the 6% after 4.5 min groups, respectively. CONCLUSIONS: An empirical model can be used to predict the FGF - FD combinations that attain a target end-expired anesthetic agent concentration with clinically acceptable accuracy within the first 5 min of the start of administration. The sequences are easily calculated in an Excel file and simple to use (one fixed FGF - FD setting), and will minimize agent consumption and reduce pollution by allowing to determine the lowest possible FGF that can be used. Different anesthesia machines will likely have different equations for different agents.