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Anesthesiologists represent an occupational group exposed to specific occupational hazards, including potential exposure to waste anesthetic gas released during medical procedures. In recent decades, halogenated anesthetic gases, such as desflurane and sevoflurane, have largely replaced nitrous oxide, due to better safety profiles and lower adverse health effects. However, possible long-term effects of low concentration exposures are unknown. A longitudinal analysis of health surveillance data was performed to test for possible changes over time in key markers of liver and kidney function. Moreover, we assessed the appropriateness of applying linear mixed models to occupational health data. A retrospective cohort study was conducted using health surveillance data from a cohort of anesthesiologists and a cohort of unexposed physicians working at the Polyclinic Hospital San Martino of Genoa, Italy, during 2016-2022. A 2-level linear mixed model with covariance structure of first order autoregressive model (AR(1)) type at the first level and unstructured type at the second level was applied. One hundred seventy subjects were included in the analysis, equally divided between exposed and unexposed. At the first and last periodic examination, liver and kidney markers were not statistically different in the 2 cohorts. The only significant change found related to estimated glomerular filtrate, which was found at the last follow-up to be greater among the exposed (M = 104.18 vs. 90.07, p = 0.007). The linear mixed model showed that anesthetic gas exposure was not associated with any of the outcomes. These results suggest the absence of increase in liver and kidney profile markers in the study population. Health surveillance data, aggregated and analyzed with appropriate statistical models, allow inferences to be made about potential health effects of workers due to uncontrolled exposures. To this end, the linear mixed model represents a powerful tool for longitudinal analysis of data derived from monitoring workers. Int J Occup Med Environ Health. 2024;37(5):557-68.

PurposeTo evaluate the incidence and risk factors associated with the unplanned return to OR in EOS.MethodsMedical records of 51 patients of EOS operated at three different centres using various types of GSCs were evaluated for complications requiring unplanned surgeries. Data were analysed to find out rate of unplanned surgeries in relation to the aetiology, age and Cobb angle at index surgery, type of implant, cause of unplanned surgery, and management required.ResultsOut of 51 patients, three did not meet inclusion criteria. Forty-eight patients of EOS operated by GSCs with a mean age of 6.7 years (range 2–12 years) with an average follow-up of 67.3 months were studied. There were 30 congenital, 10 idiopathic, 4 syndromic, and 4 neuromuscular cases. Thirty-nine out of 48 patients had one or more unplanned surgeries on follow-up (81.25%). Out of total 248 surgeries following index procedure, 82 were unplanned surgeries (33.06%), including 53 implant revisions, 12 implant-removal, 14 debridement, and 2 flaps. The common complications were 24.14% rod/screw breakage, 42.53% anchor pull-out, 16.09% infections, 6.90% wound dehiscence, and 4.6% neuro deficits. Unplanned surgeries were significantly higher in syndromic (58.8%) and neuromuscular (52.9%) than congenital (27.2%) and idiopathic (37.8%) cases (p < 0.05). Age at index procedure < 5 years had higher unplanned surgeries than age > 5 years (2.5 and 1.23 per patient, respectively, p < 0.05). Type of implant and initial Cobb angle did not significantly affect the rate of unplanned surgeries (p  > 0.05)ConclusionGSCs in EOS require a frequent revisit to operation room which should be well understood by the surgeon and parents.

This paper traces the expansion of a network of IoT sensors to improve the effectiveness of a centralised control room in Brazil in anticipating natural hazards. This centralised model relies on using IoT data by highly qualified experts replacing previous smaller local structures. We draw on the notion of Situational Awareness to carry out the study. Results show that although the operators were not always familiar with the characteristics of locations, the use of IoT data heightened their situational awareness in the centralised control room by improving perception and comprehension. However, they still relied on local knowledge and learned experiences to support projection and anticipation of risks. The study highlights that although data analytics systems are capable of expanding operators’ perception of local elements, they must be complemented by local richer forms of information, needed to anticipate risks and make critical decisions with major impact on local population.

Over the past 50 years, a wealth of applications has resulted from researchers turning their attention to operations such as fire suppression, law enforcement and ambulance services. The 1970s might even be argued as the 'golden age' of this particular effort, producing many of the seminal works in fire station location planning, unit assignment and ambulance queuing models. Such efforts naturally continue through to the present, but with a focus shifting away from earlier contexts of established urban emergency service systems. Simultaneously, current evidence from the field suggests that far more work remains. In this paper, we review the operational research (OR) foundation in emergency response so far, highlighting the fact that most of what has been accomplished addresses the well-structured problems of emergency services. This, in turn, offers an explanation for some paradoxical challenges from the field: most of emergency response itself is semistructured, at best. While OR has traditionally focused on the management of an organization, emergency response ultimately requires the management of disorganization, suggesting an important OR growth area for the next 50 years.

Background: Operating rooms are the core of hospitals. They are a primary source of revenue and are often seen as one of the bottlenecks in the medical system. Many efforts are made to increase throughput, reduce costs, and maximize incomes, as well as optimize clinical outcomes and patient satisfaction. We trained a predictive model on the length of surgeries to improve the productivity and utility of operative rooms in general hospitals. Methods: We collected clinical and administrative data for the last 10 years from two large general public hospitals in Israel. We trained a machine learning model to give the expected length of surgery using pre-operative data. These data included diagnoses, laboratory tests, risk factors, demographics, procedures, anesthesia type, and the main surgeon’s level of experience. We compared our model to a naïve model that represented current practice. Findings: Our prediction model achieved better performance than the naïve model and explained almost 70% of the variance in surgery durations. Interpretation: A machine learning-based model can be a useful approach for increasing operating room utilization. Among the most important factors were the type of procedures and the main surgeon’s level of experience. The model enables the harmonizing of hospital productivity through wise scheduling and matching suitable teams for a variety of clinical procedures for the benefit of the individual patient and the system as a whole.

Background Operation room (OR) planning is a complex process, especially in large hospitals with high rates of unplanned emergency procedures. Postponing elective surgery in order to provide capacity for emergency operations is inevitable at times. Elderly patients, residents of nursing homes, women, patients with low socioeconomic status and ethnic minorities are at risk for undertreatment in other contexts, as suggested by reports in the medical literature. We hypothesized that specific patient groups could be at higher risk for having their elective surgery rescheduled for non-medical reasons. Methods In this single center, prospective observational trial, we analysed 2519 patients undergoing elective surgery from October 2018 to May 2019. A 14-item questionnaire was handed out to illicit patient details. Additional characteristics were collected using electronic patient records. Information on the timely performance of the scheduled surgery was obtained using the OR’s patient data management system. 6.45% of all planned procedures analysed were postponed. Association of specific variables with postponement rates were analysed using the Mann–Whitney U test and Fisher's exact test/χ 2 -test. Results Significantly higher rates of postponing elective surgery were found in elderly patients. No significant differences in postponing rates were found for the variables gender, nationality (Germany, EU, non-EU), native language, professional medical background and level of education. Significantly lower rescheduling rates were found in patients with ties to hospital staff and in patients with a private health insurer. Conclusions Elderly patients, retirees and nursing home residents seem to be at higher risk for having their elective surgery rescheduled. However, owing to the study design, causality could not be proven. Our findings raise concern about possible undertreatment of these patient groups and provide data on short-term postponement of elective surgery. Trial registration DRKS00015836. Retrospectively registered.

We have developed a methodology for allocating operating room capacity to specialties. Our methodology consists of a finite-horizon mixed integer programming (MIP) model which determines a weekly operating room allocation template that minimizes inpatients' cost measured as their length of stay. A number of patient type priority (eg emergency over non-emergency patient) and clinical constraints (eg maximum number of hours allocated to each specialty, surgeon, and staff availability) are included in the formulation. The optimal solution from the analytical model is inputted into a simulation model that captures some of the randomness of the processes (eg surgery time, demand, arrival time, and no-show rate of the outpatients) and non-linearities (eg the MIP assumes proportional allocation of demand satisfaction (output) with room allocation (input)). The simulation model outputs the average length of stay for each specialty and the room utilization. On a case example of a Los Angeles County Hospital, we show how the hospital length of stay pertaining to surgery can be reduced.

Owing to rising concern about patient safety, surgeons and anaesthetists have looked for ways of minimising adverse events. Clinicians have encouraged behavioural scientists to bring research techniques used in other industries into the operating theatre in order to study the behaviour of surgeons, nurses and anaesthetists. Safer Surgery presents one of the first collections of studies designed to understand the factors influencing safe and efficient surgical, anaesthetic and nursing practice.

Internet is a very attractive technology for the implementation of experiments, both in order to obtain larger and more diverse samples and as a field of economic research in its own right. This paper reports on an experiment performed both online and in the laboratory, designed to strengthen the internal validity of decisions elicited over the Internet. We use the same subject pool, the same monetary stakes and the same decision interface, and control the assignment of subjects between the Internet and a traditional university laboratory. We apply the comparison to the elicitation of social preferences in a Public Good game, a dictator game, an ultimatum bargaining game and a trust game, coupled with an elicitation of risk aversion. This comparison concludes in favor of the reliability of behaviors elicited through the Internet. We moreover find a strong overall parallelism in the preferences elicited in the two settings. The paper also reports some quantitative differences in the point estimates, which always go in the direction of more other-regarding decisions from online subjects. This observation challenges either the predictions of social distance theory or the generally assumed increased social distance in internet interactions.