“…3 Frequency participants reported mental ( a ) and physical ( b ) demands over 50 % (high risk) by roles Fig. 4 Distribution of mental demand across surgical team member roles with 50 score threshold adapted from workload studies indicating hypothesized impact on performance [ 35 ] …”
Section: Resultsmentioning
confidence: 99%
“…Literature suggests workload thresholds of 40 ± 10 (out of 100) in aviation combat tasks [ 34 ]. In healthcare, two separate studies by Mazur et al suggested threshold of 50–55 (out of 100) is the point at which performance in clinical tasks decline and clinical errors become more common [ 35 , 36 ]. Additionally, several studies in laparoscopy showed a positive relationship between mental workload and performance errors, e.g., tissue injuries and instrument positioning [ 14 , 37 ].…”
BackgroundSurgical performance, provider health, and patient safety can be compromised when workload demands exceed individual capability on the surgical team. The purpose of this study is to quantify and compare intraoperative workload among surgical team members.MethodsObservations were conducted for an entire surgical day for 33 participating surgeons and their surgical team at one medical institution. Workload (mental, physical, case complexity, distractions, and case difficulty) was measured for each surgical team member using questions from validated questionnaires. Statistical analyses were performed with a mixed effects model.ResultsA total of 192 surgical team members participated in 78 operative cases, and 344 questionnaires were collected. Procedures with high surgeon mental and physical workload included endovascular and gastric surgeries, respectively. Ratings did not differ significantly among surgeons and residents, but scrub nurses physical demand ratings were 14–22 (out of 100) points lower than the surgeons, residents, and surgical assistants. Residents reported the highest mental workload, averaging 19–24 points higher than surgical assistants, scrub nurses, and circulating nurses. Mental and physical demands exceeded 50 points 28–45 % of the time for surgeons and residents. Workload did not differ between minimally invasive and open techniques.ConclusionThe workload questionnaires are an effective tool for quantifying intraoperative workload across the surgical team to ensure mental and physical demands do not exceed thresholds where performance may decrease and injury risk increase. This tool has the potential to measure the safety of current procedures and drive design of workload interventions.
“…3 Frequency participants reported mental ( a ) and physical ( b ) demands over 50 % (high risk) by roles Fig. 4 Distribution of mental demand across surgical team member roles with 50 score threshold adapted from workload studies indicating hypothesized impact on performance [ 35 ] …”
Section: Resultsmentioning
confidence: 99%
“…Literature suggests workload thresholds of 40 ± 10 (out of 100) in aviation combat tasks [ 34 ]. In healthcare, two separate studies by Mazur et al suggested threshold of 50–55 (out of 100) is the point at which performance in clinical tasks decline and clinical errors become more common [ 35 , 36 ]. Additionally, several studies in laparoscopy showed a positive relationship between mental workload and performance errors, e.g., tissue injuries and instrument positioning [ 14 , 37 ].…”
BackgroundSurgical performance, provider health, and patient safety can be compromised when workload demands exceed individual capability on the surgical team. The purpose of this study is to quantify and compare intraoperative workload among surgical team members.MethodsObservations were conducted for an entire surgical day for 33 participating surgeons and their surgical team at one medical institution. Workload (mental, physical, case complexity, distractions, and case difficulty) was measured for each surgical team member using questions from validated questionnaires. Statistical analyses were performed with a mixed effects model.ResultsA total of 192 surgical team members participated in 78 operative cases, and 344 questionnaires were collected. Procedures with high surgeon mental and physical workload included endovascular and gastric surgeries, respectively. Ratings did not differ significantly among surgeons and residents, but scrub nurses physical demand ratings were 14–22 (out of 100) points lower than the surgeons, residents, and surgical assistants. Residents reported the highest mental workload, averaging 19–24 points higher than surgical assistants, scrub nurses, and circulating nurses. Mental and physical demands exceeded 50 points 28–45 % of the time for surgeons and residents. Workload did not differ between minimally invasive and open techniques.ConclusionThe workload questionnaires are an effective tool for quantifying intraoperative workload across the surgical team to ensure mental and physical demands do not exceed thresholds where performance may decrease and injury risk increase. This tool has the potential to measure the safety of current procedures and drive design of workload interventions.
“…The effort and perceived performance were higher than other subscales, and their scores exceeded the midpoint threshold on the NASA-TLX subscale. The midpoint threshold has been applied to access an unsustainable demand in other domains [37,38]. However, these specific sustainability thresholds have not been established within HAI.…”
Section: Effects Of Automation On Overall and Subscale Workload Measumentioning
Automation aims to improve the task performance and the safety of human operators. The success of automation can be facilitated with well-designed human-automation interaction (HAI), which includes the consideration of a trade-off between the benefits of reliable automation and the cost of Failed automation. This study evaluated four different types of HAIs in order to validate the automation trade-off, and HAI types were configured by the levels and the statuses of office automation. The levels of automation were determined by information amount (i.e., Low and High), and the statues were decided by automation function (i.e., Routine and Failed). Task performance including task completion time and accuracy and subjective workload of participants were measured in the evaluation of the HAIs. Relatively better task performance (short task completion time and high accuracy) were presented in the High level in Routine automation, while no significant effects of automation level were reported in Failed automation. The subjective workload by the National Aeronautics and Space Administration (NASA) Task Load Index (TLX) showed higher workload in High and Failed automation than Low and Failed automation. The type of sub-functions and the task classification can be estimated as major causes of automation trade-off, and dissimilar results between empirical and subjective measures need to be considered in the design of effective HAI. the amount of automation autonomy and human physical and cognitive activity [5,6]. Sheridan and Verplank [7] suggested 10 LOA as the basis of the classic human-machine task allocation principle: at a higher level, automation can execute decision-making tasks without the aid of human operators, at a lower level, it can execute an option within the operators' controls, and at a further lower level, it may be simply performed by the human operators. Kaber and Endsley [6] proposed that automation could be classified according to the information-processing stages: perceiving the status of the system variables or scanning displays (i.e., monitoring), formulation of task-processing plans (i.e., generating), deciding on an optimal plan (i.e., selecting), and the control actions at an interface (i.e., implementing). Elaborating on this classification, Parasuraman et. al. [4] presented a four-stage model of automation to support the design of human-automation interactions (HAI) in complex systems: information acquisition, information analysis, decision and action selection, and action implementation. This classification aids the formulation of specific function allocation schemes for automation. The combination of the LOA with information processing stages led to the development of a concept known as the degree of automation (DOA) [3]. Each function in a model or an information processing stage, either by the human or machine (or some combination thereof), is responsible for the effects of automation on the task performance.Under routine conditions or at higher LOA or DOA, human operators simply supervi...
“…With increased reporting into ILS, there have been efforts to develop standardized operational frameworks, metrics, and terminologies to learn from errors in RO, 1,[10][11][12][13][14] and prior works have described many features of errors in radiation therapy, such as where they originate in the RO workflow, 15 which clinical features predict event reporting, 16 and the frequencies at which different RO team members report events. 17,18 Although there is a substantial and growing literature on incident learning in the RO context, few reports specifically focus on causes of errors in RO.…”
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