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We previously developed and validated the Oxford NOTECHS rating system for evaluating the non-technical skills of an entire operating theatre team. Experience with the scale identified the need for greater discrimination between levels of performance within the normal range. We report here the development of a modified scale (Oxford NOTECHS II) to facilitate this. The new measure uses an eight-point instead of a four point scale to measure each dimension of non-technical skills, and begins with a default rating of 6 for each element. We evaluated this new scale in 297 operations at five NHS sites in four surgical specialities. Measures of theatre process reliability (glitch count) and compliance with the WHO surgical safety checklist were scored contemporaneously, and relationships with NOTECHS II scores explored. Mean team Oxford NOTECHS II scores was 73.39 (range 37-92). The means for surgical, anaesthetic and nursing sub-teams were 24.61 (IQR 23, 27); 24.22 (IQR 23, 26) and 24.55 (IQR 23, 26). Oxford NOTECHS II showed good inter-rater reliability between human factors and clinical observers in each of the four domains. Teams with high WHO compliance had higher mean Oxford NOTECHS II scores (74.5) than those with low compliance (71.1) (p = 0.010). We observed only a weak correlation between Oxford NOTECHS II scores and glitch count; r = -0.26 (95% CI -0.36 to -0.15). Oxford NOTECHS II scores did not vary significantly between 5 different hospital sites, but a significant difference was seen between specialities (p = 0.001). Oxford NOTECHS II provides good discrimination between teams while retaining reliability and correlation with other measures of teamwork performance, and is not confounded by technical performance. It is therefore suitable for combined use with a technical performance scale to provide a global description of operating theatre team performance.

BackgroundThe introduction of new technology into the operating room (OR) can be beneficial for patients, but can also create new problems and complexities for physicians and staff. The observation of flow disruptions (FDs)—small deviations from the optimal course of care—can be used to understand how systems problems manifest. Prior studies showed that the docking process in robotic assisted surgery (RAS), which requires careful management of process, people, technology and working environment, might be a particularly challenging part of the operation. We sought to explore variation across multiple clinical sites and procedures; and to examine the sources of those disruptions.MethodsTrained observers recorded FDs during 45 procedures across multiple specialties at three different hospitals. The rate of FDs was compared across surgical phases, sites, and types of procedure. A work-system flow of the RAS docking procedure was used to determine which steps were most disrupted.ResultsThe docking process was significantly more disrupted than other procedural phases, with no effect of hospital site, and a potential interaction with procedure type. Particular challenges were encountered in room organization, retrieval of supplies, positioning the patient, and maneuvering the robot.ConclusionsDirect observation of surgical procedures can help to identify approaches to improve the design of technology and procedures, the training of staff, and configuration of the OR environment, with the eventual goal of improving safety, efficiency and teamwork in high technology surgery.

Background Resilience, in the field of Resilience Engineering, has been identified as the ability to maintain the safety and the performance of healthcare systems and is aligned with the resilience potentials of anticipation, monitoring, adaptation, and learning. In early 2020, the COVID-19 pandemic challenged the resilience of US healthcare systems due to the lack of equipment, supply interruptions, and a shortage of personnel. The purpose of this qualitative research was to describe resilience in the healthcare team during the COVID-19 pandemic with the healthcare team situated as a cognizant, singular source of knowledge and defined by its collective identity, purpose, competence, and actions, versus the resilience of an individual or an organization. Methods We developed a descriptive model which considered the healthcare team as a unified cognizant entity within a system designed for safe patient care. This model combined elements from the Patient Systems Engineering Initiative for Patient Safety (SEIPS) and the Advanced Team Decision Making (ADTM) models. Using a qualitative descriptive design and guided by our adapted model, we conducted individual interviews with healthcare team members across the United States. Data were analyzed using thematic analysis and extracted codes were organized within the adapted model framework. Results Five themes were identified from the interviews with acute care professionals across the US ( N  = 22): teamwork in a pressure cooker , consistent with working in a high stress environment; healthcare team cohesion , applying past lessons to present challenges , congruent with transferring past skills to current situations; knowledge gaps , and altruistic behaviors , aligned with sense of duty and personal responsibility to the team. Participants’ described how their ability to adapt to their environment was negatively impacted by uncertainty, inconsistent communication of information, and emotions of anxiety, fear, frustration, and stress. Cohesion with co-workers, transferability of skills, and altruistic behavior enhanced healthcare team performance. Conclusion Working within the extreme unprecedented circumstances of COVID-19 affected the ability of the healthcare team to anticipate and adapt to the rapidly changing environment. Both team cohesion and altruistic behavior promoted resilience. Our research contributes to a growing understanding of the importance of resilience in the healthcare team. And provides a bridge between individual and organizational resilience.

'The Problem with?' series covers controversial topics related to efforts to improve healthcare quality, including widely recommended but deceptively difficult strategies for improvement and pervasive problems that seem to resist solution. Since the seminal studies by Gawande and colleagues1 and Pronovost et al,2 checklists have become the go-to solution for a vast range of patient safety and quality issues in healthcare. Some see them as a quick and obvious solution to a relatively straightforward problem. For others, they illustrate a failure to understand and address the complex challenges in patient safety and quality improvement. Indeed, successes3 and failures4-6 illustrate an underlying difficulty with understanding precisely why checklists work in some cases but not in others. A recent viewpoint summarises the varying applications of checklists in aviation and healthcare, reflecting upon the dangers of making assumptions about their 'ubiquitous utility'.7 This provided a timely \"The Problem with?\"8 opportunity, in which we consider the narratives that often surround the challenges faced in designing and implementing a successful checklist, and the science used to explore it. 20 references

Frequently espoused by well meaning clinicians and aviators, rather than academically qualified HF professionals, it has led to misunderstandings about the range of approaches, knowledge, science and techniques that can be applied from the field of HF to address patient safety and quality of care problems. [...]in healthcare, CRM was among the first, and has been by far the most dominant HF paradigm. 5 Table 1 Aviation examples 29 Healthcare examples 30 Equipment design A cockpit is designed to minimise perceptual and control errors, 31 security systems have been developed to reduce operator fatigue and boredom and enhance training opportunities 32 Equipment predisposes to control 33 and perceptual errors, 34 often poorly maintained, with significant gaps in engineering for safety 35 Task design Design based on a thorough understanding of what is needed to get an aircraft safely from A to B 36 Lack of standardisation, 37 professional autonomy, differences between practice settings, and differing prioritisations of competing goals gave rise to widespread variation in individual behaviours and institutional protocols, 38 making task definition a challenge Communications and teamwork Structured communications are embedded within tasks that are well defined, trained and practiced 39 Safety communications 40 and tasks 41 are highly variable and tasks can be intermittently performed 41 Selection and training Specific scientific approach, 42 including simulation and recurrent training 30 Training follows the apprentice model, with little attention to rigorously establishing which skills are essential or even for evaluating the degree to which these skills have been successfully acquired 43 Incident reporting systems Encouraged reporting behaviours. 44 'Black box' flight recorders allow the detailed independent reconstruction of accidents 45 Usually ineffective, 46 with reconstruction of incidents rarely possible, 47 and 'black boxes' culturally difficult to employ.\\n 29 From gate keepers of knowledge to trusted colleagues The speed with which HF ideas have spread in healthcare reflects recognition of the tremendous need for the application of HF expertise.

Early work in the 1950s focused on improvements within military and industrial environments including the design of equipment, the layout of workspaces and the health and safety of workers. 9 During the 1960s and 1970s, other specialisms such as cybernetics, systems engineering and management studies became popular and resulted eventually in the adoption of the systems approach as one of the main components of modern-day HFE. 10 Figure 1 shows a recent example of an HFE systems model which uses an 'onion' metaphor to depict the various factors influencing performance and effective work design. 11 While being widely championed in patient safety, where factors related to individuals, technology and the wider organisation are afforded equal consideration and analysed in parallel, there is also evidence that the systems approach within HFE and patient safety is still underexploited and could be taken much further. 12-14 HFE scientists and practitioners apply a holistic approach in order to understand complex interacting systems and subsystems involving people. In the 1960s, a number of studies showed that designers and engineers had little or no interest in human factors, partly since human factors information was seen as inaccessible as compared with charts, graphs and tables.\\n Table 1 uses the metaphor of 'layers' within the systems ('onion') model ( figure 1 ) in order to illustrate examples of gaps of coverage in our current use of HFE within healthcare.

Background Operating room (OR) turnover time, time taken between one patient leaving the OR and the next entering, is an important determinant of OR utilization, a key value metric for hospital administrators. Surgical robots have increased the complexity and number of tasks required during an OR turnover, resulting in highly variable OR turnover times. We sought to streamline the turnover process and decrease robotic OR turnover times and increase efficiency. Methods Direct observation of 45 pre-intervention robotic OR turnovers was performed. Following a previously successful model for handoffs, we employed concepts from motor racing pit stops, including briefings, leadership, role definition, task allocation and task sequencing. Turnover task cards for staff were developed, and card assignments were distributed for each turnover. Forty-one cases were observed post-intervention. Results Average total OR turnover time was 99.2 min (95% CI 88.0–110.3) pre-intervention and 53.2 min (95% CI 48.0–58.5) at 3 months post-intervention. Average room ready time from when the patient exited the OR until the surgical technician was ready to receive the next patient was 42.2 min (95% CI 36.7–47.7) before the intervention, which reduced to 27.2 min at 3 months (95% CI 24.7–29.7) post-intervention ( p  < 0.0001). Conclusions Role definition, task allocation and sequencing, combined with a visual cue for ease-of-use, create efficient, and sustainable approaches to decreasing robotic OR turnover times. Broader system changes are needed to capitalize on that result. Pit stop and other high-risk industry models may inform approaches to the management of tasks and teams.

Background Flow disruptions (FDs) are deviations from the progression of care that compromise safety or efficiency. The frequency and specific causes of FDs remain poorly documented in trauma care. We undertook this study to identify and quantify the rate of FDs during various phases of trauma care. Methods Seven trained observers studied a Level I trauma center over 2 months. Observers recorded details on FDs using a validated Tablet-PC data collection tool during various phases of care—trauma bay, imaging, operating room (OR)—and recorded work-system variables including breakdowns in communication and coordination, environmental distractions, equipment issues, and patient factors. Results Researchers observed 86 trauma cases including 72 low-level and 14 high-level activations. Altogether, 1,759 FDs were recorded (20.4/case). High-level trauma comprised a significantly higher number ( p  = 0.0003) and rate of FDs ( p  = 0.0158) than low-level trauma. Across the three phases of trauma care, there was a significant effect on FD number ( p  < 0.0001) and FD rate ( p  = 0.0005), with the highest in the OR, followed by computed tomography. The highest rates of FD per case and per hour were related to breakdowns in coordination. Conclusions This study is the largest direct observational study of the trauma process conducted to date. Complexities associated with the critical patient who arrives in the trauma bay lead to a high prevalence of disruptions related to breakdowns in coordination, communication, equipment issues, and environmental factors. Prospective observation allows individual hospitals to identify and analyze these systemic deficiencies. Appropriate interventions can then be evaluated to streamline the care provided to trauma patients.

To examine the effectiveness of a \"systems\" approach using Lean methodology to improve surgical care, as part of a programme of studies investigating possible synergy between improvement approaches. A controlled before-after study using the orthopaedic trauma theatre of a UK Trust hospital as the active site and an elective orthopaedic theatre in the same Trust as control. All staff involved in surgical procedures in both theatres. A one-day \"lean\" training course delivered by an experienced specialist team was followed by support and assistance in developing a 6 month improvement project. Clinical staff selected the subjects for improvement and designed the improvements. We compared technical and non-technical team performance in theatre using WHO checklist compliance evaluation, \"glitch count\" and Oxford NOTECHS II in a sample of directly observed operations, and patient outcome (length of stay, complications and readmissions) for all patients. We collected observational data for 3 months and clinical data for 6 months before and after the intervention period. We compared changes in measures using 2-way analysis of variance. We studied 576 cases before and 465 after intervention, observing the operation in 38 and 41 cases respectively. We found no significant changes in team performance or patient outcome measures. The intervention theatre staff focused their efforts on improving first patient arrival time, which improved by 20 minutes after intervention. This version of \"lean\" system improvement did not improve measured safety processes or outcomes. The study highlighted an important tension between promoting staff ownership and providing direction, which needs to be managed in \"lean\" projects. Space and time for staff to conduct improvement activities are important for success.