Introduction

Bracing, a strategy employed by humans and robotic devices, can be generally described as a parallel mechanical link between the actor, the environment, and/or the workpiece that alters the mechanical impedance between the tool and workpiece in order to improve task performance. In this study we investigated the potential value of bracing in the context of bone milling to treat cam-type femoroacetabular impingement (FAI) lesions. The goal of this study was to evaluate whether a proposed bracing technique could enable a user to perform a cam resection more accurately and quickly than a currently employed arthroscopic technique.

We describe the impact of a targeted performance improvement programme and the associated performance improvement interventions, on mortality rates, error rates and process of care for haemodynamically unstable patients with pelvic fractures. Clinical care and performance improvement data for 185 adult patients with exsanguinating pelvic trauma presenting to a United Kingdom Major Trauma Centre between January 2007 and January 2011 were analysed with univariate and multivariate regression and compared with National data. In total 62 patients (34%) died from their injuries and opportunities for improved care were identified in one third of deaths. Three major interventions were introduced during the study period in response to the findings. These were a massive haemorrhage protocol, a decision-making algorithm and employment of specialist pelvic orthopaedic surgeons. Interventions which improved performance were associated with an annual reduction in mortality (odds ratio 0.64 (95% confidence interval (CI) 0.44 to 0.93), p = 0.02), a reduction in error rates (p = 0.024) and significant improvements in the targeted processes of care. Exsanguinating patients with pelvic trauma are complex to manage and are associated with high mortality rates; implementation of a targeted performance improvement programme achieved sustained improvements in mortality, error rates and trauma care in this group of severely injured patients. Cite this article: Bone Joint J 2014;96-B:1090–7

The fractures of the hip are a main problem of health care. These fractures will be multiply by 2 in 2025 and 3 in 2050. In the population of old people, 1/3 of the women and 1/9 of the men will have a fracture of their hip. Some authors used the name of “EPIDEMIC”.

The aima of the INPECH project (Indicators of the Performance for the comparison between hospital) was to check if the comparison of performances of clinical teams, on the basis of indicators chosen by themselves, allowed to improve their performances. It was an experimentation of a volonteer processes of comparison of performance between 3 multidisciplinary teams.

The study followed some stages:

in every hospital, description of the process of care of the hip fractures with a choice of indicators of performance (difficulties of the care percepted by the professionals, possibilities of improvement, feasibility of the record of the indicators during the whole process)

The analysis of this second inclusion authorized a real change in our professional practices and showed the efficacy of the indicators which were improved.

This study had permitted to show qualitative differences of care of hip fractures between the hospitals. The confrontation between the teams had authorized a positive reflexion of some of our professional practices and the improvement was underlined by the second series of inclusion.

Introduction:. Most of the published papers on AI based diagnosis have focused on the algorithm's diagnostic performance in a ‘binary’ setting (i.e. disease vs no disease). However, no study evaluated the actual value for the clinicians of an AI based approach in diagnostic. Detection of Traumatic thoracolumbar (TL) fractures is challenging on planar radiographs, resulting in significant rates of missed diagnoses (30-60%), thus constituting a field in which a performance improvement is needed. Aim of this study is therefore to evaluate the value provided by AI generated saliency maps (SM), i.e. the maps that highlight the AI identified region of interests. Methods:. An AI model aimed at identifying TL fractures on plain radiographs was trained and tested on 567 single vertebrae images. Three expert spine surgeons established the Ground Truth (GT) using CT and MRI to confirm the presence of the fracture. From the test set, 12 cases (6 with a GT of fracture and 6 with a GT of no fracture, associated with varying levels of algorithm confidence) were selected and the corresponding SMs were generated and shown to 7 independent evaluators with different grade of experience; the evaluators were requested to: (1) identify the presence or absence of a fracture before and after the saliency map was shown; (2) grade, with a score from 1 (low) to 6 (high) the pertinency (correlation between the map and the human diagnosis), and the utility (the perceived utility in confirming or not the initial diagnosis) of the SM. Furthermore, the usefulness of the SM was evaluated through the rate of correct change in diagnosis after the maps had been shown. Finally, the obtained scores were correlated with the algorithm confidence for the specific case. Results:. Of the selected maps, 8 had an agreement between the AI diagnosis and the GT, while in 4 the diagnosis was discordant (67% accuracy). The pertinency of the map was found higher when the AI diagnosis was the same as the GT and the human diagnosis (respectively p-value = .021 and <.000). A positive and significant correlation between the AI confidence score and the perceived utility (Spearman: 27%, p-value=.0-27) was found. Furthermore, evaluator with experience < 5 year found the maps more useful than the experts (z-score=2.004; p-value=.0455). Among the 84 evaluation we found 12 diagnostic errors in respect to the GT, 6 (50%) of which were reverted after the saliency map evaluation (z statistic = 1.25 and p-value = .21). Discussion:. The perceived utility of AI generated SM correlate with the model confidence in the diagnosis. This highlights the fact that to be considered helpful, the AI must provide not only the diagnosis but also the case specific confidence. Furthermore, the perceived utility was higher among less experienced users, but overall, the SM were useful in improving the human diagnostic accuracy. Therefore, in this setting, the AI enhanced approach provides value in improving the human performance

Although wait-times for hip fracture surgery have been linked to mortality and are being used as quality-of-care indicators worldwide, controversy exists about the duration of the wait that leads to complications. Our objective was to use new population-based wait-time data to emprically derive an optimal time window in which to conduct hip fracture surgery before the risk of complications increases. We used health administrative data from Ontario, Canada to identify hip fracture patients between 2009 and 2014. The main exposure was the time from hospital arrival to surgery (in hours). The primary outcome was mortality within 30 days. Secondary outcomes included a composite of mortality or other medical complications (MI, DVT, PE, and pneumonia) also within 30 days. Risk-adjusted cubic splines modeled the probability of each complication according to wait-time. The inflection point (in hours) when complications began to increase was used to define ‘early’ and ‘delayed’ surgery. To evaluate the robustness of this definition, outcomes amongst propensity-score matched early and delayed patients were compared using percent absolute risk differences (% ARDs, with 95% confidence intervals [CIs]). There were 42,230 patients who met entry criteria. Their mean age was 80.1 (±10.7) and the majority were female (70.5%). The risk of complications modeled by cubic splines consistently increased when wait-times were greater than 24 hours, irrespective of the complication considered. Compared to 13,731 propensity-score matched patients who received surgery earlier, 13,731 patients receiving surgery after 24 hours had a significantly higher risk of 30-day mortality (N=898 versus N=790, % ARD 0.79 [95% CI 0.23 to 1.35], p = .006) and the composite outcome (N=1,680 versus N=1,383, % ARD 2.16 [95% CI 1.43 to 2.89], p < .001). Overall, there were 14,174 patients (33.6%) who received surgery within 24 hours and 28,056 patients (66.4%) who received surgery after 24 hours. Increased wait-time was associated with a greater risk for 30-day mortality and other complications. The finding that a wait-time of 24 hours represents a threshold defining higher risk may inform existing hip fracture guidelines. Since two-thirds of patients did not receive surgery within this timeframe, performance improvement efforts that reduce wait-times are warranted

The February 2015 Trauma Roundup. 360 . looks at: Evaluating the syndesmosis in ankle fractures; Calcaneal fracture management an ongoing problem; Angular stable locking in low tibial fractures did not improve results; Open fractures: do the seconds really count?; Long-term outcomes of tibial fractures; Targeted performance improvements in pelvic fractures

Introduction. Motion analysis is a validated method of assessing technical dexterity within surgical skills centers. A more accessible and cost-effective method of skills assessment is to use a global rating scale (GRS). We aimed to perform a validation experiment to compare an arthroscopic GRS against motion analysis for monitoring orthopaedic trainees learning simulated arthroscopic meniscal repairs. Methods. An arthroscopic meniscal repair task on a knee simulator was set up in a bioskills laboratory. Nineteen orthopaedic trainees with no experience of meniscal repair were recruited and their performance assessed whilst undertaking a standardized meniscal repair on 12 occasions. An arthroscopic GRS, assessing parameters such as “depth perception,” “bimanual dexterity,” “instrument handling,” and “final product analysis” was used to evaluate technical skill. Performance was assessed blindly by watching video recordings of the arthroscopic tasks. Dexterity analysis was performed using a motion analysis tracking system which measured “time taken,” “total path length of the subject's hands,” and “number of hand movements”. Results. Motion analysis objectively defined the learning curves and demonstrated significant improvement in performance over the 12 tasks (p< 0.0001). The GRS demonstrated the same learning curve with a significant improvement in performance (p< 0.0001). Importantly, for each individual subject, there was significant improvement in performance as assessed by GRS over the 12 tasks (p< 0.0001). There was a moderate correlation (p< 0.0001) between GRS and all the motion analysis parameters (r values: time=−0.58, path length=−0.58, hand movements=−0.51). Conclusion. Established arthroscopic GRSs have not previously been used to monitor learning curves during complex arthroscopic tasks. The results demonstrate that both the GRS and motion analysis are able to detect performance improvement during such tasks. This further validates the arthroscopic GRS for use in monitoring individual trainees and has the advantage over motion analysis of being directly transferrable to the operating room

Aims

The primary aim of this paper was to outline the processes involved in building the Partners Arthroplasty Registry (PAR), established in April 2016 to capture baseline and outcome data for patients undergoing arthroplasty in a regional healthcare system. A secondary aim was to determine the quality of PAR’s data. A tertiary aim was to report preliminary findings from the registry and contributions to quality improvement initiatives and research up to March 2019.

Aim

There is not adequate evidence to establish whether external fixation (EF) of pelvic fractures leads to a reduced mortality. We used the Japan Trauma Data Bank database to identify isolated unstable pelvic ring fractures to exclude the possibility of blood loss from other injuries, and analyzed the effectiveness of EF on mortality in this group of patients.