A multi-method exploration of surgical incidents in UK context: causes, impact, support, and learning.

Introduction: Surgical incidents are events that occur during a surgical or invasive procedure in an operating theatre. When an incident happens, priority is rightly given to supporting the patient and their family. These incidents can also have a profound negative impact on the healthcare professionals involved. Aim: The overall aim of this PhD programme of work was to explore the impact of surgical incidents on operating theatre staff, what factors might have contributed to their occurrence, and how staff could be better supported following such events. Methods: The thesis is comprised of four stages. The researcher conducted a systematic review of the of the psychological, emotional, and behavioural impacts of surgical incidents on operating theatre staff (stage one). A second systematic review was carried out to explore what practical tools might help teams deconstruct and learn from safety incidents in various high reliability organisations and whether those tools could be adapted for use in the healthcare system (stage two). The researcher also conducted a retrospective review of surgical incidents to identify what factors might have contributed to the occurrence of serious surgical incidents at a large London NHS Trust (stage three). The researcher then conducted the first qualitative study in the UK to explore the personal, professional, and behavioural impact of surgical incidents on operating theatre staff (both medical and non-medical) and how they could be better supported following a surgical event (stage four). Results: The researcher found a significant knowledge gap around what structured support systems were currently in place to support theatre staff involved in surgical incidents (stage one). The second systematic review (stage two) revealed how high reliability organisations such as aviation and military use various learning tools such as debriefing, simulation, crew resource management and reporting systems to disseminate safety messages to their staff. The researcher found the following factors, including the task, equipment and resources, teamwork, work environmental, and organisational and management, contributed to the occurrence of surgical incidents (stage three). Theatre protocols were also found to be either unavailable, outdated, or not followed correctly. The lack of effective communication within multidisciplinary teams, and inadequate medical staffing levels were perceived to have also contributed. The researcher conducted 45 interviews with medical and non-medical operating staff (stage four), who emphasised the importance of receiving personalised support soon after the incident. Theatre staff described how the first “go to” person was their peers and reported feeling comforted when their peers empathised with their own experience(s). Other participants found it very difficult to receive support, perceiving it as a sign of weakness. Although family members played an important role in supporting second victims, some participants felt unable to discuss the incident with them, fearing that they might not understand. This study further highlighted unfairness during the investigation process in the treatment of non-medical theatre staff. Discussion and Conclusion: This study revealed the need for clear support structures to be put in place for theatre staff who have been involved in surgical incidents. Healthcare organisations need to offer timely support to front-line staff following these incidents. They need to encourage multidisciplinary team investigation process to promote fairness and transparency. Senior clinicians should be proactive in offering support to junior colleagues and empathise with their own experiences, thus shifting the competitive culture to one of openness and support. Healthcare organisations should find ways to adapt the learning tools or initiatives used in high reliability organisations following safety incidents. However, the way these tools or initiatives are implemented is critical and so further work is needed to explore how to successfully embed them into healthcare organisations

ST-V-Net: Incorporating Shape Prior Into Convolutional Neural Netwoks For Proximal Femur Segmentation

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance

A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images

Purpose: Proximal femur image analyses based on quantitative computed tomography (QCT) provide a method to quantify the bone density and evaluate osteoporosis and risk of fracture. We aim to develop a deep-learning-based method for automatic proximal femur segmentation. Methods and Materials: We developed a 3D image segmentation method based on V-Net, an end-to-end fully convolutional neural network (CNN), to extract the proximal femur QCT images automatically. The proposed V-net methodology adopts a compound loss function, which includes a Dice loss and a L2 regularizer. We performed experiments to evaluate the effectiveness of the proposed segmentation method. In the experiments, a QCT dataset which included 397 QCT subjects was used. For the QCT image of each subject, the ground truth for the proximal femur was delineated by a well-trained scientist. During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models. Results: Visual comparison demonstrated high agreement between the model prediction and ground truth contours of the proximal femur portion of the QCT images. In the entire cohort, the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 and a specificity of 0.9992. In addition, an R2 score of 0.9956 (p<0.001) was obtained when comparing the volumes measured by our model prediction with the ground truth. Conclusion: This method shows a great promise for clinical application to QCT and QCT-based finite element analysis of the proximal femur for evaluating osteoporosis and hip fracture risk

Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength

The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Method: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. With an analytical solution of the product of Gaussian distribution, we adopted variational inference to train the designed MVAE-PoE model to perform common latent feature extraction. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. Results: The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Compared to existing multi-view information fusion methods, the proposed MVAE-PoE achieved the best performance. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation dosage and clinical costs from QCT.Comment: 16 pages, 3 figure

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load

BackgroundHip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or “strength”) and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.ResultsWe developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively.ConclusionThe proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT

Surgical incidents and their impact on operating theatre staff: qualitative study

Background:Surgical incidents can have significant effects on both patients and health professionals, including emotional distress and depression. The aim of this study was to explore the personal and professional impacts of surgical incidents on operating theatre staff.Methods:Face-to-face semistructured interviews were conducted with a range of different healthcare professionals working in operating theatres, including surgeons and anaesthetists, operating department practitioners, and theatre nurses, and across different surgical specialties at five different hospitals. All interviews were audio recorded, transcribed verbatim, and analysed using an inductive thematic approach, which involved reading and re-reading the transcripts, assigning preliminary codes, and searching for patterns and themes within the codes, with the aid of NVivo 12 software. These emerging themes were discussed with the wider research team to gain their input. Results:Some 45 interviews were conducted, generally lasting between 30 and 75 min. Three overarching themes emerged: personal and professional impact; impact of the investigation process; and positive consequences or impact. Participants recalled experiencing negative emotions following surgical incidents that depended on the severity of the incident, patient outcomes, and the support that staff received. A culture of blame, inadequate support, and lack of a clear and transparent investigative process appeared to worsen impact. Conclusion:The study indicated that more support is needed for operating theatre staff involved in surgical incidents. Greater transparency and better information during the investigation of such incidents for staff are still needed

Enquête de satisfaction auprès des patients dans le Limousin en médecine générale

LIMOGES-BU Médecine pharmacie (870852108) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Evaluating users' experiences of electronic prescribing systems in relation to patient safety: a mixed methods study

Background User interface (UI) design features such as screen layout, density of information, and use of colour may affect the usability of electronic prescribing (EP) systems, with usability problems previously associated with medication errors. To identify how to improve existing systems, our aim was to explore prescribers’ perspectives of UI features of a commercially available EP system, and how these may affect patient safety. Methods Two studies were conducted, each including ten participants prescribing a penicillin for a test patient with a penicillin allergy. In study 1, eye-gaze tracking was used as a means to explore visual attention and behaviour during prescribing, followed by a self-reported EP system usability scale. In study 2, a think-aloud method and semi-structured interview were applied to explore participants’ thoughts and views on prescribing, with a focus on UI design and patient safety. Results Study 1 showed high visual attention toward information on allergies and patient information, allergy pop-up alerts, and medication order review and confirmation, with less visual attention on adding medication. The system’s usability was rated ‘below average’. In study 2, participants highlighted EP design features and workflow, including screen layout and information overload as being important for patient safety, benefits of EP systems such as keeping a record of relevant information, and suggestions for improvement in relation to system design (colour, fonts, customization) and patient interaction. Conclusions Specific UI design factors were identified that may improve the usability and/or safety of EP systems. It is suggested that eye-gaze tracking and think-aloud methods are used in future experimental research in this area. Limitations include the small sample size; further work should include similar studies on other EP systems