<sec> <title>BACKGROUND</title> Preventable surgical errors of varying degrees of physical, emotional, and financial harm account for a significant number of adverse events. These errors are frequently tied to systemic problems within a health system, including the absence of necessary policies/procedures, obstructive cultural hierarchy, and communication breakdown between staff. We developed an innovative, theory-based virtual reality training to promote understanding and sensemaking towards the holistic view of the culture of patient safety and high reliability. </sec> <sec> <title>OBJECTIVE</title> We aim to assess the effect of VR training on HCWs' understanding of contributing factors to patient safety events, sensemaking of patient safety culture, and high-reliability organization principles in the lab environment. Further, we aim to assess the effect of VR training on patient safety culture, TeamSTEPPS® behavior scores, and reporting of patient safety events in the surgery department of an academic medical center in the clinical environment. </sec> <sec> <title>METHODS</title> This mixed-methods study uses a pre- vs post-VR training study design involving attending faculty, residents, nurses, technicians of the department of surgery, and front-line HCWs in the operation rooms at an academic medical center. HCWs understanding of contributing factors to patient safety events will be assessed using a scale based on the Human Factors Analysis and Classification System. We will use the Data Frame Theory framework,17 supported by a semi-structured interview guide to capture the sensemaking process of patient safety culture and HRO principles. Changes in the culture of patient safety will be quantified using the Agency for Healthcare Research and Quality (AHRQ) Surveys on Patient Safety Culture™. TeamSTEPPS® behavior scores based on observation will be measured using the Teamwork Evaluation of Non-Technical Skills tool.20 Patient safety events reported in the voluntary institutional reporting system will be compared pre- vs. post-training. We will compare the AHRQ patient safety culture scores, and patient safety events reporting pre- vs. post-training using descriptive statistics and within-subjects two-tailed, two sample t-test with significance level set at 0.05. </sec> <sec> <title>RESULTS</title> Ethical approval was obtained in May 2021 from the Institutional Review Board of the University of North Carolina at Chapel Hill. Enrollment of participants for this study will start in Fall 2022 and is expected to be completed by early spring 2023. The data analysis is expected to be completed by July 2023. </sec> <sec> <title>CONCLUSIONS</title> Our findings will help assess the effectiveness of VR training in improving HCWs’ understanding of contributing factors of patient safety events, sensemaking of patient safety culture, and HRO principles and behaviors. These findings will contribute to developing VR training to improve patient safety culture in other specialties. </sec>
These results indicate that high dietary quality may contribute to the preservation of overall health during aging, partly via obesity prevention and partly via other mechanisms.
344 Background: Multiple stakeholders have called for the development of tools to facilitate values elicitation with patients to inform treatment decision making about chemotherapy. We previously developed a best-worst scaling (BWS) measure, a theory-driven values elicitation tool, for older adults (≥ 60 years) with blood cancers through patient and expert engagement. We sought to evaluate the feasibility and validity of using this measure to inform initial treatment decisions for newly-diagnosed patients. Methods: We completed a convergent mixed-methods study to test feasibility and validity of remote delivery of the measure following guidance for choice experiments (Janssen, Exp Rev Pharm Out Res, 2017). Convergent validity was evaluated by comparing preferences with a ranking exercise. Content validity was tested in cognitive interviews. Results: 178 patients at a public safety net hospital were eligible, 157 (88%) were approached, and 48 (31%) consented (48% lymphoma, 40% leukemia/myelodysplastic syndrome, 12% other). Mean age was 73.9 years (range 60 - 95). 59% were male; 96% were white; 67% had a bachelor’s degree or higher. 52% completed the BWS measure prior to their initial treatment visit. Patients agreed or strongly agreed that the instrument was understandable (96%), relevant to them (58%), and showed their real preferences (75%). 50% felt it was easy to answer. 54% of responses from the BWS measure were concordant with ranking on their top priority. Cognitive interviews (n=7) demonstrated content validity: all patients were able to accurately describe the instructions of the BWS instrument and correctly define each included outcome; most patients reported that the BWS instrument helped solidify their preferences; all patients reported it was beneficial; no patients reported missing domains of outcomes; all reported that the time and emotional burden to complete the measure were appropriate. Conclusions: Using this BWS measure to inform treatment decisions of older adults with newly-diagnosed blood cancers is feasible and appears valid for clinical use. Future work is needed to optimize implementation and validate the measure in different settings and with a larger and more diverse population.
Chart checking is a time intensive process with high cognitive workload for physicists. Previous studies have partially automated and standardized chart checking, but limited studies implement data-driven approaches to reduce cognitive workload for quality assurance processes. This study aims to evaluate feature selection methods to improve the interpretability and transparency of machine learning models in predicting the degree of difficulty for a pretreatment physics chart check. We compare chi-square, mutual information, feature importance thresholding, and greedy feature selection for four different classifiers. Random forest has the highest performance with SMOTE oversampling using mutual information for feature selection (accuracy 84.0%, AUC 87.0%, precision 80.0%, recall 80.0%). This study demonstrates that feature selection methods can improve model interpretability and transparency.
Dietary behaviour of patients significantly differed from subjects from the general population.
Read moreThe interpretation of breast magnetic resonance imaging (MRI) in the healthcare field depends on the good knowledge and experience of radiologists. Recent developments in artificial intelligence (AI) have shown advances in the field of radiology. However, the desired levels have not been reached in the field of radiology yet. In this study, a novel model structure is proposed to characterize the diagnostic performance of AI technology for individual breast dynamic contrast material–enhanced (DCE) MRI sequences. In the proposed model structure, Inception-v3, EfficientNet-B3, and DenseNet-201 models were used as hybrids together with the Yolo-v3 algorithm to detect breast and cancer regions. In the proposed model, DCE-MRI sequences (T2, ADC, Diffusion, Non-Contrast Fat Non-Suppressed T1, Non-Contrast Fat Suppressed T1, Contrast Fat Suppressed T1, and Subtraction T1) were evaluated separately and validation was made, thus providing a unique perspective. According to the validation results, the model structure with the best performance was determined as Yolo-v3 + DenseNet-201. With this model structure, 92.41% accuracy, 0.5936 loss, 92.44% sensitivity, and 92.44% specificity rates were obtained. In addition, it was determined that the results obtained without using contrast material in the best model were 91.53% accuracy, 0.9646 loss, 92.19% sensitivity, and 92.19% specificity. Therefore, it is predicted that the need for contrast material use can be reduced with the help of this model structure.
Read moreYüz ifadesinden duygu tanıma; insan-bilgisayar etkileşimi, duygusal hesaplama vb. gibi birçok bilgisayarla görme alanında uygulanabilen güncel bir araştırma konusudur. Bu çalışmada, KDEF ve PICS veri setleri kullanılarak derin öğrenme ile duygu tanımaya yönelik bir uygulama yapılmıştır. Öznitelik çıkarımı için derin öğrenme tekniklerinden olan ve yapay sinir ağları içeren bir yapay zekâ yaklaşımı olan Evrişimsel Sinir Ağı (ESA) kullanılarak yeni bir model geliştirilmiştir. Derin öğrenmenin yüksek başarımı için büyük veri setine ihtiyaç duyulmaktadır. KDEF veri setinde 4900, PICS veri setinde 322 görüntü bulunmaktadır. Bu amaçla öncelikle PICS veri setindeki görüntü sayısının az olmasından dolayı veri artırma yöntemi ile görüntü çoğaltma işlemi uygulanmıştır ve PICS veri seti 4830 görüntüye çıkarılmıştır. Daha sonra bu iki farklı veri seti üzerinde ayrı ayrı eğitim gerçekleştirilerek geliştirilen yeni model test edilmiştir. ESA modellerinden olan VGGNet temel alınarak geliştirilen yeni model ile gerçekleştirilen çalışmada, her bir veri setinde yedi farklı duygu sınıfı (korku, öfke, iğrenme, mutluluk, nötr, üzüntü, şaşırma) ele alınmıştır. Geliştirilen model ile KDEF veri setinin geçerleme kümesinde %97.44, PICS veri setinin geçerleme kümesinde %98.24 doğruluk değerleri elde edilerek yüksek bir başarı oranına ulaşılmıştır.
Read morePURPOSE: This study aims to examine the sequential mediating roles of job stress (WS) and work-family conflict (WFC) in the relationship between compulsory citizenship behaviour (CCB) and citizenship fatigue (CF) among healthcare workers in Turkish public hospitals, grounded in Conservation of Resources (COR) theory and the job demands-resources (JD-R) model. DESIGN/METHODOLOGY/APPROACH: A cross-sectional design was employed with 483 healthcare workers (67.7% female), including nurses, physicians, and administrative staff. Validated Turkish scales measured CCB, CF, WS and WFC. Data were analysed using correlation analysis, structural equation modelling (SEM) and Hayes' PROCESS macro (Model 6, 5,000 bootstrap samples). FINDINGS: CCB was positively associated with CF (β = 0.43, p < 0.001). Job stress and WFC sequentially mediated this relationship (total indirect effect β = 0.18, SE = 0.03, LLCI = 0.13 and ULCI = 0.25 95%). Results align with COR theory, demonstrating that CCB depletes resources, escalating stress and WFC, ultimately leading to CF. ORIGINALITY/VALUE: This study advances the literature by empirically testing a novel serial mediation model in healthcare, highlighting how CCB's effects extend beyond the workplace into family life. It underscores the need for organisational interventions to mitigate compulsory demands and support work-life balance.
Read morePurpose: Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans. Methods and Materials: Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector machine, random forest classifier, adaboost classifier, decision tree classifier, and neural network. These were incorporated into a voting classifier, where at least 2 algorithms needed to predict a case as difficult for it to be classified as such. Sensitivity analyses were conducted to evaluate feature importance. Results: The voting classifier achieved an overall accuracy of 77.4% on the test set, with 76.5% accuracy on difficult cases and 78.4% accuracy on less difficult cases. Sensitivity analysis showed features associated with plan complexity (number of fractions, dose per monitor unit, number of planning structures, and number of image sets) and clinical relevance (patient age) were sensitive across at least 3 algorithms. Conclusions: This approach can be used to equitably allocate plans to physicists rather than randomly allocate them, potentially improving pretreatment chart check effectiveness by reducing errors propagating downstream.
Read moreINTRODUCTION: Effective electronic health record (EHR)-based training interventions facilitate improved EHR use for healthcare providers. One such training intervention is simulation-based training that emphasises learning actual tasks through experimentation in a risk-free environment without negative patient outcomes. EHR-specific simulation-based training can be employed to improve EHR use, thereby enhancing healthcare providers' skills and behaviours. Despite the potential advantages of this type of training, no study has identified and mapped the available evidence. To fill that gap, this scoping review will synthesise the current state of literature on EHR simulation-based training. METHODS AND ANALYSIS: The Arksey and O'Malley methodological framework will be employed. Three databases (PubMed, Embase and Cumulative Index to Nursing and Allied Health Literature) will be searched for published articles. ProQuest and Google Scholar will be searched to identify unpublished articles. Databases will be searched from inception to 29 January 2020. Only articles written in English, randomised control trials, cohort studies, cross-sectional studies and case-control studies will be considered for inclusion. Two reviewers will independently screen titles and abstracts against inclusion and exclusion criteria. Then, they will review full texts to determine articles for final inclusion. Citation chaining will be conducted to manually screen references of all included studies to identify additional studies not found by the search. A data abstraction form with relevant characteristics will be developed to help address the research question. Descriptive numerical analysis will be used to describe characteristics of included studies. Based on the extracted data, research evidence of EHR simulation-based training will be synthesised. ETHICS AND DISSEMINATION: Since no primary data will be collected, there will be no formal ethical review. Research findings will be disseminated through publications, presentations and meetings with relevant stakeholders.
Read moreFish is a very diverse animal of all vertebrate animal groups, of which there are more than 33,000 species in the world. There are different types of fish in the four major geographical regions of Turkey. Classification of different fish species is very important for aquaculture, stock management of water bodies, monitoring of aquatic organisms and conservation of marine biology. In the classification of fish, both knowledge and great effort are required to determine the characteristics of fish. Traditionally, however, manual classification of extrinsic characteristics of different fish species has been a difficult and time-consuming process due to their close resemblance to each other. Recently, deep learning methods used in the light of developments in the field of computer vision have facilitated the training of fish image classification models and the recognition of various fish species. In this study, a new evolutionary neural network model classifying 8 different fish species using deep learning methods was proposed. The proposed model is compared with the ResNet-50, ResNet-101 and VGG16 models. The success accuracies obtained as a result of the comparison are respectively; 98.12% in the proposed model, 91.37% in the ResNet-50 model, 86.12% in the ResNet-101 model and 97.75% in the VGG16 model. It has been observed that the proposed model classifies sea fish, which is widely consumed in our country, with higher performance compared to other models.
Read moreMalaria is a disease that causes a parasite called plasmodium to be transmitted to humans as a result of the bite of female anopheles’ mosquitoes. Malaria is detected by examining the blood sample taken from the patient as a result of a microbiological examination under a microscope by specialist physicians. Although microscopy is widely used, its efficiency is low because it is time-consuming and depends on the interpretation of the specialist physician. In recent years, deep learning methods used in the field of computer vision increase the efficiency of specialist physicians by making a significant contribution to the decision-making process in solving real-life problems. In this study, ResNet architectures were preferred to quickly classify the malaria parasite using deep learning methods. For the training and testing of ResNet architectures, a dataset consisting of a total of 27558 red blood cell images containing 13779 parasitized and 13779 uninfected were used. Using this dataset, ResNet architectures were compared. As a result of the comparison, the best success accuracy (94.09%) was obtained with the ResNet-50 v2 model.
Read moreRecently, coronavirus disease (Covid-19) has become a serious public health threat, spreading worldwide in a very short time and threatening the lives of millions. Furthermore, many being infected with coronavirus have the potential to transmit the disease without showing any symptoms. Covid-19 causes upper respiratory and lung infections in many patients. With the increasing number of cases and mutations, medical resources are being drained day by day due to the rapid transmission of the disease, and the health systems of many countries are negatively affected. For this reason, it is very important to use available resources appropriately and timely for the detection and treatment of the disease. In this study, VGG16 and ResNet50 deep learning models were used to quickly evaluate x-ray images and to make the pre-diagnosis of Covid-19, and an alternative model was proposed. The proposed model was developed using the convolutional neural network deep learning architecture. VGG16, ResNet50 and the proposed model were trained and tested using a total of 12,739 x-ray images belonging to 6,157 patients (9,121 images with Covid-19 findings and 3,618 with normal findings reported by specialist physicians). As a result of the training of the models, success accuracy of 99.92% in the VGG16 model, 99.65% in the ResNet50 model and 99.76% in the proposed model were obtained.
Read moreAbstract Background Health care providers are now required to provide their patients access to their consultation and progress notes. Early research of this concept, known as “OpenNotes,” showed promising results in terms of provider acceptability and patient adoption, yet objective evaluations relating to patients' interactions with the notes are limited. Objectives To assess the effect of the complexity level of notes and number of accesses (initial read vs. continuous access) on the user's performance, perceived usability, cognitive workload, and satisfaction with the notes. Methods We used a 2*2 mixed subjects experimental design with two independent variables: (1) note's complexity at two levels (simple vs. complex) and (2) number of accesses to notes at two levels (initial vs. continuous). Fifty-three participants were randomly assigned to receive a simple versus complex radiation oncology clinical note and were tested on their performance for understanding the note content after an initial read, and then with continuous access to the note. Performance was quantified by comparing each participant's answers to the ones developed by the research team and assigning a score of 0 to 100 based on participants' understanding of the notes. Usability, cognitive workload, and satisfaction scores of the notes were quantified using validated tools. Results Performance for understanding was significantly better in simple versus complex notes with continuous access (p = 0.001). Continuous access to the notes was also positively associated with satisfaction scores (p = 0.03). The overall perceived usability, cognitive workload, and satisfaction scores were considered low for both simple and complex notes. Conclusion Simplifying notes can improve understanding of notes for patients/families. However, perceived usability, cognitive workload, and satisfaction with even the simplified notes were still low. To make notes more useful for patients and their families, there is a need for dramatic improvements to the overall usability and content of the notes.
Read moreReducing the size of magnetorheological (MR) actuators imposes severe constraints on magnetic volume and excitation efficiency, thereby limiting the achievable torque output, particularly at the 20 mm-class outer-diameter scale. To address this limitation, a miniaturized multi-drum MR actuator is proposed for enhanced torque output. It features multiple concentric annular shear gaps operating in radial shear mode and a shared magnetic flux path to improve flux utilization. Finite-element analysis is conducted to guide the magnetic circuit design, revealing a radial field distribution inherent to flux-sharing architectures and motivating an optimization strategy that drives the inner shear gap and magnetic paths close to saturation simultaneously. An optimized actuator prototype with an outer diameter of 20.8 mm, a height of 21.7 mm, and a total mass of 42.2 g was fabricated. Experimental characterization demonstrates that the actuator delivers a maximum torque of 91 mN·m with a low off-state torque of approximately 2 mN·m, achieving a substantial performance improvement over representative prior designs of comparable dimensions.
Read moreGiven the persistent safety incidents in operating rooms (ORs) nationwide (approx. 4000 preventable harmful surgical errors per year), there is a need to better analyze and understand reported patient safety events. This study describes the results of applying the Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) supported by the Teamwork Evaluation of Non-Technical Skills (TENTS) instrument to analyze patient safety event reports at one large academic medical center. Results suggest that suboptimal behaviors stemming from poor communication, lack of situation monitoring, and inappropriate task prioritization and execution were implicated in most reported events. Our proposed methodology offers an effective way of programmatically sorting and prioritizing patient safety improvement efforts.
Read moreBu calismada, bilisim teknolojileri (BT) calisanlarinin yenilik yapma davranislarinin bazi demografik ozelliklere gore farklilik gosterip gostermediginin tespit edilmesi amaclanmistir. Bu amacla Istanbul’daki BT calisanlarina bazi demografik sorularin yaninda Yenilikci Davranis Anketi uygulanmistir. Verilerin analizinde; guvenilirlik analizi, aciklayici faktor analizi (AFA), dogrulayici faktor analizi (DFA) ve tek yonlu varyans analizi teknikleri kullanilmistir. AFA ile incelenen olcegin gecerliligi DFA ile de dogrulanmistir. Olcegin BT calisanlarinin yenilikci davranisini olcmek icin kullanilabilecegine karar verilmistir. Ayrica olcegin oldukca guvenilir oldugu gorulmustur (Cronbach Alpha katsayisi = 0.932). Arastirma sonucunda, BT calisanlarinin yenilikci davranisinin cinsiyet (p = 0.030) ve uzmanlik alanina (p = 0.008) gore anlamli farklilik gosterdigi, yas (p = 0.102), medeni durum (p = 0.633), egitim durumu (p = 0.840) ve mesleki deneyime (p = 0.536) gore farklilik gostermedigi tespit edilmistir.
Read morePurpose This paper aims to present an aerial manipulator that can measure the crack depth of concrete structures. Currently, the detection of crack depth in concrete structures depends mainly on manual operations with nondestructive testing (NDT) instruments. There still lacks automatic equipment to release the workers at height from the highly dangerous and time-consuming NDT detection tasks. Design/methodology/approach The proposed aerial manipulator consists of a hexacopter, a two degrees of freedom (DOF) manipulator and a custom-designed ultrasonic crack detector based on the ultrasonic flat-measured principles. A four-phase control strategy is proposed for the aerial manipulator to complete the concrete crack depth measuring task. Findings The experimental results show that the proposed control strategy is effective and the prototype of the aerial manipulator succeeds in contact detection of the cracks in the concrete specimens. The field test results further verify that the proposed aerial manipulator is capable of contact inspection of bridge piers and other similar concrete structures. Originality/value The proposed aerial manipulator provides an unmanned aerial vehicle (UAV) based solution for NDT detection of concrete crack depth. It serves as a novel tool for the inspection personnel working at height to get the interior damage conditions of concrete structures safely and quickly.
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