Exploring System Thinking Leadership Approaches to the Healthcare Cybersecurity Environment

A Florida-based obstetrics and gynecology facility reported in February 2019 that they lost data because of a ransomware attack. In November 2017, 107,000 healthcare records were exposed from data breaches, and 340,000 records were exposed in December 2017. In 2019, 23,000 patient records at Critical Care, Pulmonary & Sleep Associates were compromised when a hacker gained access to an employee's email account and sent out phishing emails to the other employees, eventually exposing the patient data. On January 11, 2018, Adams Memorial Hospital and Hancock Regional Hospital, both in Indiana, experienced independent ransomware attacks, with Hancock Regional Hospital paying $50,000 in ransom. These incidents point to significant and complex cybersecurity risks for all healthcare organizations. Effectively managing these risks requires healthcare managers to develop system thinking and adaptive leadership skills. This paper explores the nuances and complexities around systems thinking in the healthcare cybersecurity environment.

Variable Practice May Enhance Ball Catching Skills in Some but Not All Children With DCD

Children with DCD exhibit many motor issues including ball catching, which have not been addressed clinically. The purpose of this study was to examine if variable practice can improve one-handed catching performance of five boys with DCD with severe (M = 11.1 years, SD = .7) and moderate movement problems (M = 11. 7 years, SD = .9). Both groups attended 12 sessions, with 8 blocks of 10 attempts, across four different ball velocities. Children with moderate issues caught more balls at post- and transfer test, as compared to pre-test. Kinematic profiles showed that both groups exhibited stable configuration of the catching arm; however, only children with moderate issues increased (peak) velocity, degree of spatial adaptations, and extended the pre-programmed ballistic phase of the action. Thus, variable practice appeared to be effective for children with moderate issues but not those with severe problems. In line with VPH hypothesis, these improvements were due to strengthening of the schema responsible for spatial and temporal adaptations of the end effector.

Evaluating the Performance of a Hockey Helmet in Mitigating Concussion Risk Using Measures of Acceleration and Energy During Simulated Free Fall

Hockey helmets represent the best form of head protection available to reduce the occurrence of skull fracture and concussion. Currently, helmet testing protocols focus on the reduction of peak linear acceleration measures. Gaps exist in analyzing how certain impact factors such as angle, neck stiffness, and location influence the energy loaded to the helmet and the risk of injury during head collisions. This study examined the effect of helmet impact angle, neck stiffness-torque levels, and helmet impact locations on energy reduction and risk of head injury grounded on acceleration measures using simulated free fall head collisions. The researchers conducted 540 impacts to collect the data. The results revealed statistical interaction effects between the angle of impact and location on measures of energy and risk of head injury. This study builds on existing literature by introducing an energy measurement technique to assess helmet performance. The outcome also provides an avenue for helmet manufacturers to evaluate the performance of the helmet in reducing concussion risk.

Artificial Intelligence-Assisted Endoscopy in Ulcerative Colitis

Inflammatory bowel diseases (IBD) are disorders that cause chronic inflammation in the gastrointestinal (GI) tract. The two most common forms of IBD are Crohn's disease and ulcerative colitis (UC). Imaged by high-definition video-camera via the colonoscope, the mucosa of the colon is recorded and examined by the endoscopist. Endoscopy is the gold standard method of discerning the disease severity and the treatment outcome in patients with UC. Determining the severity and the extent of the disease is important in guiding the management. This is challenging due to inter-individual variation, subjectivity in reporting endoscopic scores, and human time commitment. To address these concerns, computational aids via artificial intelligence (AI) can contribute to the processing of endoscopy data. In this editorial, the authors provide an overview of AI use in the endoscopic assessment UC activity and severity.

The Effects of Taping on Muscle Activity and Throwing Velocity in Fatigued Baseball Players

Due to the repetitive high forces and torques placed on an individual during a baseball pitch, shoulder pain is present in 46-57% of pitchers. Therapeutic taping has been proposed to have beneficial qualities in injury prevention, rehabilitation, and performance enhancement via muscular facilitation. Therefore, the purpose of this pilot study was to investigate the effect of taping on the velocity of an overhead baseball throw and muscle activation patterning of the supraspinatus, infraspinatus, and pectoralis major muscles in baseball players after muscle fatigue was induced. Participants were asked to complete three pre-test maximum velocity overhead throws, a fatiguing protocol, followed by three post-test maximum velocity pitches. There was no statistically significant difference in throwing velocity or muscle activity with the application of the different taping conditions in the three phases of an overhead baseball throw. These finding suggest Kinesio Tape® does not change muscle activation or velocity of overhead baseball throws when compared to a no tape condition.

Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques

The traditional methods of cancer diagnosis and cancer-type recognition have quite a large number of limitations in terms of speed and accuracy. However, recent studies on cancer diagnosis are focused on molecular level identification so as to improve the capability of diagnosis process. By statistically analyzing the heart cancer datasets using a set of protocols and algorithms, gene expression profiles are efficiently analyzed. Various machine learning classifiers are used to classify the selected data. Cross-validation was performed to avoid overfitting and different ratios of training, and testing data was used to conclude the best optimization technique and classification algorithm for the heart cancer datasets. The data is optimized using optimization techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), and hybrid particle swarm optimization with grey wolf optimizer (HPSOGWO). Results show an improvement in the prediction accuracy of heart cancer by the hybrid algorithm as compared to PSO and GWO algorithms.

The Immediate Effects of Tai Chi via a Video Platform Delivery on the Postural Stability of Healthy Young Adults

Tai chi is effective for improving the postural stability of older adults, but the effects on healthy young adults has yet to be investigated. Currently, the use of virtual delivery platforms for various exercise prescriptions to patients has had to be quickly introduced related to the onset of the COVID-19 pandemic and physical distancing requirements. This study attempted to explore the pre-/post-test measures of postural stability from 15 healthy young adult participants under eyes open (EO) and eyes closed (EC) and firm/foam surface conditions after learning a battery of tai chi exercises and forms delivered by instructional video. Center of pressure-based measures of mean sway velocity (MSV) and 95% elliptical sway area (EA) were assessed. Three-way ANOVAs were conducted to determine if MSV and EA were significantly different across the eye conditions, surface conditions, and time. Both EA and MSV were found to significantly increase from EO to EC on a foam surface; however, only MSV under EC conditions was found to decrease significantly following the intervention.

Detection and Segmentation of Medical Images Using Generic Algorithms

Image processing plays an indispensable and significant role in the development of various fields like medical imaging, astronomy, GIS, disaster management, agriculture monitoring, and so on. Medical images which are recorded in digital forms are processed by high-end computers to extract whatever information we desire. In the fast-developing modern world of medical imaging diagnosis and prognosis, where manual photo interpretation is time-consuming, automatic object detection from devices like CT-Scans and MRIs has limited potential to generate the required results. This article addresses the process of identifying Region of Interests in cancer based medical images based on combination of Otsu’s algorithm and Canny edge detection methods. The primary objective of this paper is to derive meaningful and potential information from medical image in different scenarios by applying the image segementation in combination with genetic algorithms in a robust manner to detect region of interest.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

Impact of Attentional Loading and Task Constraints on Postural Control of Healthy Older Adults

Postural control is attention demanding, and it may be jeopardized when a secondary cognitive task is involved, particularly for older adults. The magnitude of this interference depends on different individual (perceptuo-motor status), task (single vs. dual tasking), and environmental constraints (support surface). The purpose of this research was to examine if older adults may be affected by various types of secondary cognitive tasks, while maintaining quiet standing on different support surfaces. In line with conceptual models, the results indicated that postural control of older adults was compromised when they were required to dual-task, especially when the support surface was challenging. This was a robust finding across all the measures of COP. In regards to the degree to which different attentional tasks affected postural control, the results remained equivocal. From a practical standpoint, the results indicated that older adults should exhibit caution when simultaneously performing a balance and a cognitive task involved particularly when the surface is unsteady.