Measuring the Interprofessional Health of the Pediatric Cardiovascular Operating Room Work Environment.

Introduction: Pediatric cardiac surgery is complex and has significant risk, requiring interprofessional teamwork for optimal outcomes. Unhealthy work environments have been linked to poor patient outcomes, staff dissatisfaction, and intention to leave. We describe the interprofessional health of pediatric cardiovascular operating room (CVOR) work environments in the United States and the establishment of a healthy work environment (HWE) benchmark score. Methods: Utilizing the American Association of Critical Care Nurses Healthy Work Environments Assessment Tool (HWEAT), interprofessional staff from 11 pediatric CVORs were surveyed. Responses were aggregated, summarized, and stratified by role to examine differences. The following phase used an e-Delphi approach to obtain expert consensus on a benchmark target. Results: Across 11 centers, 179 (60%) completed surveys were reviewed. The interprofessional mean HWEAT score was 3.55 (2.65-4.34). Mean scores for each standard were within the "good" range. Participants reported the highest scores for effective decision-making, with a mean of 3.69 (3.00-4.20). Meaningful recognition scored lowest, mean 3.26 (2.33-4.07). When stratified, surgeons reported higher overall HWE scores (M = 3.79, SD = 0.13) than nurses (M = 3.41, SD = 0.19; P = 0.02, two-tailed). The proposed benchmark was 3.50. Conclusions: This is the first time the American Association of Critical Care Nurses HWEAT has been used to describe the interprofessional health of work environments in pediatric CVORs in the United States. The targeted benchmark can support pediatric CVOR improvement strategies. Creating and sustaining an HWE is an interprofessional opportunity to support high-quality patient outcomes and clinical excellence.

#Healthcare: Patient and Family Uses and Perceptions of Health Care Social Media.

BACKGROUND/INTRODUCTION: Although social media is becoming a primary resource for information and support in all aspects of life, including health care, limited information is available describing social media use in parents whose child undergoes surgical care. OBJECTIVES/AIMS: The aims of this study were to describe how patients/families use social media to address health care needs and understand their perceptions of social media privacy and reliability. METHODS: A descriptive survey of 39 questions, both fixed choice and open ended, was distributed to a convenience sample of parents during their child's preoperative visit. Descriptive statistics were used to summarize fixed-choice responses. Content analysis was used to assess open-ended responses and comments. RESULTS: A total of 205 completed surveys were available for review. Overall, 195 (95.6%) reported using social media, with 70 (35%) using social media up to 5 times a day and another 61 (30.5%) using it 6 to 40 times a day. Respondents used social media for medical information (122/60.1%), to make health care decisions (53/26.5%), after a diagnosis (104/52%), after a medical visit (88/44%), and to update friends and family (129/65.5%). Most respondents were undecided (111/58.1%) when asked how reliable medical information was on social media sites, with 33 (17.3%) believing medical information to be "reliable to very reliable" on social media sites. Among the 61 comments received, 4 themes emerged: Spectrum of Social Media Use, Social Media and Health Care Interaction, Social Media as a Source of Support and Peer Experience, and Reliability of Social Media. DISCUSSION: Most respondents utilized social media for health care information while reporting feeling undecided on the reliability of the information. Understanding the multiple ways patients and families utilize social media provides health care members opportunities to discuss medical information, inform health care decision making, and support patient and family needs.

A Monte Carlo Simulation Approach to Optimizing Capacity in a High-Volume Congenital Heart Pediatric Surgical Center.

Importance: Elective surgeries are primarily scheduled according to surgeon availability with less consideration of patients' postoperative cardiac intensive care unit (CICU) length of stay. Furthermore, the CICU census can exhibit a high rate of variation in which the CICU is operating at over-capacity, resulting in admission delays and cancellations; or under-capacity, resulting in underutilized labor and overhead expenditures. Objective: To identify strategies to reduce variation in CICU occupancy levels and avoid late patient surgery cancellation. Design: Monte Carlo simulation study of the daily and weekly CICU census at Boston Children's Hospital Heart Center. Data on all surgical admissions to and discharges from the CICU at Boston Children's Hospital between September 1, 2009 and November 2019 were included to obtain the distribution of length of stay for the simulation study. The available data allows us to model realistic length of stay samples that include short and extended lengths of stay. Main Outcomes: Annual number of patient surgical cancellations and change in average daily census. Results: We demonstrate that the models of strategic scheduling would result in up to 57% reduction in patient surgical cancellations, increase the historically low Monday census and decrease the historically higher late-mid-week (Wednesday and Thursday) censuses in our center. Conclusions and Relevance: Use of strategic scheduling may improve surgical capacity and reduce the number of annual cancellations. The reduction of peaks and valleys in the weekly census corresponds to a reduction of underutilization and overutilization of the system.

Myeloproliferative Disorder with Intraoral Lesions in an Olive Baboon (Papio anubis).

Spontaneous myeloid leukemia is rarely reported in non-human primates. We report a case of myeloproliferative disorder suggestive of acute myeloid leukemia with intraoral lesions in an olive baboon (Papio anubis). Clinical pathology, radiology, gross examination (pre-mortem and post-mortem), histopathology, and immunohistochemistry findings are provided.

Predicting Influenza A Tropism with End-to-End Learning of Deep Networks.

The type of host that a virus can infect, referred to as host specificity or tropism, influences infectivity and thus is important for disease diagnosis, epidemic response, and prevention. Advances in DNA sequencing technology have enabled rapid metagenomic analyses of viruses, but the prediction of virus phenotype from genome sequences is an active area of research. As such, automatic prediction of host tropism from analysis of genomic information is of considerable utility. Previous research has applied machine learning methods to accomplish this task, although deep learning (particularly deep convolutional neural network, CNN) techniques have not yet been applied. These techniques have the ability to learn how to recognize critical hierarchical structures within the genome in a data-driven manner. We designed deep CNN models to identify host tropism for human and avian influenza A viruses based on protein sequences and performed a detailed analysis of the results. Our findings show that deep CNN techniques work as well as existing approaches (with 99% mean accuracy on the binary prediction task) while performing end-to-end learning of the prediction model (without the need to specify handcrafted features). The findings also show that these models, combined with standard principal component analysis, can be used to quantify and visualize viral strain similarity.

Monitoring the health of the work environment with a daily assessment tool: the REAL - Relative Environment Assessment Lens - indicator.

BACKGROUND: Evidence shows that the health of the work environment impacts staff satisfaction, interdisciplinary communication, and patient outcomes. Utilising the American Association of Critical-Care Nurses' Healthy Work Environment standards, we developed a daily assessment tool. METHODS: The Relative Environment Assessment Lens (REAL) Indicator was developed using a consensus-based method to evaluate the health of the work environment and to identify opportunities for improvement from the front-line staff. A visual scale using images that resemble emoticons was linked with a written description of feelings about their work environment that day, with the highest number corresponding to the most positive experience. Face validity was established by seeking staff feedback and goals were set. RESULTS: Over 10 months, results from the REAL Indicator in the cardiac catheterisation laboratory indicated an overall good work environment. The goal of 80% of the respondents reporting their work environment to be "Great", "Good", or "Satisfactory" was met each month. During the same time frame, this goal was met four times in the cardiovascular operating room. On average, 72.7% of cardiovascular operating room respondents reported their work environment to be "Satisfactory" or better. CONCLUSION: The REAL Indicator has become a valuable tool in assessing the specific issues of the clinical area and identifying opportunities for improvement. Given the feasibility of and positive response to this tool in the cardiac catheterisation laboratory, it has been adopted in other patient-care areas where staff and leaders believe that they need to understand the health of the environment in a more specific and frequent time frame.

Efficient photocatalytic hydrogen production by platinum-loaded carbon-doped cadmium indate nanoparticles.

Undoped and carbon doped cadmium indate (CdIn(2)O(4)) powders were synthesized using a sol-gel pyrolysis method and evaluated for hydrogen generation activity under UV-visible irradiation without the use of a sacrificial reagent. Each catalyst powder was loaded with a platinum cocatalyst in order to increase electron-hole pair separation and promote surface reactions. Carbon-doped indium oxide and cadmium oxide were also prepared and analyzed for comparison. UV-vis diffuse reflectance spectra indicate the band gap for C-CdIn(2)O(4) to be 2.3 eV. C-doped In(2)O(4) showed a hydrogen generation rate approximately double that of the undoped material. When compared to platinized TiO(2) in methanol, which was used as a control material, C-CdIn(2)O(4) showed a 4-fold increase in hydrogen production. The quantum efficiency of the material was calculated at different wavelength intervals and found to be 8.7% at 420-440 nm. The material was capable of hydrogen generation using visible light only and with good efficiency even at 510 nm.

A bayesian approach to deformed pattern matching of iris images.

We describe a general probabilistic framework for matching patterns that experience in-plane nonlinear deformations, such as iris patterns. Given a pair of images, we derive a maximum a posteriori probability (MAP) estimate of the parameters of the relative deformation between them. Our estimation process accomplishes two things simultaneously: It normalizes for pattern warping and it returns a distortion-tolerant similarity metric which can be used for matching two nonlinearly deformed image patterns. The prior probability of the deformation parameters is specific to the pattern-type and, therefore, should result in more accurate matching than an arbitrary general distribution. We show that the proposed method is very well suited for handling iris biometrics, applying it to two databases of iris images which contain real instances of warped patterns. We demonstrate a significant improvement in matching accuracy using the proposed deformed Bayesian matching methodology. We also show that the additional computation required to estimate the deformation is relatively inexpensive, making it suitable for real-time applications.

Wavelet packet correlation methods in biometrics.

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.

Biometric verification with correlation filters.

Using biometrics for subject verification can significantly improve security over that of approaches based on passwords and personal identification numbers, both of which people tend to lose or forget. In biometric verification the system tries to match an input biometric (such as a fingerprint, face image, or iris image) to a stored biometric template. Thus correlation filter techniques are attractive candidates for the matching precision needed in biometric verification. In particular, advanced correlation filters, such as synthetic discriminant function filters, can offer very good matching performance in the presence of variability in these biometric images (e.g., facial expressions, illumination changes, etc.). We investigate the performance of advanced correlation filters for face, fingerprint, and iris biometric verification.