Enhanced Data Hiding Using Some Attribute of Color Image

Images are one of the most widely used multimedia in the correspondence between people, as some of the characteristics of these images can be used to hide important messages. Each image has different characteristics, and the method of concealment changes depending on the characteristics of the image used. In this research, an algorithm was proposed to increase the efficiency of the data embedding algorithm by relying on some of the characteristics of the colored digital image. First, the color image is dismantled to the basic color layers (red, green, blue). Then, the amount of variation in each layer is measured by using image processing techniques. After that, the high contrast layer is identified and used as a cover to include the message to be included, while the other two layers are used as a key to the encryption algorithm that is applied to the text before the embedding process to increase data security.The method of concealment depends on the first and second bit values in the selected layer as a cover for the embedding process. Three criteria were used to measure the efficiency of the proposed algorithm. © 2023 IEEE

Good risk assessment practice in hospitals

Risk assessment is essential to ensure safety in hospitals. However, hospitals have paid little attention to risk assessment. Several problems have already been identified in the literature about current risk assessment practice, such as inadequate risk assessment guidance and bias in risk scoring. This research aimed to improve current risk assessment practice in hospitals in the National Health Service (NHS) in England. To address this aim, the research investigated current risk assessment practice and designed a new risk assessment approach by the use of mixed methods. One hundred hospitals’ risk assessment documents were reviewed to examine the current recommended risk assessment practice. Seventeen interviews and sixty-one questionnaires were conducted, a risk management system from a single hospital was reviewed, and strategic risks from thirty-four hospitals were reviewed, in order to examine how risks are assessed in actual practice. Following that, the proposed approach was designed by conducting requirements analysis and then evaluated by interviews and questionnaires with ten healthcare staff. The findings of this research reveal that hospitals conduct risk assessments in different ways (i.e. with a focus on individual patient-based, operational and strategic risks). There are also many problems involved in current risk assessment practice regarding both the foundations and use of risk assessment. For example, organisation-wide risk assessments predominantly rely on risk matrices which might lead to wrong risk prioritisation and resource allocation; and risks tend to reflect existing or past problems rather than being proactive. All these reveal a need to improve current risk assessment practice. This research makes an important contribution to the current understanding of risk assessment practice in hospitals by providing extensive evidence on both recommended and actual practice, and proposes a new risk assessment framework. The framework guides healthcare staff on how to conduct risk assessment in a more comprehensive way by encouraging its potential users to consider good risk assessment practice.The Ministry of National Education, Republic of Turke

A framework to support risk assessment in hospitals

In healthcare, risk assessment is used alongside a number of reactive risk management approaches to ensure the quality and safety of the care delivered. However, problems have been identified regarding its current application in hospitals, despite the considerable efforts made. In this paper, we present a framework that aims to address these current challenges and to guide staff working in healthcare settings in undertaking an effective risk assessment in hospitals. We report on the design of this framework, where we used a V developmental model, in conjunction with mixed methods, including interviews, document analysis and group discussions. The framework consists of a risk assessment model that depicts the main risk assessment steps; risk assessment explanation cards that provide prompts to help apply each step; and a risk assessment form that helps to systematise the risk assessment and document the findings. We also report on the evaluation of the framework, which shows promising results. While the framework was recommended for use in practice, it was also suggested that it would be helpful as a training tool. With its use in risk assessment, we anticipate that risk assessments would lead to more effective decisions being made, and, thus, to more appropriate actions being taken to minimise risks. Consequently, the quality and safety of care delivered would be improved.National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of Englan

Evaluating inputs of failure modes and effects analysis in identifying patient safety risks.

PURPOSE: There is a growing awareness on the use of systems approaches to improve patient safety and quality. While earlier studies evaluated the validity of such approaches to identify and mitigate patient safety risks, so far only little attention has been given to their inputs, such as structured brainstorming and use of system mapping approaches (SMAs), to understand their impact in the risk identification process. To address this gap, the purpose of this paper is to evaluate the inputs of a well-known systems approach, failure modes and effects analysis (FMEA), in identifying patient safety risks in a real healthcare setting. DESIGN/METHODOLOGY/APPROACH: This study was conducted in a newly established adult attention deficit hyperactivity disorder service at Cambridge and Peterborough Foundation Trust in the UK. Three stakeholders of the chosen service together with the facilitators conducted an FMEA exercise along with a particular system diagram that was initially found as the most useful SMA by eight stakeholders of the service. FINDINGS: In this study, it was found that the formal structure of FMEA adds value to the risk identification process through comprehensive system coverage with the help of the system diagram. However, results also indicates that the structured brainstorming refrains FMEA participants from identifying and imagining new risks since they follow the process predefined in the given system diagram. ORIGINALITY/VALUE: While this study shows the potential contribution of FMEA inputs, it also suggests that healthcare organisations should not depend solely on FMEA results when identifying patient safety risks; and therefore prioritising their safety concerns

Positive effect of restrictions on antibiotic consumption

Conclusion: Hospital-acquired Acinetobacter infections, antibiotic consumption, and infection-related mortality were decreased significantly with the restriction of G2C. Positive behaviors that were obtained during the restricted period were continued with release of restriction

Effect of camera monitoring and feedback along with training on hospital infection rate in a neonatal intensive care unit

Background In terms of pediatric healthcare-associated infections (HAI), neonatal intensive care units (NICU) constitute the greatest risk. Contacting a health care personnel, either directly or indirectly, elevates NICU occurrence rate and risks other infants in the same unit. In this study, it is aimed to retrospectively analyze the effect of the training along with camera monitoring and feedback (CMAF) to control the infection following a small outbreak. Methods ESBL producing Klebsiella pneumoniae was detected on three infants in May 2014 at the isolation room of Sakarya University Hospital NICU. Precautions were taken to prevent further spread of the infection. The infected infants were isolated and the decolonization process was initiated. For this aspect, health care workers (HCWs) in NICU were trained for infection control measures. An infection control committee has monitored the HCWs. Before monitoring, an approval was obtained from the hospital management and HCWs were informed about the CMAF, who were then periodically updated. On a weekly basis, NICU workers were provided with the feedbacks. Epidemic period and post-epidemic control period (June-July-August 2014) were evaluated and p value < 0.05 was considered statistically significant. Results Healthcare-associated infection (HAI) density was 9.59% before the onset of the CMAF, whereas it was detected as 2.24% during the CMAF period (p < 0.05). Following the precautions, HAI and HAI density rates have reduced to 76.6% and 74.85%, respectively. Moreover, hand hygiene compliance of health care workers was found 49.0% before the outbreak, whereas this rate has elevated to 62.7% after CMAF. Conclusions Healthcare workers should be monitored in order to increase their compliance for infection control measures. Here, we emphasized that that CMAF of health workers may contribute reducing the HAI rate in the NICU

Exploring the impact of safety culture on incident reporting: lessons learned from machine learning analysis of NHS England staff survey and incident data

Safety culture is one of the key factors contributing to safety, even though limited evidence supports its impact on safety outcomes. This study uses supervised machine learning algorithms to explore the association between safety culture and incident reporting. The study used National Health Service (NHS) England annual staff survey data as a proxy of safety culture to predict eighteen incident reporting variables. The study did not achieve high accuracy rates in the prediction models. The highest association was found between safety culture and the number of incidents reported in class low, medium and high. LightGBM was the best-performed algorithm. SHAP plots were used to explain the model. Findings suggest that compassionate culture, violence and harassment and work pressure are critical in predicting the number of incidents reported. More specifically, the violence and harassment had a more significant impact on predicting the number of incidents reported in class high than in class medium and low. The involvement had more effect on predicting class low. The results demonstrated different behaviours in predicting different incident reporting classes. The findings facilitate lessons learned from staff surveys and incident reporting data in NHS England. Consequently, the findings can contribute to improving the safety culture in hospitals

A comparison of machine learning algorithms in predicting COVID-19 prognostics

ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care

What kinds of insights do Safety-I and Safety-II approaches provide? a critical reflection on the use of SHERPA and FRAM in healthcare

Over the past decade, the field of healthcare has seen a significant shift in its approach to patient safety. Traditionally, safety efforts focused on understanding past harm and preventing errors, primarily through the use of standardisation and the introduction of barriers and safeguards, such as standardised communication protocols (e.g., SBAR (Haig et al., 2006)), checklists (e.g., WHO surgical safety checklist (Haynes et al., 2009)) and technology with safety features (e.g., smart infusion pumps (Taxis and Franklin, 2011)). This type of thinking about patient safety in terms of past harm and errors is also referred to as Safety-I (Hollnagel, 2014), even though this terminology has been criticised as it does not reflect adequately the diversity in safety science thinking (Leveson, 2020). However, the evidence for whether interventions based on this (Safety-I) thinking lead to improvements in patient safety is mixed at best (Kellogg et al., 2017, Wears and Sutcliffe, 2019), and critics have argued that the additional “safety clutter” produced as a result of such interventions might be counterproductive (Rae et al., 2018, Halligan et al., 2023).This work was funded by the National Institute for Health Research (NIHR) [Programme Grant for Applied Research NIHR200868

Inborn errors of OAS-RNase L in SARS-CoV-2-related multisystem inflammatory syndrome in children

Funding Information: The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute, the Rockefeller University, the St. Giles Foundation, the National Institutes of Health (NIH) (R01AI088364 and R21AI160576), the National Center for Advancing Translational Sciences (NCATS), NIH Clinical and Translational Science Award (CTSA) program (UL1TR001866), the Yale Center for Mendelian Genomics and the GSP Coordinating Center funded by the National Human Genome Research Institute (NHGRI) (UM1HG006504 and U24HG008956), the Yale High-Performance Computing Center (S10OD018521), the Fisher Center for Alzheimer's Research Foundation, the Meyer Foundation, the JBP Foundation, the French National Research Agency (ANR) under the "Investments for the Future" program (ANR-10-IAHU-01), the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (ANR-10-LABX-62-IBEID), the French Foundation for Medical Research (FRM) (EQU201903007798), the ANR GenMISC (ANR-21-COVR-039), the ANRS-COV05, ANR GENVIR (ANR-20-CE93-003) and ANR AABIFNCOV (ANR-20-CO11-0001) projects, the ANR-RHU program (ANR-21-RHUS-08), the European Union's Horizon 2020 research and innovation program under grant agreement 824110 (EASI-genomics), the HORIZON-HLTH-2021-DISEASE-04 program under grant agreement 01057100 (UNDINE), the ANR-RHU Program ANR-21-RHUS-08 (COVIFERON), the Square Foundation, Grandir - Fonds de solidarité pour l'enfance, the Fondation du Souffle, the SCOR Corporate Foundation for Science, the French Ministry of Higher Education, Research, and Innovation (MESRI-COVID-19), Institut National de la Santé et de la Recherche Médicale (INSERM), and Paris Cité University. We acknowledge support from the National Institute of Allergy and Infectious Diseases (NIAID) of the NIH under award R01AI104887 to R.H.S. and S.R.W. The Laboratory of Human Evolutionary Genetics (Institut Pasteur) is supported by the Institut Pasteur, the Collège de France, the French Government's Investissement d'Avenir program, Laboratoires d'Excellence "Integrative Biology of Emerging Infectious Diseases" (ANR-10-LABX-62-IBEID) and "Milieu Intérieur" (ANR-10-LABX-69-01), the Fondation de France (no. 00106080), the FRM (Equipe FRM DEQ20180339214 team), and the ANR COVID-19-POPCELL (ANR-21-CO14-0003-01). A. Puj. is supported by ACCI20-759 CIBERER, EasiGenomics H2020 Marató TV3 COVID 2021-31-33, the HORIZON-HLTH-2021-ID: 101057100 (UNDINE), the Horizon 2020 program under grant no. 824110 (EasiGenomics grant no. COVID-19/PID12342), and the CERCA Program/Generalitat de Catalunya. The Canarian Health System sequencing hub was funded by the Instituto de Salud Carlos III (COV20-01333 and COV20-01334), the Spanish Ministry of Science and Innovation (RTC-2017-6471-1; AEI/FEDER, UE), Fundación MAPFRE Guanarteme (OA21/131), and Cabildo Insular de Tenerife (CGIEU0000219140 and "Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19"). The CoV-Contact Cohort was funded by the French Ministry of Health and the European Commission (RECOVER project). Our studies are also funded by the Ministry of Health of the Czech Republic Conceptual Development of Research Organization (FNBr, 65269705) and ANID COVID0999 funding in Chile. G. Novelli and A. Novelli are supported by Regione Lazio (Research Group Projects 2020) No. A0375-2020-36663, GecoBiomark. A.M.P., M.L.D., and J.P.-T. are supported by the Inmungen-CoV2 project of CSIC. This work was supported in part by the Intramural Research Program of the NIAID, NIH. The research work of A.M.P, M.L.D., and J.P.-T. was funded by the European Commission-NextGenerationEU (Regulation EU 2020/2094), through CSIC's Global Health Platform (PTI Salud Global). I.M. is a senior clinical investigator at FWO Vlaanderen supported by a VIB GC PID grant, by FWO grants G0B5120N (DADA2) and G0E8420N, and by the Jeffrey Modell Foundation. I.M. holds an ERC-StG MORE2ADA2 grant and is also supported by ERN-RITA. A.Y. is supported by fellowships from the European Academy of Dermatology and Venereology and the Swiss National Science Foundation and by an Early Career Award from the Thrasher Research Fund. Y.-H.C. is supported by an A*STAR International Fellowship (AIF). M.O. was supported by the David Rockefeller Graduate Program, the New York Hideyo Noguchi Memorial Society (HNMS), the Funai Foundation for Information Technology (FFIT), the Honjo International Scholarship Foundation (HISF), and the National Cancer Institute (NCI) F99 Award (F99CA274708). A.A.A. was supported by Ministerio de Ciencia Tecnología e Innovación MINCIENCIAS, Colombia (111584467551/CT 415-2020). D.L. is supported by a fellowship from the FRM for medical residents and fellows. E.H. received funding from the Bank of Montreal Chair of Pediatric Immunology, Foundation of CHU Sainte-Justine, CIHR grants PCC-466901 and MM1-181123, and a Canadian Pediatric Society IMPACT study. Q.P.-H. received funding from the European Union's Horizon 2020 research and innovation program (ATAC, 101003650), the Swedish Research Council, and the Knut and Alice Wallenberg Foundation. Work in the Laboratory of Virology and Infectious Disease was supported by NIH grants P01AI138398-S1, 2U19AI111825, R01AI091707-10S1, and R01AI161444; a George Mason University Fast Grant; the G. Harold and Leila Y. Mathers Charitable Foundation; the Meyer Foundation; and the Bawd Foundation. R.P.L. is on the board of directors of both Roche and the Roche subsidiary Genentech. J.L.P. was supported by a Francois Wallace Monahan Postdoctoral Fellowship at the Rockefeller University and by a European Molecular Biology Organization Long-Term Fellowship (ALTF 380-2018). Publisher Copyright: © 2023 American Association for the Advancement of Science. All rights reserved.Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe condition that follows benign COVID-19. We report autosomal recessive deficiencies of OAS1, OAS2, or RNASEL in five unrelated children with MIS-C. The cytosolic double-stranded RNA (dsRNA)-sensing OAS1 and OAS2 generate 2'-5'-linked oligoadenylates (2-5A) that activate the single-stranded RNA-degrading ribonuclease L (RNase L). Monocytic cell lines and primary myeloid cells with OAS1, OAS2, or RNase L deficiencies produce excessive amounts of inflammatory cytokines upon dsRNA or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stimulation. Exogenous 2-5A suppresses cytokine production in OAS1-deficient but not RNase L-deficient cells. Cytokine production in RNase L-deficient cells is impaired by MDA5 or RIG-I deficiency and abolished by mitochondrial antiviral-signaling protein (MAVS) deficiency. Recessive OAS-RNase L deficiencies in these patients unleash the production of SARS-CoV-2-triggered, MAVS-mediated inflammatory cytokines by mononuclear phagocytes, thereby underlying MIS-C.publishersversionpublishe