Making treatment guideline recommendations in chronic kidney disease and type 2 diabetes more accessible to primary care providers in the United States.

Clinical practice guidelines for the management of chronic kidney disease (CKD) associated with type 2 diabetes (T2D) are designed to assist healthcare professionals with clinical decision making by providing recommendations on the screening, detection, management, and treatment of these conditions. However, primary care practitioners (PCPs) may have clinical inertia when it comes to routinely enacting CKD and T2D guideline recommendations in their clinical practices. Guideline developers have published a range of resources with the aim of facilitating easier access to guideline recommendations to support efficient and consistent implementation into clinical practice of PCPs. Challenges remain in providing strategies to reduce inertia in the application of guideline recommendations in primary care. In this review, we explore reasons behind the low level of awareness and poor uptake of published evidence-based care approaches to the optimal management of patients with T2D and CKD. Finally, we present suggestions on strategies to improve the implementation of guideline-directed recommendations in primary care.

Clinical practice guidelines for managing chronic kidney disease (CKD) for people who also have type 2 diabetes (T2D) provide healthcare providers with recommendations on how to identify, diagnose, and treat CKD. Although treatments cannot cure CKD, they can help to reduce the risk of CKD getting worse. The recommendations are based on results of clinical trials that tested how safe and how well a medication works among many people with CKD and T2D. If these clinical trials show that the medicine is beneficial for people with CKD and T2D, then it may be included in guideline recommendations. Most people living with T2D and early-stage CKD are treated by their primary care practitioner (PCP). If PCPs are not fully aware of guideline recommendations, then their patients may lose the opportunity to receive medications that can benefit them. PCPs have said that barriers to implementing guideline recommendations in their clinical practices include too many guidelines and that the guidelines are difficult to understand and use in their offices. Guideline developers have thought of ways to make the guidelines easier to access and use. This includes putting the guidelines onto mobile apps, providing online resources, making versions more relevant to PCPs, and combining multiple guidelines. These approaches are helpful, but more work is needed. This review article talks about the reasons why PCPs are not always aware of the most up-to-date guideline recommendations for CKD and T2D, how guideline developers have found different ways of sharing the guideline recommendations, and what more can be done.

Effect of simulation-based zoom learning on clinical decision-making among undergraduate nursing students and experiences of students and instructors: A mixed methods study.

Aim: To determine the effect of simulation-based Zoom learning (SBZL) on perceived capabilities and clinical decision-making skills among undergraduate nursing students and to explore experiences of the instructors and students participating in SBZL. Background: Nursing is a practice profession and students acquire clinical decision-making skills in clinical settings. However, the COVID-19 pandemic has disrupted conventional clinical learning activities. In this study, the outcomes of implementing SBZL in an undergraduate programme to support students' clinical learning were examined. Design: A mixed methods design was employed. Methods: This study recruited 195 final-year students to participate in the SBZL programme, which was developed based on the NLN Jeffries Simulation Theory to guide its design, implementation and evaluation. Case scenarios were developed and simulated through Zoom. Students' perceived capabilities, perceptions of the learning environment and clinical decision-making skills were assessed before and after SBZL. A historical control group of 226 previous final year students who had received a clinical practicum was included for comparison. Semi-structured interviews were conducted with 11 instructors and 19 students to explore their experiences of participating in SBZL. Results: A total of 102 students completed the post-SBZL questionnaire. An increase in perceived creative thinking (mean difference = 0.24, p < 0.001) was observed post-SBZL. After SBZL, the perceptions of the learning environment were significantly improved. However, the SBZL group demonstrated lower perceived problem-solving capability than the control group (mean difference = 0.14, p = 0.007). Clinical decision-making was significantly improved in the SBZL group than in the control group (p < 0.001). Both the instructors and students reported positive experiences with SBZL, and highlighted challenges and factors for improving its implementation. Conclusions: SBZL showed improvement in perceived creative thinking, perceptions of the learning environment and clinical decision-making. This innovative teaching and learning method can be valuable for nursing education in various regions to prepare students for real-life roles. Tweetable abstract: Simulation-based Zoom learning is better than traditional teaching in improving clinical decision-making skills among undergraduate nursing students.

Strategies for optimising early detection and obstetric first response management of postpartum haemorrhage at caesarean birth: a modified Delphi-based international expert consensus.

OBJECTIVE: There are no globally agreed on strategies on early detection and first response management of postpartum haemorrhage (PPH) during and after caesarean birth. Our study aimed to develop an international expert's consensus on evidence-based approaches for early detection and obstetric first response management of PPH intraoperatively and postoperatively in caesarean birth. DESIGN: Systematic review and three-stage modified Delphi expert consensus. SETTING: International. POPULATION: Panel of 22 global experts in PPH with diverse backgrounds, and gender, professional and geographic balance. OUTCOME MEASURES: Agreement or disagreement on strategies for early detection and first response management of PPH at caesarean birth. RESULTS: Experts agreed that the same PPH definition should apply to both vaginal and caesarean birth. For the intraoperative phase, the experts agreed that early detection should be accomplished via quantitative blood loss measurement, complemented by monitoring the woman's haemodynamic status; and that first response should be triggered once the woman loses at least 500 mL of blood with continued bleeding or when she exhibits clinical signs of haemodynamic instability, whichever occurs first. For the first response, experts agreed on immediate administration of uterotonics and tranexamic acid, examination to determine aetiology and rapid initiation of cause-specific responses. In the postoperative phase, the experts agreed that caesarean birth-related PPH should be detected primarily via frequently monitoring the woman's haemodynamic status and clinical signs and symptoms of internal bleeding, supplemented by cumulative blood loss assessment performed quantitatively or by visual estimation. Postoperative first response was determined to require an individualised approach. CONCLUSION: These agreed on proposed approaches could help improve the detection of PPH in the intraoperative and postoperative phases of caesarean birth and the first response management of intraoperative PPH. Determining how best to implement these strategies is a critical next step.

Development and Validation of a Decision Tree Analysis Model for Predicting Home Discharge in a Convalescent Ward: A Single Institution Study.

OBJECTIVES: Accurately predicting the likelihood of inpatients' home discharge in a convalescent ward is crucial for assisting patients and families in decision-making. While logistic regression analysis has been commonly used, its complexity limits practicality in clinical settings. We focused on decision tree analysis, which is visually straightforward. This study aimed to develop and validate the accuracy of a prediction model for home discharge for inpatients in a convalescent ward using a decision tree analysis. METHODS: The cohort consisted of 651 patients admitted to our convalescent ward from 2018 to 2020. We collected data from medical records, including disease classification, sex, age, duration of acute hospitalization, discharge destination (home or nonhome), and Functional Independence Measure (FIM) subitems at admission. We divided the cohort data into training and validation sets and developed a prediction model using decision tree analysis with discharge destination as the target and other variables as predictors. The model's accuracy was validated using the validation data set. RESULTS: The decision tree model identified FIM grooming as the first single discriminator of home discharge, diverging at four points and identifying subsequent branching for the duration of acute hospitalization. The model's accuracy was 86.7%, with a sensitivity of 0.96, specificity of 0.52, positive predictive accuracy of 0.88, and negative predictive accuracy of 0.80. The area under the receiver operating characteristic curve was 0.75. CONCLUSION: The predictive model demonstrated more than moderate predictive accuracy, suggesting its utility in clinical practice. Grooming emerged as a variable with the highest explanatory power for determining home discharge.

An algorithm to identify patients aged 0-3 with rare genetic disorders.

BACKGROUND: With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. RESULTS: Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. CONCLUSIONS: The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction.

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection-play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.

'High hopes for treatment': Australian stakeholder perspectives of the clinical translation of advanced neurotherapeutics for rare neurological diseases.

INTRODUCTION: Advanced therapies offer unprecedented opportunities for treating rare neurological disorders (RNDs) in children. However, health literacy, perceptions and understanding of novel therapies need elucidation across the RND community. This study explored healthcare professionals' and carers' perspectives of advanced therapies in childhood-onset RNDs. METHODS: In this mixed-methodology cross-sectional study, 20 healthcare professionals (clinicians, genetic counsellors and scientists) and 20 carers completed qualitative semistructured interviews and custom-designed surveys. Carers undertook validated psychosocial questionnaires. Thematic and quantitative data analysis followed. RESULTS: Participants described high positive interest in advanced therapies, but low knowledge of, and access to, reliable information. The substantial 'therapeutic gap' and 'therapeutic odyssey' common to RNDs were recognised in five key themes: (i) unmet need and urgency for access; (ii) seeking information; (iii) access, equity and sustainability; (iv) a multidisciplinary and integrated approach to care and support and (v) difficult decision-making. Participants were motivated to intensify RND clinical trial activity and access to advanced therapies; however, concerns around informed consent, first-in-human trials and clinical trial procedures were evident. There was high-risk tolerance despite substantial uncertainties and knowledge gaps. RNDs with high mortality, increased functional burdens and no alternative therapies were consistently prioritised for the development of advanced therapies. However, little consensus existed on prioritisation to treatment access. CONCLUSIONS: This study highlights the need to increase clinician and health system readiness for the clinical translation of advanced therapeutics for RNDs. Co-development and use of educational and psychosocial resources to support clinical decision-making, set therapeutic expectations and promotion of equitable, effective and safe delivery of advanced therapies are essential. PATIENT OR PUBLIC CONTRIBUTION: Participant insights into the psychosocial burden and information need to enhance the delivery of care in this formative study are informing ongoing partnerships with families, including co-production and dissemination of psychoeducational resources featuring their voices hosted on the Sydney Children's Hospitals Network website SCHN Brain-Aid Resources.

ChatGPT as a Tool for Medical Education and Clinical Decision-Making on the Wards: Case Study.

BACKGROUND: Large language models (LLMs) are computational artificial intelligence systems with advanced natural language processing capabilities that have recently been popularized among health care students and educators due to their ability to provide real-time access to a vast amount of medical knowledge. The adoption of LLM technology into medical education and training has varied, and little empirical evidence exists to support its use in clinical teaching environments. OBJECTIVE: The aim of the study is to identify and qualitatively evaluate potential use cases and limitations of LLM technology for real-time ward-based educational contexts. METHODS: A brief, single-site exploratory evaluation of the publicly available ChatGPT-3.5 (OpenAI) was conducted by implementing the tool into the daily attending rounds of a general internal medicine inpatient service at a large urban academic medical center. ChatGPT was integrated into rounds via both structured and organic use, using the web-based "chatbot" style interface to interact with the LLM through conversational free-text and discrete queries. A qualitative approach using phenomenological inquiry was used to identify key insights related to the use of ChatGPT through analysis of ChatGPT conversation logs and associated shorthand notes from the clinical sessions. RESULTS: Identified use cases for ChatGPT integration included addressing medical knowledge gaps through discrete medical knowledge inquiries, building differential diagnoses and engaging dual-process thinking, challenging medical axioms, using cognitive aids to support acute care decision-making, and improving complex care management by facilitating conversations with subspecialties. Potential additional uses included engaging in difficult conversations with patients, exploring ethical challenges and general medical ethics teaching, personal continuing medical education resources, developing ward-based teaching tools, supporting and automating clinical documentation, and supporting productivity and task management. LLM biases, misinformation, ethics, and health equity were identified as areas of concern and potential limitations to clinical and training use. A code of conduct on ethical and appropriate use was also developed to guide team usage on the wards. CONCLUSIONS: Overall, ChatGPT offers a novel tool to enhance ward-based learning through rapid information querying, second-order content exploration, and engaged team discussion regarding generated responses. More research is needed to fully understand contexts for educational use, particularly regarding the risks and limitations of the tool in clinical settings and its impacts on trainee development.

Clinical Decision Making in Inflammatory Bowel Disease Mimics: Practice Management from Inflammatory Bowel Disease LIVE.

Background: Since 2009, inflammatory bowel disease (IBD) specialists have utilized "IBD LIVE," a weekly live video conference with a global audience, to discuss the multidisciplinary management of their most challenging cases. While most cases presented were confirmed IBD, a substantial number were diseases that mimic IBD. We have categorized all IBD LIVE cases and identified "IBD-mimics" with consequent clinical management implications. Methods: Cases have been recorded/archived since May 2018; we reviewed all 371 cases from May 2018-February 2023. IBD-mimics were analyzed/categorized according to their diagnostic and therapeutic workup. Results: Confirmed IBD cases made up 82.5% (306/371; 193 Crohn's disease, 107 ulcerative colitis, and 6 IBD-unclassified). Sixty-five (17.5%) cases were found to be mimics, most commonly medication-induced (n = 8) or vasculitis (n = 7). The evaluations that ultimately resulted in correct diagnosis included additional endoscopic biopsies (n = 13, 21%), surgical exploration/pathology (n = 10, 16.5%), biopsies from outside the GI tract (n = 10, 16.5%), genetic/laboratory testing (n = 8, 13%), extensive review of patient history (n = 8, 13%), imaging (n = 5, 8%), balloon enteroscopy (n = 5, 8%), and capsule endoscopy (n = 2, 3%). Twenty-five patients (25/65, 38%) were treated with biologics for presumed IBD, 5 of whom subsequently experienced adverse events requiring discontinuation of the biologic. Many patients were prescribed steroids, azathioprine, mercaptopurine, or methotrexate, and 3 were trialed on tofacitinib. Conclusions: The diverse presentation of IBD and IBD-mimics necessitates periodic consideration of the differential diagnosis, and reassessment of treatment in presumed IBD patients without appropriate clinical response. The substantial differences and often conflicting treatment approaches to IBD versus IBD-mimics directly impact the quality and cost of patient care.

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis.

This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

The effect of nomophobic behaviors among nurses on their clinical decision-making perceptions.

AIM: This study aimed to explore the impact of nomophobic behaviors among hospital nurses on their clinical decision-making perceptions. This understanding can offer insights to enhance the work environment, improve the clinical decision-making ability of nurses and guide medical institutions in the management of related equipment and policy development. BACKGROUND: The term "nomophobia" refers to the anxiety and fear individuals experience when they cannot use their smartphones or when smartphones are not accessible. Nursing clinical decision-making is a complex process, including a meticulous assessment of the patient's pathological condition and medical history, alongside the application of nursing knowledge and experiential learning rooted in critical thinking. The concept of clinical decision-making perceptions is defined as a deliberate cognitive understanding of one's decision-making processes, which significantly influences the clinical decision-making capabilities of healthcare professionals, thereby impacting the quality of patient care. The factors influencing these clinical decision-making perceptions have been the subject of extensive research. However, there is no Chinese research on the impact of nurses ' nomophobic behaviors on their clinical decision-making perception. DESIGN: A cross-sectional descriptive survey using online-based delivery modes was used. METHODS: A descriptive cross-sectional survey design was employed. Using convenience sampling, we surveyed the nurses from a tertiary hospital in Nanjing in May 2023. Data were gathered using a sociodemographic data form, the Nomophobia Questionnaire and the Clinical Decision-Making in Nursing Scale. Techniques including the independent sample t-test, one-way ANOVA, Pearson correlation analysis and linear regression analysis were employed to probe the degree of nomophobia and its effects on their perception of clinical decision-making. Out of 284 questionnaires gathered, 272 were deemed valid, resulting in a 95.8% effective response rate. RESULTS: The data revealed that participants exhibited a medium level of nomophobia (54.01 ± 24.09) and clinical decision-making perceptions (144.94 ± 20.08). A robust negative correlation was discerned between nomophobia and clinical decision-making perceptions (r: -0.365, P<0.001). This study highlighted that as the degree of nomophobia intensified, nurses' clinical decision-making perceptions decreased with the increase in nomophobia. CONCLUSION: Nomophobic behaviors can hamper nurses' perception of clinical decision-making, potentially leading to inaccuracies or errors. Nurses must use mobile phones judiciously, practice self-regulation and mitigate the disruptive effects of nomophobia on their decision-making. In addition, medical institutions should foster relevant education or craft policies to regularize mobile phone use, augmenting nurses' efficiency and decision-making prowess, enhancing patient care quality, diminishing medical errors and ensuring patient health and safety.

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

How is bioelectrical impedance used in neonatal intensive care? A scoping review.

Poor growth and nutrition management in the neonatal period can have a negative impact upon both the short- and long-term outcomes for the infant. Improvements in bioelectrical impedance technology and accompanying licencing agreements now make this enhanced device available for use in infants as small as 23 weeks gestational age. An exploration of this technology and its use is now timely. The aim of the scoping review was to answer the following question: in preterm and sick term infants in the neonatal intensive care unit, how is bioelectrical impedance being utilized, in what situations, and when? The scoping review was conducted using Arksey and O'Malley's (Int J Soc Res Methodol 8(1):19-32, 2005) framework. Forty-nine papers were initially identified and 16 were included in the scoping review. Three studies were experimental designs, and 13 were observational studies. The review found that BIA was used in neonatal intensive care in three main ways, for, (1) fluid status evaluation, (2) as a measure of adequate nutrition and growth, (3) to validate the technology as an outcome measure in neonates. CONCLUSION: There is a paucity of recent robust research papers which investigate the use of bioelectrical impedance in preterm neonates. Available evidence spans a range of 30 years, with technological advancement reducing the application of older studies to the modern neonatal setting. Although this technology may be helpful for decision-making around fluid management and nutrition, in preterm infants, robust evidence is needed to demonstrate the clinical benefit of bioelectrical impedance beyond that of usual care. WHAT IS KNOWN: • Clinical decisions regarding neonatal nutrition and fluid management are currently based upon the interpretation of vital signs, fluid balance, weight trend, biochemical markers, and physical examination. • Bioelectrical Impedance Analysis (BIA) is a non-invasive method of assessing body composition which is now available to be used in infants as small as 23 weeks gestation. WHAT IS NEW: • Bioelectrical Impedance has been used in three main ways in the NICU, for fluid status evaluation, for measuring nutrition and growth and to validate BIA as an outcome. • There is a lack of recent robust research data to support the use of the device within clinical decision making in neonatal intensive care.

An Insight into Knowledge, Perspective, and Practices of Indian Optometrists towards Childhood Myopia.

The current understanding of clinical approaches and barriers in managing childhood myopia among Indian optometrists is limited. This research underscores the necessity and relevance of evidence-based practice guidelines by exploring their knowledge, attitude, and practice towards childhood myopia. A self-administered internet-based 26-item survey was circulated online among practicing optometrists in India. The questions assessed the demographics, knowledge, self-reported clinical practice behavior, barriers, source of information guiding their management, and extent of adult caregiver engagement for childhood myopia. Of 393 responses, a significant proportion of respondents (32.6-92.4%) were unaware of the ocular complications associated with high myopia, with less than half (46.5%) routinely performing ocular biometry in clinical practice. Despite the growing awareness of emerging myopia management options, the uptake remains generally poor, with single-vision distance full-correction spectacles (70.3%) being the most common mode of vision correction. Barriers to adopting optimal myopia care are medicolegal concerns, absence of clinical practice guidelines, and inadequate consultation time. Own clinical experience and original research articles were the primary sources of information supporting clinical practice. Most (>70%) respondents considered involving the adult caregiver in their child's clinical decision-making process. While practitioners' awareness and activity of newer myopia management strategies are improving, there is plenty of scope for its enhancement. The importance of evidence-based practice guidelines and continuing education on myopia control might help practitioners enhance their clinical decision-making skills.

From "exceptional ethics" to public health ethics. Patients and proxies facing COVID-19. Ethics and public health during COVID-19 / D'une « éthique d'exception ¼ à une éthique de la santé publique. Patients et proches face à la COVID-19. Éthique et santé publique en temps de COVID-19.

The study aimed to elicit the perception and ethical considerations of patients and proxies with respect both to the individual medical decisions and public health decisions made during the COVID-19 crisis. It used a qualitative, multi-center study based on semi-directive interviews, conducted by an interdisciplinary team. The analysis was conducted using a thematic analysis approach and an ethical framework. Three themes emerged from the analysis: 1) patients, unlike proxies, did not complain about their diminished role in the decision-making process. Both highlighted the importance of "basic care" as opposed to a technical approach to treatment; 2) despite the transparency of the information process, a deep "crisis of trust" has developed between citizens and public authorities; 3) although both patients and proxies accepted the limitations of personal liberties imposed in the name of public health, they argued that these limitations should respect certain boundaries, both temporal and spacial. Above all, they should not affect basic affective human relationships, even if such boundaries are a factor in an increased risk of infection. The study showed that there is a need to reconsider the definition and the main principles of public health ethics, namely transparency and proportionality.

L'étude vise à analyser la perception que les patients et les proches de patients pris en charge pendant la crise de la COVID-19, ont pu avoir de leur prise en charge, et leurs réflexions éthiques sur la place et la définition de la santé publique. L'étude a utilisé une méthode qualitative et multicentrique. Les entretiens semi-directifs ont été conduits par une équipe pluridisciplinaire et analysés avec une approche thématique et une grille de lecture éthique à partir des principes de l'éthique biomédicale. Trois thèmes ont émergé : 1) Les patients ont exprimé peu de revendications de participer aux décisions médicales les concernant, contrairement aux proches qui se sont sentis exclus de leur rôle. Tous ont mis l'accent sur l'importance des soins de base par rapport aux soins techniques ; 2) La gestion de la crise n'est pas jugée sévèrement, mais une crise de confiance importante a été mise en évidence, malgré la « transparence ¼ affichée de l'information ; 3) les contraintes collectives ont été largement acceptées au nom de la solidarité, mais on a jugé qu'elles doivent avoir des limites (temporelle et spatiales). Surtout, elles ne doivent pas empêcher des relations humaines simples et essentielles. L'étude met en évidence qu'il est nécessaire de développer une réflexion nouvelle autour de l'éthique de la santé publique : il convient de questionner les principes de « transparence ¼ et de « proportionnalité ¼ et d'adopter une définition de « santé publique ¼ plus large que la minimisation du risque infectieux.

Clinical decision making for VA ECMO weaning in patients with cardiogenic shock A formative qualitative study.

Weaning and liberation from VA ECMO in cardiogenic shock patients comprises a complex process requiring a continuous trade off between multiple clinical parameters. In the absence of dedicated international guidelines, we hypothesized a great heterogeneity in weaning practices among ECMO centers due to a variety in local preferences, logistics, case load and individual professional experience. This qualitative study focused on the appraisal of clinicians' preferences in decision processes towards liberation from VA ECMO after cardiogenic shock while using focus group interviews in 4 large hospitals. The goal was to provide novel and unique insights in daily clinical weaning practices. As expected, we found we a great heterogeneity of weaning strategies among centers and professionals, although participants appeared to find common ground in a clinically straightforward approach to assess the feasibility of ECMO liberation at the bedside. This was shown in a preference for robust, easily accessible parameters such as arterial pulse pressure, stable cardiac index ≥2.1 L/min, VTI LVOT and 'eyeballing' LVEF.

The Relationship Between Psychological Empowerment and Clinical Decision-Making Among Staff Nurses in Governmental Hospital in Al-Baha, Saudi Arabia.

Background The Saudi Arabian government has published its 2030 vision for improving health care to meet worldwide standards for the nursing profession. To fulfill this vision, building large-scale healthcare facilities is necessary. Among the most common occupations, nursing is vital to health care systems. Although working in health care institutions is challenging, demanding, and comprehensive, they are created to save lives and enhance patient satisfaction. Therefore, health care organizations must seek to develop psychologically empowered and decision-making nurses who can help meet clients' demands and enhance patient care, safety, quality, and outcomes. This study aims to determine the association between psychological empowerment (PE) and clinical decision-making (CDM) among staff nurses. Methods This study employed a quantitative cross-sectional correlation design. Three Saudi Ministry of Health-affiliated hospitals in the Al-Baha region were included. The sample size was calculated using the Raosoft online sample size calculator, with a total of 318 participants. The study sample included nurses working in inpatient, outpatient, and critical care departments. Convenience sampling techniques with inclusion and exclusion criteria were employed. An online survey with three sections was used for data collection: sociodemographic characteristics, the psychological empowerment instrument, and the nursing decision-making instrument. Data collection began at the beginning of February 2023 and was completed by the beginning of April 2023. Results The participants were 318 nurses working in critical areas, inpatient, and outpatient departments at three governmental hospitals in the Al-Baha region. Overall, 285 participants (89.6%) had a high level of PE, and the majority, 263 participants (82.7%), exhibited flexible-oriented decision-making. Approximately three-quarters of the sample, 281 participants (88.4%), were female, and more than half of the staff nurses, 187 participants (58.8%), were married. The majority of participants, 250 (78.6%), had a bachelor's degree. Regarding professional experience, most staff nurses, 134 participants (42.1%), had between one and five years of experience, and the majority worked in inpatient units, 160 participants (50.3%), while 104 (32.7%) worked in critical care. Conclusion The current study found a significant association between nurses' PE and CDM. Nurses with the highest PE were the most flexible in their CDM. Moreover, the findings of this study offer some points that nurse managers and leaders can use to generate empowerment and make their staff better decision-makers. One recommendation is to develop training and coaching programs to enhance PE among staff nurses, thereby raising their work meaningfulness, which would reflect in better CDM. Additionally, this study recommends that future research be conducted to examine how PE affects CDM.

Development of an enhanced scoring system to predict ICU readmission or in-hospital death within 24 hours using routine patient data from two NHS Foundation Trusts.

RATIONALE: Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration. OBJECTIVES: We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU. DESIGN: A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a 'static' score of the patient's condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient's risk of deterioration throughout their hospital stay. SETTING: Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model. PARTICIPANTS: A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort. RESULTS: Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653). CONCLUSIONS: We showed that a scoring system incorporating data from a patient's stay in the ICU has better performance than commonly used EWS systems based on vital signs alone. TRIAL REGISTRATION NUMBER: ISRCTN32008295.