Search

Summary: There is more and more online information available at our fingertips, but how do we efficiently identify and extract relevant information for good clinical decision making? The aim of this review article is to provide equine professionals with the building blocks to be able to develop skills for expertly navigating the scientific literature in a timely fashion. This includes what key questions you need to ask before starting (why am I searching and how much time do I have), where the best places are to search and how to find freely available legal copies of papers (articles, conference proceedings, book chapters and dissertations). Additionally, how to best conduct an information search and what you get from searches of varying comprehensiveness. Finally, this article will cover where to find databases of research syntheses where others have already carried out the hard work for you and some advanced techniques which are likely to save you substantial amounts of time. This article draws together expertise from a panel of veterinary clinicians, nurses and technicians, information specialists and librarians and clinical researchers. [ABSTRACT FROM AUTHOR]

The use of Digital Twins (DTs) or the digital replicas of physical entities has provided benefits to several industry sectors, most notably manufacturing. To date, the application of DTs in the healthcare sector has been minimal, however. But, as pressure increases for more precise and personalized treatments, it behooves us to investigate the potential for DTs in the healthcare context. As a proof-of-concept demonstration prior to working with real patients, we attempt in this paper, to explore the potential for creating and using DTs. We do this in a synthetic environment at this stage, making use of data that is all computer-generated. DTs of synthetic present patients are created making use of data of synthetic past patients. In the real world, the clinical objective for creating such DTs of real patients would be to enable enhanced real-time clinical decision support to enable more precise and personalized care. The objective of the numerical experiment reported in this paper, is to envisage the possibilities and challenges of such an approach. We attempt to better understand the strengths and weaknesses of applying DTs in the healthcare context to support more precise and personalized treatments. [ABSTRACT FROM AUTHOR]

Background: Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods: This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results: There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions: Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice. [ABSTRACT FROM AUTHOR]

Introduction: This reviews aim was to explore the evidence base for the use of antipsychotic medications in the management of acute symptoms in patients with emotionally unstable personality disorder. Methods: Relevant databases and grey literature were searched for relevant papers. Inclusion/exclusion criteria were applied which resulted in the retrieval of eight papers pertinent to the literature review. The papers were critically appraised, and a thematic analysis was undertaken to collate relevant data. Findings: Five themes emerged from this literature review: different outcome measures, symptom domains (anger, impulsivity, mood instability, pseudopsychotic symptoms, self-harm and suicidal behaviours, adverse effects, the quality of the evidence and bias). Conclusions: The evidence base is not robust enough to allow conclusions to be drawn. Any positive results must be weighed up against the potential for adverse events. This review lays the foundations for further research, which is needed to explore antipsychotic treatment. [ABSTRACT FROM AUTHOR]

Background: Clinical reasoning (CR) is a holistic and recursive cognitive process. It allows nursing students to accurately perceive patients' situations and choose the best course of action among the available alternatives. This study aimed to identify the randomised controlled trials studies in the literature that concern clinical reasoning in the context of nursing students. Methods: A comprehensive search of PubMed, Scopus, Embase, and the Cochrane Controlled Register of Trials (CENTRAL) was performed to identify relevant studies published up to October 2023. The following inclusion criteria were examined: (a) clinical reasoning, clinical judgment, and critical thinking in nursing students as a primary study aim; (b) articles published for the last eleven years; (c) research conducted between January 2012 and September 2023; (d) articles published only in English and Spanish; and (e) Randomised Clinical Trials. The Critical Appraisal Skills Programme tool was utilised to appraise all included studies. Results: Fifteen papers were analysed. Based on the teaching strategies used in the articles, two groups have been identified: simulation methods and learning programs. The studies focus on comparing different teaching methodologies. Conclusions: This systematic review has detected different approaches to help nursing students improve their reasoning and decision-making skills. The use of mobile apps, digital simulations, and learning games has a positive impact on the clinical reasoning abilities of nursing students and their motivation. Incorporating new technologies into problem-solving-based learning and decision-making can also enhance nursing students' reasoning skills. Nursing schools should evaluate their current methods and consider integrating or modifying new technologies and methodologies that can help enhance students' learning and improve their clinical reasoning and cognitive skills. [ABSTRACT FROM AUTHOR]

In managing patients with chronic diseases, such as open angle glaucoma (OAG), the case treated in this paper, medical tests capture the disease phase (e.g. regression, stability, progression, etc.) the patient is currently in. When medical tests have low residual variability (e.g. empirical difference between the patient's true and recorded value is small) they can effectively, without the use of sophisticated methods, identify the patient's current disease phase; however, when medical tests have moderate to high residual variability this may not be the case. This paper presents a framework for handling the latter case. The framework presented integrates the outputs of interacting multiple model Kalman filtering with supervised learning classification. The purpose of this integration is to estimate the true values of patients' disease metrics by allowing for rapid and non-rapid phases; and dynamically adapting to changes in these values over time. We apply our framework to classifying whether a patient with OAG will experience rapid progression over the next two or three years from the time of classification. The performance (AUC) of our model increased by approximately 7% (increased from 0.752 to 0.819) when the Kalman filtering results were incorporated as additional features in the supervised learning model. These results suggest the combination of filters and statistical learning methods in clinical health has significant benefits. Although this paper applies our methodology to OAG, the methodology developed is applicable to other chronic conditions. [ABSTRACT FROM AUTHOR]

Despite exercise being a fundamental component of musculoskeletal rehabilitation, there is a lack concise, evidence-based recommendations to inform the clinician's development of rehabilitative exercise programs for patients in a one-on-one rehabilitation setting. Current evidence is widely dispersed throughout the literature, with each paper addressing one element of the exercise prescription process in isolation. The aim of this review is to draw together current and available literature to summarise the elements of the exercise prescription process that a clinician should consider when prescribing exercises programs for musculoskeletal rehabilitation in a one-on-one setting. Literature was first obtained by database searches, then hand searches were performed based on the reference lists from papers from the original search. Literature was then summarised and synthesised to create a collation of the elements of the exercise prescription process a clinician should consider for musculoskeletal rehabilitation in a one-on-one setting. The reviewed literature was categorised into two main themes: the clinical considerations and decision making underpinning the exercise prescription and the physical implementation of the exercise prescription to the patient. Each theme contained key elements and sub-elements that may systematically guide a clinician through the exercise prescription process. Whilst much evidence on exercise prescription in musculoskeletal rehabilitation is present in the literature, there is a lack of concise collated evidence that explores the elements of the exercise prescription process that could inform a clinicians clinical practice. The summary of these elements provides clinicians with a succinct summary that could aid in improving their exercise prescription process and potentially improve patient outcomes. Further research into the efficacy and importance of each element in clinical practice is needed. [ABSTRACT FROM AUTHOR]

Patient value is an important factor in clinical decision making, but conventionally, it is not incorporated in the decision processes. Clinical decision making has some clinical guidelines as a reference. There are very few value-based clinical guidelines, but knowledge about how values affect decision making is mentioned in some scattered studies in the literature. We use a literature review method to extract evidence and integrate it as part of the decision-making model. In this paper, a value-based therapy recommendation comprehensive model is proposed. A literature analysis is conducted to collect value-based evidence. The patients' values are defined and classified with fine granularity. Categorized values and candidate therapies are used in combination as filtering keywords to build this literature database. The literature analysis method generates a literature database used as a source of arguments for influencing decision making based on values. Then, a formalism model is put forward to integrate the value-based evidence with clinical evidence, and the literature databases and clinical guidelines are collected and analyzed to populate the evidence repository. During the decision-making processes, the evidence repository is utilized to match patients' clinical information and values. Decision-makers can dynamically adjust the relative importance of the two pieces of evidence to obtain a treatment plan that is more suitable for the patient. A prototype system was implemented using a case study for breast cancer and validated for feasibility and effectiveness through controlled experiments. [ABSTRACT FROM AUTHOR]

Clinical simulation is a useful method for evaluating AI-enabled clinical decision support (CDS). Simulation studies permit patient- and risk-free evaluation and far greater experimental control than is possible with clinical studies. The effect of CDS assisted and unassisted patient scenarios on meaningful downstream decisions and actions within the information value chain can be evaluated as outcome measures. This paper discusses the use of clinical simulation in CDS evaluation and presents a case study to demonstrate feasibility of its application. [ABSTRACT FROM AUTHOR]

Abstract Background Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice.

This article draws on 2 years' worth of ethnographic observation of team meetings to explore decision‐making in an NHS clinical genomics service. The focus of discussions was on ambiguous genomic results known as VUS or Variants of Uncertain Significance, which may be pathogenic but which also may turn out to be benign. In examining decision‐making around such results, we note how, in contrast to much policy and promotional material in this area, clinicians in these meetings (clinical geneticists and genetic counsellors) place great emphasis on parental phenotypes and whether the parents of a patient share the symptoms and signs of the suspected condition. This information is then combined with the result of genomic tests to decide whether the variant a patient has is responsible for their condition. This article explores the way in which clinicians attempt to flexibly enrol parents into genomic explanations through informal diagnosis of their possible phenotypes and the way in which actually meeting parents allows some clinicians to trump explanations based on documentary or photographic data. The paper sheds light on the way that earlier scholarly understandings of such decisions (around, say dysmorphology) remain relevant and explores claims that laboratory tests overrule clinical decision‐making. [ABSTRACT FROM AUTHOR]

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for futurepandemic events. [ABSTRACT FROM AUTHOR]

Aims: To identify how the nursing process (assessment, planning, intervention and outcome evaluation) has been incorporated into digital health systems (electronic medical records, electronic care plans and clinical decision support systems) to gain an understanding of known benefits and challenges posed to nurses' decision‐making processes. Background: Nursing terminologies, including the International Classification for Nursing Practice (ICNP), and Nursing Minimum Data Set's (NMDS), have been developed to improve standardised language integration of components of nursing care into digital systems. However, there is limited evidence regarding whether the complete nursing process is effectively being incorporated into digital health systems. Methods: An integrative systematic review following PRISMA guidelines. A search strategy was applied to extract articles from included databases: CINAHL, MEDLINE, SCOPUS and Web of Science Core Collection. Articles were limited to English language and published January 2007–March 2022 and assessed using a pre‐determined eligibility criteria. Quality assessment and a narrative synthesis were conducted. Results: A total of 3321 articles were identified, and 27 studies included. There were (n = 10) qualitative, (n = 4) quantitative non‐randomised controlled trials, (n = 3) quantitative descriptive studies and (n = 10) mixed methods. Nurse assessment and planning components were the most comprehensive phases incorporated into digital health systems, and interventions and outcome evaluation were scarcely reported. Conclusions: Inadequate capture of nursing work is a problem unresolved by digital health systems. This omission may be hindering nurse clinical decision‐making for patient care and limiting the visibility of the nursing role in health care interventions and the associated impact on patient outcomes. Relevance to clinical practice: Further research is needed on how digital systems can support nurses to apply the full nursing process and to further evaluate patient outcomes. Digital systems can support health‐service level evaluation through capturing missed nursing care and the consequences on patients utilising nurse‐sensitive‐outcomes; however, this is not yet being realised. [ABSTRACT FROM AUTHOR]

Background. The need for health systems that allow for continuous monitoring and early adverse event detection in individuals outside of the acute care setting has been highlighted by the global rise in chronic cardiorespiratory diseases and the recent COVID-19 pandemic. Currently, it is unclear what type of evidence exists concerning the use of physiological data collected from commercially available wrist and textile wearables to assist in clinical decision making. The aim of this review was therefore to systematically map and summarize the scientific literature surrounding the use of these wearables in clinical decision making as well as identify knowledge gaps to inform further research. Methodology. Six electronic bibliographic databases were systematically searched (Ovid MEDLINE, EMBASE, CINAHL, PubMed, Scopus, and SportsDiscus). Publications from database inception to May 6, 2020 were reviewed for inclusion. Non-indexed literature relevant to this review was also searched systematically. Results were then collated, summarized and reported. Results. A total of 107 citations were retrieved and assessed for eligibility with 31 citations included in the final analysis. A review of the 31 papers revealed three major study designs which included (1) observational studies (n = 19), (2) case control series and reports (n = 8), and (3) reviews (n = 2). All papers examined the use of wearable monitoring devices for clinical decisions in the cardiovascular domain, with cardiac arrhythmias being the most studied. When compared to electrocardiogram (ECG) the performance of the wearables in facilitating clinical decisions varied depending upon the type of wearable, user's activity levels and setting in which they were employed. Observational studies collecting data in the inpatient and outpatient settings were equally represented. Eight case control series and reports were identified which reported on the use of wrist wearables in patients presenting to an emergency department or clinic to aid in the clinical diagnosis of a cardiovascular event. Two narrative reviews were identified which examined the impact of wearable devices in monitoring cardiovascular disease as well as potential challenges they may pose in the future. Conclusions. To date, studies employing wearables to facilitate clinical decisions have largely focused upon the cardiovascular domain. Despite the ability of some wearables to collect physiological data accurately, there remains a need for a specialist physician to retrospectively review the raw data to make a definitive diagnosis. Analysis of the results has also highlighted gaps in the literature such as the absence of studies employing wearables to facilitate clinical decisions in the respiratory domain. The disproportionate study of wearables in atrial fibrillation detection in comparison to other cardiac arrhythmias and conditions, as well as the lack of diversity in the sample populations used prevents the generalizability of results. [ABSTRACT FROM AUTHOR]

Background: There is a paucity of literature describing how surgeons (either novice or expert) mentally prepare to carry out a surgical procedure. This paper focuses on these processes, and is part of a larger piece of research based on the Royal Australasian College of Surgeons (RACS) Clinical Decision Making model. Methods: Interviews were conducted over a 3‐year period with registrars, trainees, fellows and consultants in the Department of Surgery at one large regional hospital in Victoria. Analysis began from the first interview with no pre‐conceived codes. Emerging themes were drawn from participants' interpretation of their experiences. Further information was obtained during discussions in theatre while patients were being prepared for surgery. Results: The findings show that the process of rehearsal changes as a surgeon gains more experience in a procedure. A 'novice' relies on external sources of information, for example textbooks and videos. After participating in a number of similar procedures their reliance gradually moves to their own sensory memories. Surgeons at all levels of experience discuss their preparations with peers, colleagues, senior clinicians, and where appropriate, with members of other disciplines. Conclusion: These findings offer insight into how surgeons, at different levels of experience, prepare for a procedure. These understandings have the potential to improve the teaching and learning of this essential component of surgical practice. [ABSTRACT FROM AUTHOR]

Introduction: Emotional intelligence is a crucial factor in managing stress and maintaining overall well-being. Emotions are essential for clinical care quality, as they drive clinical decision making. Objective: The purpose of this study was to assess the relationship between emotional intelligence and clinical decision making among fourth-year nursing students. Methods: The study was a cross-sectional study done on a convenience sample of 225 fourth-year nursing students. Data collection was performed by the "Schutte Self Report Emotional Intelligence Test" (SSEIT) and the "clinical decision making scale". Results: The analysis revealed that the emotional intelligence mean was 151.3 ± 1.9 (ranging from 33 to 165), which is high. The analysis indicated that the clinical decision making of the participants was high (177.1.3 ± 9.8) (ranging from 40 to 200). Also, the analysis showed that there was a strong positive relationship between nursing students' emotional intelligence and clinical decision making (r =.70, p = 0.001). Furthermore, the findings revealed that emotional intelligence was a predictor of clinical decision making. Conclusion: The emotional intelligence and clinical decision making among fourth-year nursing students were high. Also, clinical decision making was found to have strong positive relationship with emotional intelligence. [ABSTRACT FROM AUTHOR]

Background: The ability of a nurse to make effective clinical decisions is the most important factor that can affect the treatment quality. However, several factors can affect the ability of nursing and midwifery students to make effective clinical decisions. Objectives: This study aims to identify the confounding factors that may affect the clinical decision making of nurses and thus patient outcomes after the COVID-19 pandemic in Jordan. Methods: A descriptive cross-sectional design was employed in this study. An online self-administered questionnaire was distributed to 269 nursing and midwifery students selected through purposive sampling, 224 of whom completed the questionnaire. The valid and reliable nursing decision-making instrument, which consisted of 24 items, was employed to gather the data, and descriptive statistics and simple linear regression were employed for the data analysis. Data was collected from November to the end of December 2022. Results: Among the respondents, 72.8% were female, and the average age was 20.79 years (SD = 1.44). The vast majority of the respondents (94.6%) was unmarried, and 74.1% were pursuing a nursing degree. The simple linear regression analysis showed that clinical decision making had a negative and significant relationship with social media usage of an average of 6 h a day (β=−0.085). Moreover, the male nursing students obtained lower clinical decision-making scores (β= −0.408) compared with the female nursing students. Conclusion: Social media usage and gender have a considerable effect on the clinical decision making of the nursing and midwifery students. Therefore, the confounding factors that can affect the clinical decision making of nurses should be discussed further, and strategies to address such factors should be implemented. [ABSTRACT FROM AUTHOR]

Background: In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany. Methods: The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models. Results: Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p < 0.001 in the validation cohort). Calibration curves show a good agreement between score-based predictions and actual observed mortality. Additional external validation using inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251) confirms the results regarding discrimination and calibration and underlines the geographic and temporal validity of the reweighted Frailty Score. Decision curve analysis indicates that the clinical usefulness of the reweighted score as a general decision support tool is superior to the initial version of the score. Assessment of the applicability of the reweighted Frailty Score in a non-elderly population (N = 198,819) shows that discrimination is superior to the initial version of the score (AUC = 0.92 vs. AUC = 0.87, p < 0.001). In addition, we observe a fairly age-stable influence of the reweighted Frailty Score on in-hospital mortality, which does not differ substantially for women and men. Conclusions: Our data indicate that the reweighted Frailty Score is superior to the original Frailty Score for identification of older, frail patients at risk for in-hospital mortality. Hence, we recommend using the reweighted Frailty Score in the German in-hospital setting. [ABSTRACT FROM AUTHOR]

Whereas the Precision Medicine approach plays a critical role in revolutionizing clinical decision-making, the accessibility of patient data in a customized way can empower this patient-centric clinical practice. Thus, successful utilization of the Internet of Things (IoT), is a technological booster for replacing the "one-size-fits-all" model to "patient-like-me" to tailor the treatment pathway according to the individual's variabilities in genetic profile, lifestyle, and environments indicators. This real-time and data-driven clinical decision-making model improves the quality of healthcare service, reduces side effects, cuts the cost of overt treatment, and saves more lives. This paper aims to highlight the significant role of IoT in emerging PM applications. [ABSTRACT FROM AUTHOR]

Although Healthcare Analytics (HA) plays a critical role to uncover the link between data and discover novel and actionable information, the level that each analytics influences clinical decision-making is different. This paper aims to highlight the role of each HA in respect to Precision Medicine (PM). Where, PM is defined as a new approach in medical decision-making that takes into account individual differences in genetic, environmental, and lifestyle indicators. Besides, Simon's model in decision-making assesses the performance of clinical decision-making followed by HA. In addition, SOAP framework is discussed as an example under the influence of HA for adopting PM. [ABSTRACT FROM AUTHOR]

Aim. Diagnostic uncertainty, risk perceptions, time constraints, and pressure from resident/family members and nurses have been identified as potential barriers to reducing antibiotic prescribing in residential aged-care facilities. The current study investigated the relationship between nurses' anxiety, clinical tolerance of uncertainty, and behaviors that favor antimicrobial initiation. Methods. A cross-sectional online survey was used to present a clinical vignette with measures of situational anxiety, clinical tolerance to uncertainty using the Physicians Reactions to Uncertainty Scale (adapted for nurses), and self-reported behavior on the Immediate Assessment and Antibiotics scale. The vignette described a hypothetical resident with a potential upper respiratory tract infection. Incremental information was provided with varying levels of symptoms, input from aged-care staff, and the availability of test results. Results. A total of 157 registered and enrolled nurses completed the survey from August 2021 to August 2022. Nurses' anxiety fully mediated on the relationship between clinical tolerance of uncertainty and perceived need for antibiotics and immediate assessment (P = 0.004), with 6% of variation explained by the model. Significant effects of clinical tolerance of uncertainty on anxiety (P < 0.001) and anxiety on antibiotic initiation (P = 0.002) were found, but there was no significant direct effect between clinical tolerance of uncertainty and antibiotic initiation (P = 0.34). Conclusions. This is the first study that links perceived need for antibiotics and assessment to nurses' anxiety and clinical tolerance of uncertainty. Thus, it may be that providing strategies for the management of anxiety related to uncertainty could nullify some processes contributing to inappropriate prescribing in aged-care settings Highlights: The impact of non-clinical factors (e.g., resident and family preferences) on prescribing is well-established. There is a gap in the literature regarding the mechanisms through which these preferences are experienced as pressure by prescribers within the unique context of residential aged-care facilities (RACFs). A significant relationship was found between nurses' anxiety, clinical tolerance of uncertainty, and the perceived need for antibiotics and assessment. As such, there is a need to expand stewardship beyond education alone to include interventions that help nurses manage uncertainty and anxiety and include other stakeholders (e.g., family members) when making clinical decisions in the RACF setting. [ABSTRACT FROM AUTHOR]

The tragic COVID-19 pandemic affected many children worldwide. Among the factors that may influence the course of viral infections including COVID-19, it is still uncertain whether atopy has a protective or predisposing role. The study aims to address the knowledge gap by investigating the prevalence and severity of COVID-19 among atopic children in Kerman, in 2022. A descriptive-analytical cross-sectional study on children with a history of atopy was performed in Kerman Medical University. Demographic information, type of atopy (including allergic rhinitis, Hyper-Reactive Airway Disease (HRAD) or asthma, eczema, urticaria, anaphylaxis, and food allergy), history of COVID-19 infection, and disease severity were recorded. A total of 1007 children and adolescents, (boys: 56.4%, girls: 43.6%, age:5.61±2.64 years) were included in the study. History of COVID-19 infection was positive in 53.5%, with 75.9% of the cases exhibiting mild disease severity. The frequency of atopies was HRAD or asthma (67.2%), allergic rhinitis (42.6%), and food allergy (27.4%). The frequency of COVID-19 cases was significantly higher among patients with HRAD or asthma, whereas it was significantly lower among those with food allergies, anaphylaxis, and eczema. Among atopic individuals, COVID-19 severity was significantly lower in those with allergic rhinitis, while the opposite trend was observed among food-allergic individuals. This study sheds light on the relationship between atopy and COVID-19 among pediatric patients. It seems specific types of atopies may influence the risk and severity of COVID-19 infection differently. A better understanding of these associations can inform clinical management and preventive measures for vulnerable pediatric populations. [ABSTRACT FROM AUTHOR]

Objective: We appraised whether FDA registration trials for ADHD pharmacotherapy in adults provides comparable information to inform treatment expectations. Method: Comparison of ADHD outcome measure patterns in ADHD pharmacotherapy FDA drug label source studies. Results: Among stimulants, from fixed-dose titration data, amphetamine agents had numerically higher placebo-corrected symptom improvement and symptom effect sizes than methylphenidate agents. Symptom effect sizes were lower in the flexible dosing registration studies of atomoxetine and viloxazine. Varying responder definitions were analyzable, based on ≥30% symptom improvement and/or CGI-I improvement of "much" or "very much improved." Number of exposures needed to create these responses were lower for stimulants than for viloxazine. Conclusion: Heterogeneity in the design and analysis of FDA drug label source trials restricts implications for clinical practice. Research conducted using replicated designs, direct comparison of available treatments, and outcome analyses that generalize to clinical care could better inform clinical decision making. [ABSTRACT FROM AUTHOR]

Background The need for health systems that allow for continuous monitoring and early adverse event detection in individuals outside of the acute care setting has been highlighted by the global rise in chronic cardiorespiratory diseases and the recent COVID-19 pandemic. Currently, it is unclear what type of evidence exists concerning the use of physiological data collected from commercially available wrist and textile wearables to assist in clinical decision making. The aim of this review was therefore to systematically map and summarize the scientific literature surrounding the use of these wearables in clinical decision making as well as identify knowledge gaps to inform further research. Methodology Six electronic bibliographic databases were systematically searched (Ovid MEDLINE, EMBASE, CINAHL, PubMed, Scopus, and SportsDiscus). Publications from database inception to May 6, 2020 were reviewed for inclusion. Non-indexed literature relevant to this review was also searched systematically. Results were then collated, summarized and reported. Results A total of 107 citations were retrieved and assessed for eligibility with 31 citations included in the final analysis. A review of the 31 papers revealed three major study designs which included (1) observational studies (n = 19), (2) case control series and reports (n = 8), and (3) reviews (n = 2). All papers examined the use of wearable monitoring devices for clinical decisions in the cardiovascular domain, with cardiac arrhythmias being the most studied. When compared to electrocardiogram (ECG) the performance of the wearables in facilitating clinical decisions varied depending upon the type of wearable, user’s activity levels and setting in which they were employed. Observational studies collecting data in the inpatient and outpatient settings were equally represented. Eight case control series and reports were identified which reported on the use of wrist wearables in patients presenting to an emergency department or clinic to aid in the clinical diagnosis of a cardiovascular event. Two narrative reviews were identified which examined the impact of wearable devices in monitoring cardiovascular disease as well as potential challenges they may pose in the future. Conclusions To date, studies employing wearables to facilitate clinical decisions have largely focused upon the cardiovascular domain. Despite the ability of some wearables to collect physiological data accurately, there remains a need for a specialist physician to retrospectively review the raw data to make a definitive diagnosis. Analysis of the results has also highlighted gaps in the literature such as the absence of studies employing wearables to facilitate clinical decisions in the respiratory domain. The disproportionate study of wearables in atrial fibrillation detection in comparison to other cardiac arrhythmias and conditions, as well as the lack of diversity in the sample populations used prevents the generalizability of results.

The 2021 virtual issue of the Health Information and Libraries Journal is published to link to the Future Technologies Conference 2021, which takes place in Vancouver, Canada, 28–29 October 2021. This event, which began in 2016, presents the best of current systems research and practice. One of the themes of the 2021 conference is Smart Healthcare. Although libraries may appear to be far removed from some of the breakthroughs reported in the Future Technologies Conference, what is clear is that unlike those who work with technology, librarians work closely with the end users of health information—clinicians, managers, students, patients and the general public. The articles included in this virtual issue provide important insights into what library user groups want and what does and does not meet their needs. [ABSTRACT FROM AUTHOR]

Background: Like many countries where voluntary assisted dying (VAD) is legal, eligible doctors in Victoria, Australia, have sole legal authority to provide it. Doctors' attitudes towards legalised VAD have direct bearing on their willingness to participate in VAD and consequently, on whether permissive laws can effectively facilitate access to VAD. The study aimed to explore how some Victorian doctors are perceiving and experiencing the provision of legalised VAD under a recently commenced law. Methods: Semi-structured interviews with 25 Victorian doctors with no in-principle objection to legalised VAD were conducted between July 2019-February 2020. Interviews were recorded, transcribed, and analysed using thematic analysis. Ethical approval from the relevant institution was obtained. Results: Doctors perceive or experience VAD to fundamentally challenge traditional medical practice. Barriers to access to VAD derive from applicant, communication, and doctor-related factors. Doctors' willingness to participate in VAD is situation specific. [ABSTRACT FROM AUTHOR]

Portopulmonary hypertension is a relatively common pathologic condition in patients with end-stage liver disease. Traditionally, severe pulmonary hypertension is regarded as a contraindication to liver transplantation (LT) due to a high perioperative mortality rate. Recently, extracorporeal membrane oxygenation (ECMO) has been utilized for intraoperative management of LT. As venoarterial (VA) ECMO may benefit certain high-risk LT patients by reducing the ventricular workload by the equivalent of the programmed flow rate, its usage requires multidisciplinary planning with considerations of the associated complications. We highlighted two cases at our single-center institution as examples of high-risk pulmonary hypertension patients undergoing LT on planned VA ECMO. These patients both survived the intraoperative period; however, they had drastically different postoperative outcomes, generating discussions on the importance of judicious patient selection. Since ECMO has removed the barrier of intraoperative survivability, the patient selection process may need to put weight on the patient's potential for postoperative recovery and rehabilitation. Considerations on LT recipients undergoing preemptive ECMO need to expand from the ability of the patients to withstand the demands of the surgery during the immediate perioperative period to the long-term postoperative recovery course. [ABSTRACT FROM AUTHOR]

As COVID-19 pandemic public health measures are easing globally, the emergence of new SARS-CoV-2 strains continue to present high risk for vulnerable populations. The antibody-mediated protection acquired from vaccination and/or infection is seen to wane over time and the immunocompromised populations can no longer expect benefit from monoclonal antibody prophylaxis. Hence, there is a need to monitor new variants and its effect on vaccine performance. In this context, surveillance of new SARS-CoV-2 infections and serology testing are gaining consensus for use as screening methods, especially for at-risk groups. Here, we described an improved COVID-19 screening strategy, comprising predictive algorithms and concurrent, rapid, accurate, and quantitative SARS-CoV-2 antigen and host antibody testing strategy, at point of care (POC). We conducted a retrospective analysis of 2553 pre- and asymptomatic patients who were tested for SARS-CoV-2 by RT-PCR. The pre-screening model had an AUC (CI) of 0.76 (0.73–0.78). Despite being the default method for screening, body temperature had lower AUC (0.52 [0.49–0.55]) compared to case incidence rate (0.65 [0.62–0.68]). POC assays for SARS-CoV-2 nucleocapsid protein (NP) and spike (S) receptor binding domain (RBD) IgG antibody showed promising preliminary results, demonstrating a convenient, rapid (<20 min), quantitative, and sensitive (ng/mL) antigen/antibody assay. This integrated pre-screening model and simultaneous antigen/antibody approach may significantly improve accuracy of COVID-19 infection and host immunity screening, helping address unmet needs for monitoring vaccine effectiveness and severe disease surveillance. [ABSTRACT FROM AUTHOR]

The ability of COVID-19 to compromise the respiratory system has generated a substantial proportion of critically ill patients in need of invasive mechanical ventilation (IMV). The objective of this paper was to analyze the prognostic ability of the pulse oximetry saturation/fraction of inspired oxygen ratio (SpO2/FiO2) and the ratio of SpO2/FiO2 to the respiratory rate–ROX index–as predictors of IMV in an emergency department in confirmed COVID-19 patients. A multicenter, retrospective cohort study was carried out in four provinces of Spain between March and November 2020. The discriminative power of the predictive variable was assessed through a prediction model trained using a derivation sub-cohort and evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) on the validation sub-cohort. A total of 2040 patients were included in the study. The IMV rate was 10.1%, with an in-hospital mortality rate of 35.3%. The performance of the SpO2/FiO2 ratio was better than the ROX index–AUC = 0.801 (95% CI 0.746–0.855) and AUC = 0.725 (95% CI 0.652–0.798), respectively. In fact, a direct comparison between AUCs resulted in significant differences (p = 0.001). SpO2 to FiO2 ratio is a simple and promising non-invasive tool for predicting risk of IMV in patients infected with COVID-19, and it is realizable in emergency departments. [ABSTRACT FROM AUTHOR]

Background: Clinical reasoning (CR) is a holistic and recursive cognitive process. It allows nursing students to accurately perceive patients’ situations and choose the best course of action among the available alternatives. This study aimed to identify the randomised controlled trials studies in the literature that concern clinical reasoning in the context of nursing students. Methods: A comprehensive search of PubMed, Scopus, Embase, and the Cochrane Controlled Register of Trials (CENTRAL) was performed to identify relevant studies published up to October 2023. The following inclusion criteria were examined: (a) clinical reasoning, clinical judgment, and critical thinking in nursing students as a primary study aim; (b) articles published for the last eleven years; (c) research conducted between January 2012 and September 2023; (d) articles published only in English and Spanish; and (e) Randomised Clinical Trials. The Critical Appraisal Skills Programme tool was utilised to appraise all included studies. Results: Fifteen papers were analysed. Based on the teaching strategies used in the articles, two groups have been identified: simulation methods and learning programs. The studies focus on comparing different teaching methodologies. Conclusions: This systematic review has detected different approaches to help nursing students improve their reasoning and decision-making skills. The use of mobile apps, digital simulations, and learning games has a positive impact on the clinical reasoning abilities of nursing students and their motivation. Incorporating new technologies into problem-solving-based learning and decision-making can also enhance nursing students’ reasoning skills. Nursing schools should evaluate their current methods and consider integrating or modifying new technologies and methodologies that can help enhance students’ learning and improve their clinical reasoning and cognitive skills.

Patients clinically suspected of community-acquired pneumonia (CAP) were randomized between ultralow-dose chest computed tomography ([ULDCT] 261 patients) and chest radiograph ([CXR] 231 patients). We did not find evidence that performing ULDCT instead of CXR affects antibiotic treatment policy or patient outcomes. However, in a subgroup of afebrile patients, there were more patients diagnosed with CAP in the ULDCT group (ULDCT, 106 of 608 patients; CXR, 71 of 654 patients; P =.001). [ABSTRACT FROM AUTHOR]

INTRODUCTION: We describe the development and feasibility of using an online consensus approach for diagnosing cognitive impairment and dementia in rural South Africa. METHODS: Cognitive assessments, clinical evaluations, and informant interviews from Cognition and Dementia in the Health and Aging in Africa Longitudinal Study (HAALSI Dementia) were reviewed by an expert panel using a web‐based platform to assign a diagnosis of cognitively normal, mild cognitive impairment (MCI), or dementia. RESULTS: Six hundred thirty‐five participants were assigned a final diagnostic category, with 298 requiring adjudication conference calls. Overall agreement between each rater's independent diagnosis and final diagnosis (via the portal or consensus conference) was 78.3%. A moderate level of agreement between raters' individual ratings and the final diagnostic outcomes was observed (average κ coefficient = 0.50). DISCUSSION: Findings show initial feasibility in using an online consensus approach for the diagnosis of cognitive impairment and dementia in remote, rural, and low‐resource settings. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: The development of clinical practice guidelines (CPG) suffers from the lack of an explicit and transparent framework for synthesising the key elements necessary to formulate practice recommendations. We matched deliberations of the American Society of Haematology (ASH) CPG panel for the management of pulmonary embolism (PE) with the corresponding decision‐theoretical constructs to assess agreement of the panel recommendations with explicit decision modelling. Methods: Five constructs were identified of which three were used to reformulate the panel's recommendations: (1) standard, expected utility threshold (EUT) decision model; (2) acceptable regret threshold model (ARg) to determine the frequency of tolerable false negative (FN) or false positive (FP) recommendations, and (3) fast‐and‐frugal tree (FFT) decision trees to formulate the entire strategy for management of PE. We compared four management strategies: withhold testing versus d‐dimer → computerized pulmonary angiography (CTPA) ('ASH‐Low') versus CTPA→ d‐dimer ('ASH‐High') versus treat without testing. Results: Different models generated different recommendations. For example, according to EUT, testing should be withheld for prior probability PE < 0.13%, a clinically untenable threshold which is up to 15 times (2/0.13) below the ASH guidelines threshold of ruling out PE (at post probability of PE ≤ 2%). Three models only agreed that the 'ASH low' strategy should be used for the range of pretest probabilities of PE between 0.13% and 13.27% and that the 'ASH high' management should be employed in a narrow range of the prior PE probabilities between 90.85% and 93.07%. For all other prior probabilities of PE, choosing one model did not ensure coherence with other models. Conclusions: CPG panels rely on various decision‐theoretical strategies to develop its recommendations. Decomposing CPG panels' deliberation can provide insights if the panels' deliberation retains a necessary coherence in developing guidelines. CPG recommendations often do not agree with the EUT decision analysis, widely used in medical decision‐making modelling. [ABSTRACT FROM AUTHOR]

Background: Triage is a process that prevents Children from being hospitalized unnecessarily by quickly diagnosing and identifying the children's condition. Due to physiological and anatomical differences in children, nursing students must pass at least one course of pediatric triage. This study aims to evaluate the effectiveness of outcome-based pediatric triage education on knowledge and decision making of nursing students. Methods: This is a quasi-experimental study with pretest-posttest design. In this study, 76 nursing students were enrolled and then were randomly divided into two groups of traditional (slidepresentation) or outcome-based method (slide-presentation, movie and simulation). Before the sessions and one month later the level of knowledge and decision-making were assessed. Data were collected using a researcher-made questionnaire according to Emergency Severity Index (ESI) triage, version 4. After entering data in SPSS software, they were analysed using descriptive and inferential statistical tests. Results: The results indicate that there is no statistically significant difference between the adjusted mean score of students' decision-making in traditional education groups and outcome-based education (P=0.485). Also, there is no statistically significant difference between the adjusted mean score of students' knowledge in traditional education groups and outcome-based education (P=0.340). Conclusion: This study showed that outcome-based education is an appropriate teaching method that can increases decision-making power and level of knowledge among nursing students. Because the importance of pediatric triage, it is recommended that pediatric triage will provide as a separate lesson in Bachelor’s degree of nursing and faculty members pay more attention to it. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: Critics have charged that evidence‐based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to an algorithm based on evidence‐based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion. Methods: An observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID‐19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID‐19 was converted into a clinical pathway and translated into fast‐and‐frugal decision (FFT) tree ('algorithm'). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion. Results: In an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse‐probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23–143), indicating that for every 40 patients (23–143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive‐care unit (ICU) [NNT = 19 (95% CI: 13–40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings. Conclusions: When delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes. [ABSTRACT FROM AUTHOR]

The term intramural (interstitial) ectopic pregnancy refers to a pregnancy developing outside the uterine cavity, with a gestational sac implanted into the interstitial part of the Fallopian tube, surrounded by a layer of the myometrium. The prevalence rate of interstitial pregnancy (IP) is 2–4% of all ectopic pregnancies. Surgery is the primary treatment for interstitial ectopic pregnancy; the pharmacological management of ectopic pregnancy, including IP, in asymptomatic patients includes systemic administration of methotrexate. In this report, we present two cases of this rare pregnancy type, reviewing our management technique and treatment ways presented in the literature. In our patients, the management was initially conservative and included methotrexate, administered as intravenous bolus injection, regular beta-human chorionic gonadotropins (β-HCG) level measurements in peripheral blood, and monitoring of the patient's general condition. Due to signs of intra-abdominal bleeding in patient A and inadequate β-HCG level reduction in patient B, both patients eventually underwent laparoscopic cornual resection. Pregnancy, implanted into the interstitial part of the Fallopian tube and surrounded by myometrial tissue with myometrial invasion of the trophoblast, poses a serious diagnostic challenge to modern gynecology due to particularly low sensitivity and specificity of symptoms, and may require both pharmacological and surgical treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: The aim of this review is to evaluate the present status of the use of cell-free DNA and its fraction of circulating tumor DNA (ctDNA) because this year July 2022, an ESMO guideline was published regarding the application of ctDNA in patient care. In the near future the data obtained from ctDNA may routinely be used for finding minimal residual disease, detecting relapse, determining either the unknown primary tumor, or the site of metastases. It can also be used for deciding upon the appropriate efficiency of the therapy and/or emerging resistance to the therapy. Therefore, clinicians should be aware of the potentials and the limitations of the assays. Of course, several open questions are still under research and as a result, cfDNA and ctDNA testing are not part of routine care yet. The aim of this review is to evaluate the present status of the use of cell-free DNA and its fraction of circulating tumor DNA (ctDNA) because this year July 2022, an ESMO guideline was published regarding the application of ctDNA in patient care. This review is for clinical oncologists to explain the concept, the terms used, the pros and cons of ctDNA; thus, the technical aspects of the different platforms are not reviewed in detail, but we try to help in navigating the current knowledge in liquid biopsy. Since the validated and adequately sensitive ctDNA assays have utility in identifying actionable mutations to direct targeted therapy, ctDNA may be used for this soon in routine clinical practice and in other different areas as well. The cfDNA fragments can be obtained by liquid biopsy and can be used for diagnosis, prognosis, and selecting among treatment options in cancer patients. A great proportion of cfDNA comes from normal cells of the body or from food uptake. Only a small part (<1%) of it is related to tumors, originating from primary tumors, metastatic sites, or circulating tumor cells (CTCs). Soon the data obtained from ctDNA may routinely be used for finding minimal residual disease, detecting relapse, and determining the sites of metastases. It might also be used for deciding appropriate therapy, and/or emerging resistance to the therapy and the data analysis of ctDNA may be combined with imaging or other markers. However, to achieve this goal, further clinical validations are inevitable. As a result, clinicians should be aware of the limitations of the assays. Of course, several open questions are still under research and because of it cfDNA and ctDNA testing are not part of routine care yet. [ABSTRACT FROM AUTHOR]

National Health Service (NHS) knowledge and library services in England are integrating digital advances into their systems and services. Health Education England (HEE) leads on the development of NHS library services. A key workstream focuses on (1) improving the infrastructure to enable discovery and management of digital knowledge resources; (2) collaborating with local teams to establish regional library management systems that are integrated with the new national discovery service for healthcare staff and learners. This article explores initiatives on resource discovery as well as the need for system‐wide partnership working to ensure that biomedical knowledge in computable form is findable, accessible, interoperable and reusable. Low levels of health and digital literacy pose a significant barrier to using health information and accessing health services. A range of interventions are aimed at enhancing citizens' digital and health literacy skills. The education and life‐long learning needs of the knowledge and library services workforce are considered. Working with CILIP and higher education institutions, HEE delivers a range of educational offers through its Learning Academy. As Artificial Intelligence and automation are implemented in health systems, knowledge and library staff form a crucial bridge between technology and those who use it. [ABSTRACT FROM AUTHOR]