E-learning use in the review of neonatal resuscitation program in physicians: a scoping review.
Ontology highlight
ABSTRACT: Objective
To determine if e-learning interventions are efficient to review Neonatal Resuscitation Program (NRP) and to prevent performance deterioration in neonatal resuscitation of already-certified healthcare professionals.Study design
In this scoping review, we searched for manuscripts published until June 2020 in five databases. We included all studies on e-learning use for NRP review in already-certified healthcare providers.Results
Among 593 abstracts retrieved, 38 full-text articles were assessed for eligibility. Five studies were included. Four studies evaluated the effectiveness of e-learning interventions immediately or months after their completion by providers. These interventions did not consistently enhance their NRP knowledge and their performance. One study showed that a growth mindset can influence positively neonatal resuscitation performance after an e-learning simulation.Conclusion
There is not enough evidence to conclude that e-learning interventions can prevent neonatal resuscitation knowledge and performance decay in already-certified providers. More research is needed on the use of e-learning simulation-based scenarios to improve NRP retention.
SUBMITTER: Spenard S
PROVIDER: S-EPMC9107007 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:AimTo review the literature on briefing and debriefing in neonatal resuscitation using International Liaison Committee on Resuscitation (ILCOR) methodology to see if a formal systematic review is justified.MethodsThis scoping review was undertaken by an ILCOR Newborn Life Support scoping review team and guided by the ILCOR methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). Studies were eligible for inclusion if they were peer-reviewed, compared briefing/debriefing of healthcare professionals who had completed a neonatal resuscitation or simulated resuscitation and reported outcomes for infants, families or staff. PubMed, Embase, Cochrane, and Web of Science databases were searched.ResultsThis review included four studies that reported on three briefing/debriefing interventions: video debriefing, the use of checklists with a briefing/debriefing component and rapid cycle deliberate practice. Video debriefing was associated with improvements in the process of care and adherence to resuscitation guidelines. Use of checklists was associated with improvements in short term clinical outcomes and a reduction in communication problems. Rapid cycle deliberate practice may lead to short but not sustained improvements in algorithm compliance and timely completion of resuscitation steps.ConclusionThis scoping review did not identify sufficient new evidence to justify conducting new systematic reviews or review of current resuscitation guidelines. Improvements in the process of care, short term clinical outcomes and reduction in communication problems were associated with briefing/debriefing supported by video, checklists or rapid, cycle deliberate practice. It highlights knowledge gaps, including the need to consider briefing/debriefing separately from other interventions, the effect of briefing/debriefing on short- and long-term clinical outcomes and the effect of rapid cycle deliberate practice on resuscitation training.
| S-EPMC8244473 | biostudies-literature
Project description:AimNeonatal resuscitation guidelines promote the laryngeal mask (LM) interface for positive pressure ventilation (PPV), but little is known about how the LM is used among Neonatal Resuscitation Program (NRP) Providers and Instructors. The study aim was to characterize the training, experience, confidence, and perspectives of NRP Providers and Instructors regarding LM use during neonatal resuscitation at birth.MethodsA voluntary anonymous survey was emailed to all NRP Providers and Instructors. Survey items addressed training, experience, confidence, and barriers for LM use during resuscitation. Associations between respondent characteristics and outcomes of both LM experience and confidence were assessed using logistic regression.ResultsBetween 11/7/22-12/12/22, there were 5,809 survey respondents: 68% were NRP Providers, 55% were nurses, and 87% worked in a hospital setting. Of these, 12% had ever placed a LM during newborn resuscitation, and 25% felt very or completely confident using a LM. In logistic regression, clinical or simulated hands-on training, NRP Instructor role, professional role, and practice setting were all associated with both LM experience and confidence.The three most frequently identified barriers to LM use were insufficient experience (46%), preference for other interfaces (25%), and failure to consider the LM during resuscitation (21%). One-third (33%) reported that LMs are not available where they resuscitate newborns.ConclusionFew NRP providers and instructors use the LM during neonatal resuscitation. Strategies to increase LM use include hands-on clinical training, outreach promoting the advantages of the LM compared to other interfaces, and improving availability of the LM in delivery settings.
| S-EPMC10716019 | biostudies-literature
Project description:Study aimTo summarize the current state of knowledge of deliberate practice and mastery learning (DP and/or ML) as teaching methods for resuscitation education.MethodsA scoping review of PubMed, Scopus, and Embase was conducted through March 1, 2021. Studies examining the effect of the incorporation of either deliberate practice and/or mastery learning during resuscitation education were eligible for inclusion. Included studies were dichotomized into studies comparing deliberate practice and/or mastery learning to other training methods (randomized controlled trials) and studies examining before and after impact of deliberate practice and/or mastery learning alone (observational studies). Studies and findings were tabulated and summarized using the scoping review methodology published by Arksey and O'Malley.Results63 published studies were screened; sixteen studies met all inclusion criteria (4 randomized controlled trials and 12 observational studies). One randomized controlled trial and eleven observational studies demonstrated improvement in skill and/or knowledge following educational interventions using deliberate practice and/or mastery learning. Significant variability between studies with regard to research designs, learner groups, comparators, and outcomes of interest made quantitative summarization of findings difficult.ConclusionsThe incorporation of deliberate practice and/or mastery learning in resuscitation education may be associated with improved educational outcomes and less skill decay than other educational methods. Current literature on DP and ML suffers from a lack of consistency in research methodology, subjects, and outcomes. Future research should employ uniform definitions for deliberate practice and mastery learning, follow research design that isolates its effect, and examine generalizable and translatable outcomes.
| S-EPMC8244416 | biostudies-literature
Project description:IntroductionNeonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are non-specific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment.MethodsPubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning.ResultsThere were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks.ConclusionDespite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis.
| S-EPMC10308703 | biostudies-literature
Project description:BackgroundHealth care professionals are increasingly using smartphones in clinical care. Smartphone use can affect patient quality of care and clinical outcomes.ObjectiveThis scoping review aimed to describe how physicians use smartphones and mobile apps in clinical settings.MethodsWe conducted a scoping review using the Joanna Briggs Institute methodology and reported the results according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. We used the following databases in our literature search: MEDLINE, Embase, Cochrane Library, Web of Science, Google Scholar, and gray literature for studies published since 2010. An additional search was also performed by scanning the reference lists of included studies. A narrative synthesis approach was used.ResultsA total of 10 studies, published between 2016 and 2021, were included in this review. Of these studies, 8 used surveys and 2 used surveys with focus group study designs to explore smartphone use, its adoption, experience of using it, and views on the use of smartphones among physicians. There were studies with only general practitioners (n=3), studies with only specialists (n=3), and studies with both general practitioners and specialists (n=4). Physicians use smartphones and mobile apps for communication (n=9), clinical decision-making (n=7), drug compendium (n=7), medical education and training (n=7), maintaining health records (n=4), managing time (n=4), and monitoring patients (n=2) in clinical practice. The Medscape medical app was frequently used for information gathering. WhatsApp, a nonmedical app, was commonly used for physician-patient communication. The commonly reported barriers were lack of regulatory oversight, privacy concerns, and limited Wi-Fi or internet access. The commonly reported facilitator was convenience and having access to evidence-based medicine, clinical decision-making support, and a wide array of apps.ConclusionsSmartphones and mobile apps were used for communication, medical education and training, clinical decision-making, and drug compendia in most studies. Although the benefits of smartphones and mobile apps for physicians at work were promising, there were concerns about patient privacy and confidentiality. Legislation is urgently needed to protect the liability of health care professionals using smartphones.
| S-EPMC10131676 | biostudies-literature
Project description:IntroductionCardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22% of survivors achieve good clinical status and only 25% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation.MethodsWe conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits.ResultsAfter identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance.ConclusionAI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.
| S-EPMC10054374 | biostudies-literature
Project description:BackgroundThe personal health and professional impact of physician pregnancy requires further study. We performed a comprehensive scoping review of physician pregnancy to synthesize and assess the evidence to aid decision-making for relevant stakeholders.MethodsA search of 7 databases resulted in 3733 citations. 407 manuscripts were included and scored for evidence level. Data were extracted into themes using template analysis.ResultsPhysician pregnancy impacted colleagues through perceived increased workload and resulted in persistent stigmatization and discrimination despite work productivity and academic metrics being independent of pregnancy events. Maternity leave policies were inconsistent and largely unsatisfactory. Women physicians incurred occupational hazard risk and had high rates of childbearing delay, abortion, and fertility treatment; obstetric and fetal complication rates compared to controls are conflicting.ConclusionsComprehensive literature review found that physician pregnancy impacts colleagues, elicits negative perceptions of productivity, and is inadequately addressed by current parental leave policies. Data are poor and insufficient to definitively determine the impact of physician pregnancy on maternal and fetal health. Prospective risk-matched observational studies of physician pregnancy should be pursued.
| S-EPMC8688196 | biostudies-literature
Project description:IntroductionThe ongoing pandemic of COVID-19 brought to the fore prone positioning as treatment for patients with acute respiratory failure. With the increasing number of patients in prone position, both spontaneously breathing and mechanically ventilated, cardiac arrest in this position is more likely to occur. This scoping review aimed to summarize the available evidence on cardiopulmonary resuscitation in prone position ('reverse CPR') and knowledge or research gaps to be further evaluated. The protocol of this scoping review was prospectively registered on 10th May 2020 in Open Science Framework (https://osf.io/nfuh9).MethodsWe searched PubMed, EMBASE, MEDLINE and pre-print repositories (bioRxiv and medRxiv) for simulation, pre-clinical and clinical studies on reverse CPR until 31st May 2020.ResultsWe included 1 study on manikins, 31 case reports (29 during surgery requiring prone position) and 2 nonrandomized studies describing reverse CPR. No studies were found regarding reverse CPR in patients with COVID-19.ConclusionsEven if the algorithms provided by the guidelines on basic and advanced life support remain valid in cardiac arrest in prone position, differences exist in the methods of performing CPR. There is no clear evidence of superiority in terms of effectiveness of reverse compared to supine CPR in patients with cardiac arrest occurring in prone position. The quality of evidence is low and knowledge gaps (e.g. protocols, training of healthcare personnel, devices for skill acquisition) should be fulfilled by further research. Meanwhile, a case-by-case evaluation of patient and setting characteristics should guide the decision on how to start CPR in such cases.
| S-EPMC7481259 | biostudies-literature
Project description:Background and objectiveVirtual reality (VR) and augmented reality (AR) have been proposed as novel methods to enhance cardio-pulmonary resuscitation (CPR) performance and increase engagement with CPR training. A scoping review was conducted to map the global evolution of these new approaches to CPR training, to assess their efficacy and determine future directions to meet gaps in current knowledge.MethodsA standardised five-stage scoping methodology was used to (1) identify the research question, (2) identify relevant studies, (3) select the studies, (4) chart the data and (5) summarise the findings. The Kirkpatrick model levels of evidence were used to chart and assess the efficacy of each intervention reported. A multi-pronged search term strategy was used to search the Web of Science, PubMed, CINAHL and EMBASE databases up to June 2020.ResultsA total of 42 articles were included in this review. The first relevant paper identified was published in 2009 and based on VR, from 2014 onwards there was a large increase in the volume of work being published regarding VR and AR uses in CPR training. This review reports Kirkpatrick level one to three evidence for the use of VR/AR-CPR. Inconsistencies in the specific language, keywords used and methodologies are highlighted.ConclusionVR and AR technologies have shown great potential in the area of CPR, and there is continuing evidence of new novel applications and concepts. As VR/AR research into CPR reaches an inflection point, it is key to bring collaboration and consistency to the wider research community, to enable the growth of the area and ease of access to the wider medical community.
| S-EPMC8040758 | biostudies-literature
Project description:BACKGROUND:Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. METHODS:Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010-2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. RESULTS:Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and "omics" data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). CONCLUSION:Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.
| S-EPMC7285453 | biostudies-literature