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Objectives To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe. Materials and Methods This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures. Results The ambient AI scribe was utilized in 9629 of 17 428 encounters (55.25%) with significant interuser heterogeneity. Compared to baseline, median time per note reduced significantly by 0.57 minutes. Median daily documentation, afterhours, and total EHR time also decreased significantly by 6.89, 5.17, and 19.95 minutes/day, respectively. Discussion An early pilot of an ambient AI scribe demonstrated robust utilization and reduced time spent on documentation and in the EHR. There was notable individual-level heterogeneity. Conclusion Large language model-powered ambient AI scribes may reduce documentation burden. Further studies are needed to identify which users benefit most from current technology and how future iterations can support a broader audience. [ABSTRACT FROM AUTHOR]

Objective This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout. Materials and Methods This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings. Results Paired survey analysis (n  = 38) revealed large statistically significant reductions in task load (−24.42, p <.001) and burnout (−1.94, p <.001), and moderate statistically significant improvements in usability scores (+10.9, p <.001). Post-survey responses (n  = 46) indicated favorable utility with improved perceptions of efficiency, documentation quality, and ease of use. Discussion In one of the first pilot implementations of ambient AI scribe technology, improvements in physician task load, burnout, and usability were demonstrated. Conclusion Ambient AI scribes like DAX Copilot may enhance clinical workflows. Further research is needed to optimize widespread implementation and evaluate long-term impacts. [ABSTRACT FROM AUTHOR]

Background: Wall shear stress (WSS) plays a crucial role in the natural history of intracranial aneurysms (IA). However, spatial variations among WSS have rarely been utilized to correlate with IAs' natural history. This study aims to establish the feasibility of using spatial patterns of WSS data to predict IAs' rupture status (i.e., ruptured versus unruptured). Methods: "Patient-specific" computational fluid dynamics (CFD) simulations were performed for 112 IAs; each IA's rupture status was known from medical records. Recall that CFD-simulated hemodynamics data (wall shear stress and its derivatives) are located on unstructured meshes. Hence, we mapped WSS data from an unstructured grid onto a unit disk (i.e., a uniformly sampled polar coordinate system); data in a uniformly sampled polar system is equivalent to image data. Mapped WSS data (onto the unit disk) were readily available for Radiomics analysis to extract spatial patterns of WSS data. We named this innovative technology "WSS-informatics" (i.e., using informatics techniques to analyze WSS data); the usefulness of WSS-informatics was demonstrated during the predictive modeling of IAs' rupture status. Results: None of the conventional WSS parameters correlated to IAs' rupture status. However, WSS-informatics metrics were discriminative (p-value < 0.05) to IAs' rupture status. Furthermore, predictive models with WSS-informatics features could significantly improve the prediction performance (area under the receiver operating characteristic curve [AUROC]: 0.78 vs. 0.85; p-value < 0.01). Conclusion: The proposed innovations enabled the first study to use spatial patterns of WSS data to improve the predictive modeling of IAs' rupture status. [ABSTRACT FROM AUTHOR]

Prescribing errors are a source of preventable harm in healthcare, which may be mitigated using Electronic Prescribing (EP) systems. Anyone who routinely prescribes medication could benefit from digitally assisted automated checks to identify whether a prescription should potentially not be allowed (e.g. drug allergy). National Health Service (NHS) Trusts have implemented a range of EP systems; however, their performance has not hitherto been evaluated. We developed the web-based Electronic Prescribing Risk and Safety Evaluation (ePRaSE) tool, which comprises a bank of prescribing scenarios to evaluate the performance of EP systems. We solicited ePRaSE testing: 68 pharmacists from across 45 English NHS Trusts, utilising 13 different EP systems volunteered for the study. We found considerable variability in mitigation performance (systems correctly identifying risk of error when prescribing) across both NHS Trusts and EP systems. Moreover, we found that mitigation performance varied considerably across NHS Trusts using the same EP system, strongly suggesting there are opportunities to optimise performance within systems. The ePRaSE tool is effective in identifying variability in risk management between NHS Trusts and EP systems. Wider use of this tool may facilitate improvements in EP system configurations, thus minimising potential harm from prescribing errors. [ABSTRACT FROM AUTHOR]

In the era where digital technologies are becoming increasingly prevalent, it is anticipated that a majority of the global population will have at least a basic understanding of informatics. However, empirical evidence suggests that a significant portion of the global population remains digitally illiterate. This phenomenon is particularly pronounced in the case of the senior adult population. In light of the aforementioned challenges, this work integrates Computer Science Unplugged exercises, based on games and recreational activities without the use of computers, and L2T2L, a learning-by-teaching methodology whereby university students learn and then, in turn, teach that learning to other populations in a cascading manner. A case study was conducted in Lima, Peru, with the participation of 140 volunteers from centres for the elderly. Thirty-five students and one teacher from the Universidad Científica del Sur were responsible for initiating the transfer of knowledge from the university to the senior citizens, with the assistance of twelve individuals responsible for their care. The results demonstrate that the participants attained a commendable level of comprehension when attempting to complete all of the assigned tasks. Furthermore, the efficacy of L2T2L is evident in its adaptability and suitability for scenarios beyond those for which it was originally designed. [ABSTRACT FROM AUTHOR]

Purpose: To investigate community-acquired pressure injuries (CAPIs) in older people by utilizing big data. Design: Retrospective data curation and analysis of inpatient data from two general medical centers between 1 January 2016 and 31 December 2018. Methods: Nursing assessments from 44,449 electronic medical records of patients admitted to internal medicine departments were retrieved, organized, coded by data engineers, and analyzed by data scientists. Potential explanatory patient characteristics tested were gender, age, admission indices, nursing assessments including CAPIs, CAPI type and location, vital signs, and the results of lab tests within the first 36 h of admission. Findings: Most CAPIs were located in the buttocks (56.9%), followed by the sacrum (11.8%), ankle (10.8%), trochanter (5.1%), and leg (3.9%). Tissue associated with CAPIs was described as necrotic, serotic, bloody, granolithic, epithelial, and infected. There were 31% of first-degree CAPIs, 41% second-degree, and 18% third-degree. Previously unacknowledged patient characteristics associated with CAPIs are as follows: age, oxygen use, intestinal function, the touch senses of heat and pain, albumin, RDW (red cell distribution width), and systolic blood pressure. Conclusions: The novel indicators for CAPIs underscore the importance of data-driven approaches in detecting and preventing CAPIs in community care. These markers can detect and prevent pressure ulcers in the community, particularly among the elderly. Relevance for Clinical Practice: Nursing management is called upon to integrate information about novel patient characteristics associated with CAPI into clinical practice. Assimilating the insights from this hospital nursing-led study into community nursing will enhance the safety and quality of care for the elderly. [ABSTRACT FROM AUTHOR]

Objectives There is rapidly growing interest in learning health systems (LHSs) nationally and globally. While the critical role of informatics is recognized, the informatics community has been relatively slow to formalize LHS as a priority area. Materials and Methods We compiled results from a short survey of LHS leaders and American Medical Informatics Association (AMIA) members, discussion from an LHS reception at the AMIA annual meeting, and a follow-up survey to inform priorities at the intersection of LHS and informatics. Results We present opportunities between informatics and LHS which fell into themes of: Understanding and Context, Shared Resources, Collaboration, Education, Data, Evaluation, and Patient Centeredness. Immediate LHS informatics priorities identified include establishing informatics LHS forum(s), case reports of LHS informatics successes and failures, LHS informatics education resources, and improved understanding of LHS principles in informatics. Conclusion Increased informatics and LHS alignment is critical for advancing this transformative national priority. [ABSTRACT FROM AUTHOR]

Objectives To examine changes in technology-related errors (TREs), their manifestations and underlying mechanisms at 3 time points after the implementation of computerized provider order entry (CPOE) in an electronic health record; and evaluate the clinical decision support (CDS) available to mitigate the TREs at 5-years post-CPOE. Materials and Methods Prescribing errors (n  = 1315) of moderate, major, or serious potential harm identified through review of 35 322 orders at 3 time points (immediately, 1-year, and 4-years post-CPOE) were assessed to identify TREs at a tertiary pediatric hospital. TREs were coded using the Technology-Related Error Mechanism classification. TRE rates, percentage of prescribing errors that were TREs, and mechanism rates were compared over time. Each TRE was tested in the CPOE 5-years post-implementation to assess the availability of CDS to mitigate the error. Results TREs accounted for 32.5% (n  = 428) of prescribing errors; an adjusted rate of 1.49 TREs/100 orders (95% confidence interval [CI]: 1.06, 1.92). At 1-year post-CPOE, the rate of TREs was 40% lower than immediately post (incident rate ratio [IRR]: 0.60; 95% CI: 0.41, 0.89). However, at 4-years post, the TRE rate was not significantly different to baseline (IRR: 0.80; 95% CI: 0.59, 1.08). "New workflows required by the CPOE" was the most frequent TRE mechanism at all time points. CDS was available to mitigate 32.7% of TREs. Discussion In a pediatric setting, TREs persisted 4-years post-CPOE with no difference in the rate compared to immediately post-CPOE. Conclusion Greater attention is required to address TREs to enhance the safety benefits of systems. [ABSTRACT FROM AUTHOR]

The promise of artificial intelligence has generated enthusiasm among patients, health care professionals, and technology developers who seek to leverage its potential to enhance the diagnosis and management of an increasing number of chronic and acute conditions. Point-of-care testing increases access to care because it enables care outside of traditional medical settings. Collaboration among developers, clinicians, and end users is an effective best practice for solving clinical problems. A common set of clearly defined terms that are easily understood by research teams is a valuable tool that fosters these collaborations. [ABSTRACT FROM AUTHOR]

Creating software tools that address the needs of a wide range of decision-makers requires the inclusion of differing perspectives throughout the development process. Software tools for biodiversity conservation often fall short in this regard, partly because broad decision-maker needs may exceed the toolkits of single research groups or even institutions. We show that participatory, collaborative codesign enhances the utility of software tools for better decision-making in biodiversity conservation planning, as demonstrated by our experiences developing a set of integrated tools in Colombia. Specifically, we undertook an interdisciplinary, multi-institutional collaboration of ecological modelers, software engineers, and a diverse profile of potential end users, including decision-makers, conservation practitioners, and biodiversity experts. We leveraged and modified common paradigms of software production, including codesign and agile development, to facilitate collaboration through all stages (including conceptualization, development, testing, and feedback) to ensure the accessibility and applicability of the new tools to inform decision-making for biodiversity conservation planning. [ABSTRACT FROM AUTHOR]

When medication administration record (MAR) "hold" capability is enabled in the electronic health record (EHR) during patient transfers, medication doses appear as "held" rather than due. We sought to quantify the incidence of delayed and missed doses of critical medications during MAR hold periods and to implement and evaluate interdisciplinary efforts and technical interventions to reduce missed medication doses during these periods. A list of critical medications was identified. MAR data were collected in patients with at least 1 critical medication dose due during the MAR hold period. MAR times were used to determine if delayed doses or missed doses occurred. Our interventions included: (1) implementation of a patient list indicator to retrospectively identify recently "held" medication doses, and (2) a report for operating room pharmacists to prospectively identify upcoming doses and ensure they were administered on time. Pre- and post-intervention period data were compared using a chi-squared test. During the pre-intervention study period, there were 1044 instances of delayed or missed doses during MAR hold. Most MAR times evaluated were on MAR hold during perioperative patient transfers. Delayed, missed, and multiple missed doses were defined in accordance with internal medication administration policies. There was no significant difference in the incidence of delayed and missed doses (69% vs 66%, P =.31), however, there was a significant reduction in the number of critical medication doses missed multiple times (0.8% vs 6.7%, P <.001) and all missed doses (35% vs 42%, P =.05) between the pre- and post-intervention period. As demonstrated across in both the pre- and post-intervention period of our study, MAR hold is commonly associated with dose delays and missed doses, which has potential negative consequences on patient outcomes. Future considerations will include implementation of a best practice alert (BPA) that directs users to a MAR tab highlighting doses held during transfers. [ABSTRACT FROM AUTHOR]

Information Science has become a pivotal force driving progress in science and technology. In today's era, Information and Communication Technology (ICT) plays a significant role in enhancing organizational efficiency and enabling individual growth. By facilitating seamless communication and data management, ICT allows businesses to optimize their relationships with clients, suppliers, and distributors. Moreover, ICT has transformed education, empowering learners to access resources globally and enabling personalized learning experiences. Informatics, as an interdisciplinary field, encompasses computer science, information systems, and data analytics, all of which contribute to solving complex challenges. This paper explores the diverse applications of Informatics and its role in shaping a technologically advanced society. Through its integration across various sectors, Informatics not only drives innovation but also bridges the gap between technological potential and practical implementation. [ABSTRACT FROM AUTHOR]

Objective We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench. Materials and Methods 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process. Results The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate. Discussion ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources. Conclusion Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions. [ABSTRACT FROM AUTHOR]

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user‐friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data‐sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science. [ABSTRACT FROM AUTHOR]

The first books in Polish about the Pascal programming language appeared in the late 1970s, and were soon followed by a Polish translation of Niklaus Wirth's book Algorithms + Data Structures = Programs. At that time, many efforts were made to prepare teachers to teach informatics in schools, and Pascal was one of the topics taught, often with no access to computers. The situation improved significantly after the first informatics curriculum was approved for schools by the Ministry of National Education (MEN) in 1985, in response to the increasing number of microcomputers in schools and students' interest in programming. As a confirmation of the conclusions drawn from the discussion in the ABACUS magazine (1984) on "Which is the best programming language for a first course in computer science," Pascal very quickly became the basic programming language in Polish schools as a tool for practicing algorithmics. The first informatics textbook was published in 1988 and in it, after a short Logo course, Pascal took the main place as a language for developing solutions to algorithmic problems in the form of computer programs. In the following years, many teaching aids were developed for programming education in schools, as well as other aids, e.g., an authoring system, programmed in Pascal. Pascal was the primary programming language in informatics education until the end of the first decade of the 21st century, subsequently replaced by Python. It is interesting that Polish winners of national and international Olympiads in Informatics for many years used Pascal, demonstrating the enormous professional values of this language. In this article we present in detail, among others things: the role and influence of Pascal on the development and level of informatics education in schools in Poland, as well as other scientific activities and initiatives supported by this language. Many students educated in this technical and mental environment found their place in professional development teams in the best computer and technology corporations, and evidently most of them joined the national working force. [ABSTRACT FROM AUTHOR]

The work of Niklaus Wirth, designer of the Pascal programming language, has led to the introduction of programming in schools in many countries often leading to a transformation in the way of thinking. In this article, we provide a retrospective analysis of the Lithuanian experience driven by Pascal and discuss the main ideas about teaching programming originating from this experience. We conducted a qualitative study by means of interviews with experts involved in the development of programming education during its early phases to examine their memories and perspectives. Programming education in Lithuania started with the Pascal-inspired Young Programmers' School by Correspondence, founded in 1981, which had a great influence on the Lithuanian programming elite. For this purpose, a compiler for a subset of Pascal was developed for students taking their first steps towards programming, or more precisely, algorithmic thinking. Many innovations were developed and brought into practice. The ones that have proved their worth and have not lost their relevance are the subject of this article. These include assessing program text readability, cultivating programming style, program reading tasks, creative thinking tasks, problem-solving approaches, detailed compiler error messages, automatic error fixing, and compiler advice to novice programmers. While some concepts became obsolete with technological advancements, others remain relevant, directly or as inspiration for new ideas, forming the basis of this study. [ABSTRACT FROM AUTHOR]

Background: Digital health technology is progressively transforming physiotherapy practice. Despite a maturing body of literature relating to physiotherapy digital health capability, research examining digital health physiotherapy competency standards is both lacking and lagging. Objective: Examine international professional practice competency standards for physiotherapists to identify themes common to digital health practice competency, published by international peak organizations governing physiotherapy practice. Methods: Systematic meta-synthesis of international peak organization physiotherapy practice competency standards. The study was undertaken over nine stages. Competency statements related to digital health were extracted, and further coded into resultant themes. Results: Eleven documents were analyzed. Fifty-two statements explicitly referenced digital health competency. Identified themes were as follows: 1) digital health data governance; 2) digital health data translation; and 3) digital health technologies. Where digital health-related competency statements do exist, they are skewed toward health information management activities. Conclusions: Digital health practice is currently under-represented in competency standards for physiotherapists. Workforce advancement in light of the burgeoning impact of digital health will prompt further updates to professional competency standards set by our peak organizations. This will have a flow on effect, whereby education providers (e.g. universities and other professional development providers) should consider curriculum and training that prepares individuals for digitally enabled practice. [ABSTRACT FROM AUTHOR]

Many studies have reported the presence of tertiary lymphoid structures (TLSs) in cancer, but the research progress of TLSs in cancer has not been systematically analyzed. Therefore, we analyzed the global scientific knowledge in the field using informatics methods. The results showed that TLSs in cancer have received increasing attention since the 21st century, with an annual publication growth rate of 27.86%. Unsupervised hierarchical clustering based on machine learning further categorized the research features into four clusters, with the cluster related to immunotherapy being considered an emerging cluster. TLSs and immunotherapy were identified as the top two hotspots with the highest occurrence frequency and total link strength. The Walktrap algorithm indicated that "TLSs, carcinoma, prognostic value" and "high endothelial venules, germinal-centers, node-like structures" are important to TLSs but remain underexplored, representing promising research directions. These findings suggest that cancer-related TLSs have brought new insights into antitumor immunity, and targeting TLSs has the potential to transform the landscape of antitumor immunotherapy. [ABSTRACT FROM AUTHOR]

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The article in this journal will bring readers more specifically to university students to see easier access to money for additional living expenses. A new perspective when students are more specifically informatics students who are taught and nurtured so that they have skills and knowledge about program development not only live with money given by parents, but with money from the creation and development of their own programs or platforms. Access and dissemination of this journal will be expanded to make it more accessible and useful to all readers not only for informatics students but for prospective students and former students who have dived into the sea of computer code more specifically on the creation and development of programs or platforms. [ABSTRACT FROM AUTHOR]

Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes.We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype‐environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD‐seq, ddRAD‐seq, whole genome sequencing data) representing environmental variation across the species geographic range.To demonstrate multi‐species applicability, we apply our toolbox to three georeferenced genomic datasets for co‐occurring East African spiny reed frogs (Afrixalus fornasini, A. delicatus and A. sylvaticus) to predict their population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced from a previous study using data for two European bat species (Myotis escalerai and M. crypticus).Our framework sets the stage for large scale, multi‐species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity and landscape barriers. [ABSTRACT FROM AUTHOR]

Background: There have been many efforts to expand existing data collection initiatives to include COVID-19 related data. One program that expanded is UK Biobank, a large-scale research and biomedical data collection resource that added several COVID-19 related data fields including questionnaires (exposures and symptoms), viral testing, and serological data. This study aimed to analyze this COVID-19 data to understand how COVID-19 data was collected and how it can be used to attribute COVID-19 and analyze differences in cohorts and time periods. Methods: A cohort of COVID-19 infected individuals was defined from the UK Biobank population using viral testing, diagnosis, and self-reported data. Changes over time, from March 2020 to October 2021, in total case counts and changes in case counts by identification source (diagnosis from EHR, measurement from viral testing and self-reported from questionnaire) were also analyzed. For the questionnaires, an analysis of the structure and dynamics of the questionnaires was done which included the amount and type of questions asked, how often and how many individuals answered the questions and what responses were given. In addition, the amount of individuals who provided responses regarding different time segments covered by the questionnaire was calculated along with how often responses changed. The analysis included changes in population level responses over time. The analyses were repeated for COVID and non-COVID individuals and compared responses. Results: There were 62 042 distinct participants who had COVID-19, with 49 120 identified through diagnosis, 30 553 identified through viral testing and 934 identified through self-reporting, with many identified in multiple methods. This included vast changes in overall cases and distribution of case data source over time. 6 899 of 9 952 participants completing the exposure questionnaire responded regarding every time period covered by the questionnaire including large changes in response over time. The most common change came for employment situation, which was changed by 74.78% of individuals from the first to last time of asking. On a population level, there were changes as face mask usage increased each successive time period. There were decreases in nearly every COVID-19 symptom from the first to the second questionnaire. When comparing COVID to non-COVID participants, COVID participants were more commonly keyworkers (COVID: 33.76%, non-COVID: 15.00%) and more often lived with young people attending school (61.70%, 45.32%). Conclusion: To develop a robust cohort of COVID-19 participants from the UK Biobank population, multiple types of data were needed. The differences based on time and exposures show the important of comprehensive data capture and the utility of COVID-19 related questionnaire data. [ABSTRACT FROM AUTHOR]

At a time when diverse forms of automated visual feedback systems on speech are introduced to the Human–Computer Interaction (HCI) community, our research investigates the possible design spaces and design implications to consider when creating them. We utilised a Wizard of Oz prototype that recognises fillers during speech and sends out real-time feedback in the form of a flickering screen and a post-report with graphic charts. We let 27 participants use this prototype and interviewed them about their general experience of using such systems. Based on thematic analysis, we propose eight design implications to consider when designing an automated visual feedback system on speech. Additionally, we discuss the possible future for automated visual feedback systems on speech where they collaborate with fields such as personal informatics (quantified self), self-determination theory (SDT) and motivation, and other socio-ethnic areas [ABSTRACT FROM AUTHOR]

Background: The interfacility transfer places the patient at greater risk for poor outcomes due to outdated, inaccurate, or miscommunication of patient information at the time of transfer. Rural patients are at greater risk for poor outcomes due to experiencing a higher rate of transfers because of health care inequities related to limited access to specialty and critical care services. This paper systematically reviewed the literature to describe the current state of interfacility transfer communication practices and methods in the United States. Methods: The review followed the 2020 Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines and checklist. PubMed, CINAHL, and Scopus were searched using MeSH terms and keywords. Inclusion criteria: peer-reviewed research articles published in English from 2013 to 2022 in the United States and included both adult and pediatric patient transfers. It was noted that there is a lack of research on nursing-based interfacility transfer communication practices and methods. Results: A total of 763 articles were reviewed, and 24 met eligibility for inclusion. The following coded themes were identified in the research literature: transfer patient characteristics and geographic barriers, communication challenges, transfer process, interoperability, digital intervention, and standardized transfer tools. Conclusion: The development and implementation of an integrated standardized interfacility transfer communication tool are warranted to decrease miscommunication and improve patient outcomes. The integration of technologies such as telehealth, the use of health information exchanges, and improved interoperability between health systems can improve communication and outcomes for all transfer patients but specifically rural transfers. Additionally, health care workers, particularly those in rural areas, need adequate infrastructure and financial resources to achieve positive patient outcomes. [ABSTRACT FROM AUTHOR]

Transient Global Amnesia (TGA) is a neurological disorder characterized by the sudden, transitory development of amnesia. It is an amnestic episode not linked to a common neurological cause, such as a seizure or stroke. During the attack, the patient can forget recent events or fail to form new memories. This study aimed to evaluate the viability of a smart location system that can be helpful to TGA patients during the episodes. It also measures the engagement of the system in uncontrolled environments versus controlled environments, and also user time interacting with the application, frequency of errors, and satisfaction level. A hybrid research approach was applied to collect both qualitative and quantitative data on response time, geographical location, and physiological indications. The findings indicate that response time to alerts in real settings is faster than that achieved in simulated settings. Suburban areas showed higher location accuracy, while TGA patients engaged with stimuli had a mean of 15.3 seconds. Error rates were reduced from 12% to 5% over four weeks. The study therefore, advocates for the use of mobile applications and systems to improve TGA management, reducing anxiety, and enhancing coping styles. [ABSTRACT FROM AUTHOR]

We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. [ABSTRACT FROM AUTHOR]

Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. [ABSTRACT FROM AUTHOR]

Objective: To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post‐9/11 Veterans' VA enrollment and retention. Data Sources and Study Setting: Interviews conducted from March–May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status. Study Design: Interview questions were developed using Petersen et al.'s Factors Influencing Choice of Healthcare System framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process. Data Collection/Extraction Methods: Two coders analyzed 22% (N = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews. Principal Findings: Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high‐quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems. Conclusions: Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention. [ABSTRACT FROM AUTHOR]

Background: Rural populations experience ongoing health inequities with disproportionately high morbidity and mortality rates, but digital health in rural settings is poorly studied. Our research question was: How does digital health influence healthcare outcomes in rural settings? The objective was to identify how digital health capability enables the delivery of outcomes in rural settings according to the quadruple aims of healthcare: population health, patient experience, healthcare costs and provider experience. Methods: A multi-site qualitative case study was conducted with interviews and focus groups performed with healthcare staff (n = 93) employed in rural healthcare systems (n = 10) in the state of Queensland, Australia. An evidence-based digital health capability framework and the quadruple aims of healthcare served as classification frameworks for deductive analysis. Theoretical analysis identified the interrelationships among the capability dimensions, and relationships between the capability dimensions and healthcare outcomes. Results: Seven highly interrelated digital health capability dimensions were identified from the interviews: governance and management; information technology capability; people, skills, and behaviours; interoperability; strategy; data analytics; consumer centred care. Outcomes were directly influenced by all dimensions except strategy. The interrelationship analysis demonstrated the influence of strategy on all digital health capability dimensions apart from data analytics, where the outcomes of data analytics shaped ongoing strategic efforts. Conclusions: The study indicates the need to coordinate improvement efforts targeted across the dimensions of digital capability, optimise data analytics in rural settings to further support strategic decision making, and consider how consumer-centred care could influence digital health capability in rural healthcare services. Digital transformation in rural healthcare settings is likely to contribute to the achievement of the quadruple aims of healthcare if transformation efforts are supported by a clear, resourced digital strategy that is fit-for-purpose to the nuances of rural healthcare delivery. [ABSTRACT FROM AUTHOR]

Background: There is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of large language models (LLMs) like ChatGPT can process image and video input in addition to text. They are thus theoretically capable of providing impressions of input radiological images. Objective: To assess the ability of multimodal LLMs to interpret pediatric radiological images. Materials and methods: Thirty medically significant cases were collected and submitted to GPT-4 (OpenAI, San Francisco, CA), Gemini 1.5 Pro (Google, Mountain View, CA), and Claude 3 Opus (Anthropic, San Francisco, CA) with a short history for a total of 90 images. AI responses were recorded and independently assessed for accuracy by a resident and attending physician. 95% confidence intervals were determined using the adjusted Wald method. Results: Overall, the models correctly diagnosed 27.8% (25/90) of images (95% CI=19.5–37.8%), were partially correct for 13.3% (12/90) of images (95% CI=2.7–26.4%), and were incorrect for 58.9% (53/90) of images (95% CI=48.6–68.5%). Conclusion: Multimodal LLMs are not yet capable of interpreting pediatric radiological images. [ABSTRACT FROM AUTHOR]

Since the implementation of the HITECH Act 2009, the integration of the electronic health record (EHR) with other technology platforms has increased the complexity and necessity of technology downtimes, and the continuity of patient care has become increasingly dependent on an intact EHR. To maintain business continuity and safe patient care during planned or unplanned EHR downtime, it is imperative that organisations have solid downtime and disaster recovery plans. Successful downtime planning will include documenting, with annual reviews, the process for patient care during downtime, as well as an exercise programme that touches all aspects of the downtime process. This paper discusses the experience of a healthcare system based in the US Midwest, which has chosen to exercise part of that process on a quarterly basis, prior to scheduled EHR upgrades. Over the past year of exercises, this healthcare system has collected various data elements in order to identify the education needed and the fine-tuning of the exercise design required to ensure staff competency and patient safety during EHR downtime. The paper describes the process, outcome and the steps the organisation is taking to improve the outcomes of future EHR downtimes. [ABSTRACT FROM AUTHOR]

Effective recruitment strategies are pivotal for informatics-based intervention trials success, particularly for people living with HIV (PLWH), where engagement can be challenging. Although informatics interventions are recognized for improving health outcomes, the effectiveness of their recruitment strategies remains unclear. We investigated the application of a social marketing framework in navigating the nuances of recruitment for informatics-based intervention trials for PLWH by examining participant experiences and perceptions. We used qualitative descriptive methodology to conduct semi-structured interviews with 90 research participants from four informatics-based intervention trials. Directed inductive and deductive content analyses were guided by Howcutt et al.'s social marketing framework on applying the decision-making process to research recruitment. The majority were male (86.7%), living in the Northeast United States (56%), and identified as Black (32%) or White (32%). Most participants (60%) completed the interview remotely. Sixteen subthemes emerged from five themes: motivation, perception, attitude formation, integration, and learning. Findings from our interview data suggest that concepts from Howcutt et al.'s framework informed participants' decisions to participate in an informatics-based intervention trial. We found that the participants' perceptions of trust in the research process were integral to the participants across the four trials. However, the recruitment approach and communication medium preferences varied between older and younger age groups. Social marketing framework can provide insight into improving the research recruitment process. Future work should delve into the complex interplay between the type of informatics-based interventions, trust in the research process, and communication preferences, and how these factors collectively influence participants' willingness to engage. [ABSTRACT FROM AUTHOR]

With artificial intelligence (AI) rapidly advancing, advanced practice nurses must understand and use it responsibly. Here, we describe an assignment in which Doctor of Nursing Practice (DNP) students learned to use generative text AI. Using our program and course outcomes, developed from the 2021 American Association of Colleges of Nursing (AACN) Essentials competency for DNP students to learn and use AI, we reviewed the literature seeking examples using ChatGPT for the DNP informatics course. No published examples existed to guide us toward infusing a ChatGPT assignment into the course. We developed a novel assignment that included a guide for students on how to use ChatGPT. Students were given time before the assignment to learn the AI/chatbot technology. They were then given the assignment and grading rubric. The assignment was to develop a tool for their current or future practice using ChatGPT. During the course faculty debrief, we learned that few students had questions and the assignment was clear. We also learned that students who sought to develop straightforward, uncomplicated patient tools succeeded with the technology. Those who sought to create something for complex patients had more challenges. Nursing education and practice will be influenced by the increasing prevalence of AI. This manuscript outlines an AI-based assignment for graduate nursing education intended for the students to become familiar with current AI and best practices for patient care. The assignment was well received by students. We plan to use it again in the next course offering. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Elementary Education / Revija za Elementarno Izobraževanje is the property of University of Maribor, Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Sepsis is one of the main causes of mortality in intensive care units (ICUs). Early prediction is critical for reducing injury. As approximately 36% of sepsis occur within 24 h after emergency department (ED) admission in Medical Information Mart for Intensive Care (MIMIC-IV), a prediction system for the ED triage stage would be helpful. Previous methods such as the quick Sequential Organ Failure Assessment (qSOFA) are more suitable for screening than for prediction in the ED, and we aimed to find a light-weight, convenient prediction method through machine learning. METHODS: We accessed the MIMIC-IV for sepsis patient data in the EDs. Our dataset comprised demographic information, vital signs, and synthetic features. Extreme Gradient Boosting (XGBoost) was used to predict the risk of developing sepsis within 24 h after ED admission. Additionally, SHapley Additive exPlanations (SHAP) was employed to provide a comprehensive interpretation of the model's results. Ten percent of the patients were randomly selected as the testing set, while the remaining patients were used for training with 10-fold cross-validation. RESULTS: For 10-fold cross-validation on 14,957 samples, we reached an accuracy of 84.1%±0.3% and an area under the receiver operating characteristic (ROC) curve of 0.92±0.02. The model achieved similar performance on the testing set of 1,662 patients. SHAP values showed that the five most important features were acuity, arrival transportation, age, shock index, and respiratory rate. CONCLUSION: Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage. This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance. [ABSTRACT FROM AUTHOR]

The article discusses a pilot study conducted at an academic dermatology center to improve clinical documentation and patient education for atopic dermatitis (AD) using an electronic medical record (EMR) system. The study implemented an AD yardstick SmartSet in the EMR to assess disease severity and provide patient education materials. Results showed increased documentation of disease severity and higher rates of educational material distribution in the yardstick group compared to the control group. The study highlights the potential of EMR technology to enhance disease management and patient education in dermatology. [Extracted from the article]

Background: Electronic health records and other clinical information systems have crucial roles in health service delivery and are often utilised for patient care as well as health promotion and research. Government agencies and healthcare bodies are gradually shifting the focus on how these data systems can be harnessed for secondary uses such as reflective practice, professional learning and continuing professional development. Whilst there has been a presence in research around the attitudes of health professionals in employing clinical information systems to support their reflective practice, there has been very little research into consumer attitudes towards these data systems and how they would like to interact with such structures. The study described in this article aimed to address this gap in the literature by exploring community perspectives on the secondary use of Electronic Health Data for health professional learning and practice reflection. Methods: A qualitative methodology was used, with data being collected via semi-structured interviews. Interviews were conducted via phone and audio recordings, before being transcribed into text for analysis. Reflective thematic analysis was undertaken to analyse the data. Results: Fifteen Australians consented to participate in an interview. Analysis of interview data generated five themes: (1) Knowledge about health professional registration and professional learning; (2) Secondary uses of Electronic Health Data; (3) Factors that enable the use of Electronic Health Data for health professional learning; (4) Challenges using Electronic Health Data for health professional learning and (5) Expectations around consent to use Electronic Health Data for health professional learning. Conclusions: Australians are generally supportive of health professionals using Electronic Health Data to support reflective practice and learning but identify several challenges for data being used in this way. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Interdisciplinary Educational Research / Disiplinlerarası Eğitim Araştırmaları Dergisi is the property of Interdisciplinary Education & Research Association / Disiplinlerarasi Egitim ve Arastirma Dernegi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Individuals with high risk for lung cancer may benefit from lung cancer screening, but there are associated risks as well as benefits. Shared decision‐making (SDM) tools with personalized information may provide key support for patients. Understanding patient perspectives on educational tools to facilitate SDM for lung cancer screening may support tool development. Aim: This study aimed to explore patient perspectives related to a SDM tool for lung cancer screening using a qualitative approach. Methods: We elicited patient perspectives by showing a provider‐facing SDM tool. Focus group interviews that ranged in duration from 1.5 to 2 h were conducted with 23 individuals with high risk for lung cancer. Data were interpreted inductively using thematic analysis to identify patients' thoughts on and desires for a patient‐facing SDM tool. Results: The findings highlight that patients would like to have educational information related to lung cancer screening. We identified several key themes to be considered in the future development of patient‐facing tools: barriers to acceptance, preference against screening and seeking empowerment. One further theme illustrated effects of patient–provider relationship as a limitation to meeting lung cancer screening information needs. Participants also noted several suggestions for the design of technology decision aids. Conclusion: These findings suggest that patients desire additional information on lung cancer screening in advance of clinical visits. However, there are several issues that must be considered in the design and development of technology to meet the information needs of patients for lung cancer screening decisions. Patient or Public Contribution: Patients, service users, caregivers or members of the public were not involved in the study design, conduct, analysis or interpretation of the data. However, clinical experts in health communication provided detailed feedback on the study protocol, including the focus group approach. The study findings contribute to a better understanding of patient expectations for lung cancer screening decisions and may inform future development of tools for SDM. [ABSTRACT FROM AUTHOR]

This article discusses a study that examined the relationship between discoid lupus erythematosus (DLE) and various demographic and health factors. The researchers analyzed data from the All of Us Research Program and found that DLE patients had higher odds of having concurrent diagnoses in various domains, including cardiovascular, autoimmune, endocrine, renal, psychiatric, and dermatologic. The study also found that LGBTQ individuals had a lower prevalence of DLE, suggesting potential social or healthcare barriers. Lower income levels and higher prevalence of insurance were also associated with DLE. The study emphasizes the need for further research to explore these disparities and understand the impact of stress and access to healthcare services. [Extracted from the article]

Background: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti‐TB drugs are generally curative. Therefore, TB‐case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it. Methods: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks. Results: Our ML‐based CT analysis (TB‐Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB‐Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB‐lesion volumes, and disease‐signs during disease pathogenesis. Conclusion: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Digital health plays a vital role in healthcare services. Governments in many countries, including China, are increasingly advocating for the appropriate use of digital technologies to address significant health system challenges. It is crucial to incorporate digital health education into the curriculum for future nurses to adapt to the changes in the digital medical system. This study aimed to evaluate the impact of an online Digital Health and Informatics Course in China on the knowledge and comprehension of key digital health and informatics topics, self-assessment of nursing informatics competencies, and satisfaction among undergraduate nursing students. The findings of this study provide recommendations for the design and implementation of future digital health education. Methods: This study employed a one-group, quasi-experimental mixed-methods design with pre- and post-assessments. The participants received digital health and informatics education through six three-hour online sessions in six interactive days, with online self-learning materials in between. An online quiz and focus group discussions pre- and post the course were designed to evaluate the knowledge and comprehension of key digital health and informatics topics. Also, a validated Chinese version of the Self-assessment of Nursing Informatics Competencies Scale was conducted pre- and post-course to assess self-assessment of nursing informatics competencies. Additionally, all students were invited to participate in an online survey with a performance-focused course evaluation form as well as focus group discussions to gather their feedback on the learning experience and their evaluations of the course. Results: A total of 24 undergraduate nursing students were enrolled in the course. All students completed all sessions of this course, resulting in an attendance rate of 100%. Additionally, all students completed both pre- and post-assessments. In terms of the knowledge and comprehension of key digital health and informatics topics, scores of the quiz on knowledge assessment improved from the pre-test [mean pretest score: 78.33 (SD 6.005)] to the post-test [mean post-test score: 83.17 (SD 4.86)] upon completion of the course (P < 0.001). Also, students acknowledged that the course enhanced their knowledge and comprehension of informatics and digital health, the benefits of (nursing) informatics in clinical practice, and the role of health care professionals in informatics and digital health. In terms of self-assessment of nursing informatics competencies, scores on nursing informatics attitudes demonstrated significant improvement (P < 0.001). Furthermore, students reported high satisfaction with various aspects of this course, including the opportunity to explore broad horizons in informatics for future careers, engaging in group discussions, and analyzing case studies on the use of informatics and digital health in clinical practice. Conclusions: This Online Digital Health and Informatics education effectively improved undergraduate nursing students' knowledge and comprehension of the key digital health and informatics topics, nursing informatics attitudes in the self-assessment of nursing informatics competency with high levels of satisfaction. In order to ensure that future education in digital health and informatics for nursing students is in line with the technological advancements in clinical settings, it is necessary to foster collaboration between medical school training and clinical practice. This collaboration should involve the use of clinical examples to illustrate advanced digital health applications and the inclusion of practical exercises on the use of digital health technology in clinical settings. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of modern enterprises, the integration of informatics with organizational design and marketing strategies has emerged as a pivotal approach to enhance economic potential and sales effectiveness. This article explores the synergistic relationship between these components, emphasizing how data-driven decision-making, agile organizational structures, and targeted marketing strategies can collectively optimize business performance. Through a comprehensive literature review, the key theories and frameworks that underpin this integration, followed by a detailed methodology that outlines the research design and data collection processes were identified. The results highlight the positive impact of integrated informatics on sales performance and organizational efficiency, supported by case studies from various industries. The discussion delves into the implications of these findings, addressing challenges such as data privacy, change management, and skill gaps. The study provides actionable recommendations for practitioners and outline future research directions to further explore this critical intersection of informatics, organizational design, and marketing strategies. This study contributes to the understanding of how modern enterprises can leverage technology and strategic alignment to thrive in a competitive marketplace. [ABSTRACT FROM AUTHOR]

Children with pulmonary hypertension (PH) often demonstrate limited exercise capacity. Data support exercise as an effective nonpharmacologic intervention among adults with PH. However, data on exercise training in children and adolescents are limited, and characteristics of the optimal exercise program in pediatric PH have not been identified. Exercise programs may have multiple targets, including muscle deficits which are associated with exercise limitations in both adult and pediatric PH. Wearable accelerometer sensors measure physical activity volume and intensity in the naturalistic setting and can facilitate near continuous data transfer and bidirectional communication between patients and the study team when paired with informatics tools during exercise interventions. To address the knowledge gaps in exercise training in pediatric PH, we designed a prospective, single arm, nonrandomized pilot study to determine feasibility and preliminary estimates of efficacy of a 16‐week home exercise intervention, targeting lower extremity muscle mass and enriched by wearable mobile health technology. The exercIse Training in pulmONary hypertEnsion (iTONE) trial includes (1) semistructured exercise prescriptions tailored to the participant's baseline level of activity and access to resources; (2) interval goal setting fostering self‐efficacy; (3) real time monitoring of activity via wearable devices; (4) a digital platform enabling communication and feedback between participant and study team; (5) multiple avenues to assess participant safety. This pilot intervention will provide information on the digital infrastructure needed to conduct home‐based exercise interventions in PH and will generate important preliminary data on the effect of exercise interventions in youth with chronic cardiorespiratory conditions to power larger studies in the future. [ABSTRACT FROM AUTHOR]

Copyright of Syntax Idea is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista EIA is the property of Escuela de Ingenieria de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista EIA is the property of Escuela de Ingenieria de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Anaesthesia / Journal Canadien d'Anesthésie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: While criteria for the diagnosis of nosocomial pneumonias exist, objective definitions are a challenge and there is no gold standard for diagnosis. We analyzed the impact of the implementation of a logical, consensus-based diagnostic and treatment protocol for managing nosocomial pneumonias in the cardiovascular surgery intensive care unit (CVS-ICU). Methods: We conducted a quasi-experimental, interrupted time series analysis to evaluate the impact of a diagnostic and treatment protocol for nosocomial pneumonias in the CVS-ICU. Impacts were measured relative to patient outcomes, diagnostic processes, and antimicrobial stewardship improvement. Descriptive statistics were used to analyze results. Results: Overall, 35 pre-protocol and 39 post-protocol patients were included. Primary clinical variables suggesting pneumonia in pre- and post-protocol patients were new lung consolidation (50% vs. 71%), new leukocytosis (59% vs. 64%), and positive culture (32% vs. 55%). Appropriate diagnostic testing improved (23% vs. 54%, p = 0.008) after protocol implementation. The proportion of patients meeting the criteria for nosocomial pneumonia (77% vs. 87%) was not statistically significant, though more patients in the post-protocol group met probable diagnostic criteria (51% vs. 77%). Duration of therapy was not significantly different (6 days [IQR = 5.0, 10.0] vs. 7 days [IQR = 6.0, 9.0]). Conclusions: The implementation of a diagnostic and treatment protocol for management of nosocomial pneumonias in the CVS-ICU resulted in improved diagnostic accuracy, advanced antimicrobial and diagnostic stewardship efforts, and laboratory cost savings without an adverse impact on patient-centered outcomes. [ABSTRACT FROM AUTHOR]

Background Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear. Methods We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines. Results From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%–100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%–100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms. Conclusions Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported. [ABSTRACT FROM AUTHOR]