Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms.

Large language models and synthetic health data: progress and prospects.

Objectives: Given substantial obstacles surrounding health data acquisition, high-quality synthetic health data are needed to meet a growing demand for the application of advanced analytics for clinical discovery, prediction, and operational excellence. We highlight how recent advances in large language models (LLMs) present new opportunities for progress, as well as new risks, in synthetic health data generation (SHDG).

Materials And Methods: We synthesized systematic scoping reviews in the SHDG domain, recent LLM methods for SHDG, and papers investigating the capabilities and limits of LLMs.

A partial loss-of-function variant in STAT6 protects against type 2 asthma.

Background: Signal transducer and activator of transcription 6 (STAT6) is central to type 2 (T2) inflammation, and common noncoding variants at the STAT6 locus associate with various T2 inflammatory traits, including diseases, and its pathway is widely targeted in asthma treatment.

Objective: We sought to test the association of a rare missense variant in STAT6, p.L406P, with T2 inflammatory traits, including the risk of asthma and allergic diseases, and to characterize its functional consequences in cell culture.

As good as it gets? A new approach to estimating possible prediction performance.

How much information does a dataset contain about an outcome of interest? To answer this question, estimates are generated for a given dataset, representing the minimum possible absolute prediction error for an outcome variable that any model could achieve. The estimate is produced using a constrained omniscient model that mandates only that identical observations receive identical predictions, and that observations which are very similar to each other receive predictions that are alike. It is demonstrated that the resulting prediction accuracy bounds function effectively on both simulated data and real-world datasets.

Health Care Usage During the COVID-19 Pandemic and the Adoption of Telemedicine: Retrospective Study of Chronic Disease Cohorts.

Background: The COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic.

The use of artificial intelligence for graduate nursing education: An educational evaluation.

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.

Body mass index is associated with pulmonary gas and blood distribution mismatch in COVID-19 acute respiratory failure. A physiological study.

Background: The effects of obesity on pulmonary gas and blood distribution in patients with acute respiratory failure remain unknown. Dual-energy computed tomography (DECT) is a X-ray-based method used to study regional distribution of gas and blood within the lung. We hypothesized that 1) regional gas/blood mismatch can be quantified by DECT; 2) obesity influences the global and regional distribution of pulmonary gas and blood; 3) regardless of ventilation modality (invasive vs.

Navigating the Global Landscape of Nursing Informatics Research Collaboration: A Blueprint for Success.

International collaboration is crucial in the field of nursing informatics research to enhance our ability to conduct globally relevant research that informs policy and practice. In this case study we describe how we have established an international research collaboration to evaluate nurses' experiences of technology use during the pandemic. We firstly describe how the collaboration was created and the successes associated with our work, before highlighting the facilitators to make an international collaboration work.

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls.

Start codon variant in LAG3 is associated with decreased LAG-3 expression and increased risk of autoimmune thyroid disease.

Autoimmune thyroid disease (AITD) is a common autoimmune disease. In a GWAS meta-analysis of 110,945 cases and 1,084,290 controls, 290 sequence variants at 225 loci are associated with AITD. Of these variants, 115 are previously unreported.

Cystic fibrosis in Iceland and the high prevalence of the N1303K variant.

Background: Cystic fibrosis (CF) is most common in populations of Northern European ancestry where the F508del variant predominates. In 2020, Iceland became a member of the European Cystic Fibrosis Society Patient Registry, and we launched an epidemiological study of CF in Iceland. The study aimed to determine the prevalence and the genetic variants present in the country.

[Anaesthesia for Emergent Caesarean Section: A Population-based Study on Icelandic and Migrant Women during 2007-2018].

Introduction: General anaesthesia for emergent caesarean section, though uncommon, is vital in expediting deliveries. Studies indicate higher complication risks among pregnant migrant women. This research investigates if migrant women in Iceland are more likely to undergo general anaesthesia for emergent caesarean section compared to their Icelandic counterparts.

[Perinatal outcomes of preterm births occuring to native-born and migrant mothers in Iceland].

Introduction: The aim of this study was to assess the incidence and perinatal outcomes of preterm births in Iceland during 1997-2018 and compare outcomes of Icelandic and migrant mothers.

Methods: The population in this historical population-based cohort study was all preterm (p<37 weeks gestation) live-born singletons born in Iceland from January 1, 1997 to December 31, 2018 and their mothers; a total of 3837 births. Data was obtained from the Icelandic Medical Birth Registry.

Estrogen receptor-positive breast cancer and adverse outcome in BRCA2 mutation carriers and young non-carrier patients.

Estrogen receptor-positive (ER+) breast cancer generally confers a more favorable prognosis than ER-negative cancer, however, a different picture is emerging for BRCA2 mutation carriers and young patients. We used nationwide data from population-based registries to study prognostic effects in those two groups. Of all 2817 eligible women diagnosed with breast cancer in Iceland during 1980-2004, 85% had been tested for the Icelandic 999del5 BRCA2 (c.

Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study.

Background: Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium.

Objective: The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system.

Eleven Years of Gender Data Visualization: A Step Towards More Inclusive Gender Representation.

We present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension of data collected from study or survey participants, passengers, or customers, as well as across academic studies, especially in certain disciplines like sociology. Our work contributes to multiple ongoing discussions on the ethical implications of data visualizations.

The relationship between electronic health records user interface features and data quality of patient clinical information: an integrative review.

Objectives: Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ).

Materials And Methods: We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022.

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium.

Background: Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review.

Purpose: The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.

Methods: APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction.

Examining Care Planning Efficiency and Clinical Decision Support Adoption in a System Tailoring to Nurses' Graph Literacy: National, Web-Based Randomized Controlled Trial.

Background: The proliferation of health care data in electronic health records (EHRs) is fueling the need for clinical decision support (CDS) that ensures accuracy and reduces cognitive processing and documentation burden. The CDS format can play a key role in achieving the desired outcomes. Building on our laboratory-based pilot study with 60 registered nurses (RNs) from 1 Midwest US metropolitan area indicating the importance of graph literacy (GL), we conducted a fully powered, innovative, national, and web-based randomized controlled trial with 203 RNs.

Predicting Heterogeneity in Patient Response to Morphine Treatment for Neonatal Opioid Withdrawal Syndrome.

Infants with neonatal opioid withdrawal syndrome commonly receive morphine treatment to manage their withdrawal signs. However, the effectiveness of this pharmacotherapy in managing the infants' withdrawal signs vary widely. We sought to understand how information available early in infant monitoring can anticipate this treatment response, focusing on early modified Finnegan Neonatal Abstinence Scoring System (FNASS) scores, polygenic risk for opioid dependence (polygenic risk score (PRS)), and drug exposure.

Physical and cognitive impact following SARS-CoV-2 infection in a large population-based case-control study.

Background: Persistent symptoms are common after SARS-CoV-2 infection but correlation with objective measures is unclear.

Methods: We invited all 3098 adults who tested SARS-CoV-2 positive in Iceland before October 2020 to the deCODE Health Study. We compared multiple symptoms and physical measures between 1706 Icelanders with confirmed prior infection (cases) who participated, and 619 contemporary and 13,779 historical controls.

Management of Fall Risk Among Older Adults in Diverse Primary Care Settings.

Objectives: Falls are persistent among community-dwelling older adults despite existing prevention guidelines. We described how urban and rural primary care staff and older adults manage fall risk and factors important to integration of computerized clinical decision support (CCDS).

Methods: Interviews, contextual inquiries, and workflow observations were analyzed using content analysis and synthesized into a journey map.

Rapid, buoyancy-driven ice-sheet retreat of hundreds of metres per day.

Rates of ice-sheet grounding-line retreat can be quantified from the spacing of corrugation ridges on deglaciated regions of the seafloor, providing a long-term context for the approximately 50-year satellite record of ice-sheet change. However, the few existing examples of these landforms are restricted to small areas of the seafloor, limiting our understanding of future rates of grounding-line retreat and, hence, sea-level rise. Here we use bathymetric data to map more than 7,600 corrugation ridges across 30,000 km of the mid-Norwegian shelf.