Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study.

Background: Physicians spend approximately half of their time on administrative tasks, which is one of the leading causes of physician burnout and decreased work satisfaction. The implementation of natural language processing-assisted clinical documentation tools may provide a solution.

Objective: This study investigates the impact of a commercially available Dutch digital scribe system on clinical documentation efficiency and quality.

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.

Importance: The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow.

Objectives: To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle.

Applying natural language processing to patient messages to identify depression concerns in cancer patients.

Objective: This study aims to explore and develop tools for early identification of depression concerns among cancer patients by leveraging the novel data source of messages sent through a secure patient portal.

Materials And Methods: We developed classifiers based on logistic regression (LR), support vector machines (SVMs), and 2 Bidirectional Encoder Representations from Transformers (BERT) models (original and Reddit-pretrained) on 6600 patient messages from a cancer center (2009-2022), annotated by a panel of healthcare professionals. Performance was compared using AUROC scores, and model fairness and explainability were examined.

Sleep quality and the need for recovery among nurses working irregular shifts: A cross-sectional study.

Background: Shift work affects the mental and physical health of nurses, yet the effect of working irregular shifts on sleep and its association with the need for recovery is under-explored.

Objective: The purpose of this study was to investigate the sleep quality of nurses working irregular shifts, including night shifts, and to determine whether sleep quality is associated with the need for recovery.

Methods: This cross-sectional study included 405 nurses working irregular shifts.

Don't be misled: 3 misconceptions about external validation of clinical prediction models.

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated.

Identification of Distinct Brain MRI Phenotypes and Their Association With Long-Term Dementia Risk in Community-Dwelling Older Adults.

Background And Objectives: Individual brain MRI markers only show at best a modest association with long-term occurrence of dementia. Therefore, it is challenging to accurately identify individuals at increased risk for dementia. We aimed to identify different brain MRI phenotypes by hierarchical clustering analysis based on combined neurovascular and neurodegenerative brain MRI markers and to determine the long-term dementia risk within the brain MRI phenotype subgroups.

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study.

Background: Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients.

Objective: This study aimed to develop a prediction model for depression risk within the first month of cancer treatment.

Drug-induced aseptic meningitis after an interlaminar lumbar epidural steroid injection.

Background: This case report describes a rare instance of drug-induced aseptic meningitis after an interlaminar lumbar epidural steroid injection.

Case Presentation: A 74 year-old female patient presented to the ED post-procedure day three after an L4-L5 interlaminar lumbar epidural steroid injection with fever, nausea, and vomiting. The patient had previously undergone numerous lumbar epidurals without complications and used identical medications, which included 1% lidocaine, iohexol contrast, methylprednisolone (Depo-medrol), and normal saline.

Strategies that nurses working irregular night shifts use to improve sleep quality: A qualitative study among good and poor sleepers.

Aims: To assess the sleep strategies that nurses working irregular night shifts use to improve their sleep quality, and to compare the strategies of good and poor sleepers to determine whether the differences between the two groups could provide insights into possible effective strategies.

Design: A qualitative descriptive study.

Methods: The study was conducted from September 2019 to January 2020.

MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival.

Background: Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time.

Methods: Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist.

Immunogenicity of the 13-Valent Pneumococcal Conjugated Vaccine Followed by the 23-Valent Polysaccharide Vaccine in Chronic Lymphocytic Leukemia.

Patients with Chronic Lymphocytic Leukemia (CLL) have a 29- to 36-fold increased risk of invasive pneumococcal disease (IPD) compared to healthy adults. Therefore, most guidelines recommend vaccination with the 13-valent pneumococcal conjugated vaccine (PCV13) followed 2 months later by the 23-valent polysaccharide vaccine (PPSV23). Because both CLL as well as immunosuppressive treatment have been identified as major determinants of immunogenicity, we aimed to assess the vaccination schedule in untreated and treated CLL patients.

Predicting Depression Risk in Patients with Cancer Using Multimodal Data.

When patients with cancer develop depression, it is often left untreated. We developed a prediction model for depression risk within the first month after starting cancer treatment using machine learning and Natural Language Processing (NLP) models. The LASSO logistic regression model based on structured data performed well, whereas the NLP model based on only clinician notes did poorly.

Diagnosis Classification in the Emergency Room Using Natural Language Processing.

Diagnosis classification in the emergency room (ER) is a complex task. We developed several natural language processing classification models, looking both at the full classification task of 132 diagnostic categories and at several clinically applicable samples consisting of two diagnoses that are hard to distinguish.

Perspectives on validation of clinical predictive algorithms.

The generalizability of predictive algorithms is of key relevance to application in clinical practice. We provide an overview of three types of generalizability, based on existing literature: temporal, geographical, and domain generalizability. These generalizability types are linked to their associated goals, methodology, and stakeholders.

Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study.

Background: Artificial intelligence-based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools.

Objective: We aimed to investigate physicians' perspectives and their current decision-making behavior before implementing a discharge AI-CDS tool for predicting readmission and mortality risk after ICU discharge.

Predicting Readmission or Death After Discharge From the ICU: External Validation and Retraining of a Machine Learning Model.

Objectives: Many machine learning (ML) models have been developed for application in the ICU, but few models have been subjected to external validation. The performance of these models in new settings therefore remains unknown. The objective of this study was to assess the performance of an existing decision support tool based on a ML model predicting readmission or death within 7 days after ICU discharge before, during, and after retraining and recalibration.

Opportunities, barriers and facilitators of an indicated prevention strategy to prevent future long-term sickness absence in SMEs: A qualitative study.

Background: The efficacy of an indicated prevention strategy for long-term absence due to sickness has been demonstrated and is implemented in multinational companies. Such a strategy may also be beneficial for small and medium-sized enterprises (SMEs). However, due to the different contexts, adoption, and implementation of this strategy in SMEs may be quite different.

Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations.

Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients).

Functional brain network and trail making test changes following major surgery and postoperative delirium: a prospective, multicentre, observational cohort study.

Background: Delirium is a frequent complication after surgery in older adults and is associated with an increased risk of long-term cognitive impairment and dementia. Disturbances in functional brain networks were previously reported during delirium. We hypothesised that alterations in functional brain networks persist after remission of postoperative delirium and that functional brain network alterations are associated with long-term cognitive impairment.

External validation and updating of prediction models for estimating the 1-year risk of low health-related quality of life in colorectal cancer survivors.

Objectives: Timely identification of colorectal cancer (CRC) survivors at risk of experiencing low health-related quality of life (HRQoL) in the near future is important for enabling appropriately tailored preventive actions. We previously developed and internally validated risk prediction models to estimate the 1-year risk of low HRQoL in long-term CRC survivors. In this article, we aim to externally validate and update these models in a population of short-term CRC survivors.

Different cardiovascular risk factors are related to distinct white matter hyperintensity MRI phenotypes in older adults.

The underlying mechanisms of the association between cardiovascular risk factors and a higher white matter hyperintensity (WMH) burden are unknown. We investigated the association between cardiovascular risk factors and advanced WMH markers in 155 non-demented older adults (mean age: 71 ± 5 years). The association between cardiovascular risk factors and quantitative MRI-based WMH shape and volume markers were examined using linear regression analysis.

Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).

Background: Evaluating patients' experiences is essential when incorporating the patients' perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended questions is widely recognized. Natural language processing (NLP) can automate the analysis of open-ended questions for an efficient approach to patient-centeredness.

Retrograde Intubation Over a Flexible Fiber-Optic Bronchoscope.

Traditional retrograde intubation consists of tracheal intubation performed over a guide wire inserted into the trachea inferior to the vocal cords and then passed transorally or transnasally. This intubation technique is reserved for patients with a difficult airway when other methods such as blind nasal intubation or video laryngoscopy fail. A guide wire passed blindly in a retrograde fashion, however, is not without its own constraints.