Post-COVID-19 Condition and Pulmonary Embolism.

Purpose: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes post-acute sequelae of coronavirus disease 2019 (COVID-19), including pulmonary vasculopathy, increasing thrombotic risk. Screening and treating survivors are essential to reduce associated disabilities. We aim to investigate the clinical characteristics of patients with post-COVID-19 condition and pulmonary embolism, as well as their health-related quality of life one year after COVID-19 diagnosis.

Patterns of diuretic titration during inpatient management of acute decompensated heart failure.

Introduction: Hospitalization rates for acute decompensated heart failure (ADHF) have increased, resulting in 6.5 million hospital days annually. Despite this, optimal diuretic strategies for managing ADHF remain unclear, highlighting the need to analyze diuretic practice patterns in ADHF treatment.

Low rates of aortic surveillance imaging and clinical follow-up in patients with acute aortic dissection.

Objectives: Patients that survive acute aortic dissection (AD) remain at high risk of morbidity/mortality from structural changes of the aorta. Aortic surveillance is challenging, especially within a tertiary referral center. Our aim was to identify follow-up imaging and appointment rates, and factors associated with incomplete surveillance in patients with acute AD.

New indices to track interoperability among US hospitals.

Objectives: To develop indices of US hospital interoperability to capture the current state and assess progress over time.

Materials And Methods: A Technical Expert Panel (TEP) informed selection of items from the American Hospital Association Health IT Supplement survey, which were aggregated into interoperability concepts (components) and then further combined into indices. Indices were refined through psychometric analysis and additional TEP input.

Large language models can accurately populate Vascular Quality Initiative procedural databases using narrative operative reports.

Objectives: Participation in the Vascular Quality Initiative (VQI) provides important resources to surgeons, but the ability to do so is often limited by time and data entry personnel. Large language models (LLMs) such as ChatGPT (OpenAI) are examples of generative artificial intelligence (AI) products that may help bridge this gap. Trained on large volumes of data, the models are used for natural language processing (NLP) and text generation.

Default Antibiotic Order Durations for Skin and Soft Tissue Infections in Outpatient Pediatrics: A Cluster Randomized Trial.

Background: Antibiotic durations for uncomplicated skin/soft tissue infections (SSTI) often exceed the guideline-recommended 5-7 days. We assessed the effectiveness of a default duration order panel in the Electronic Health Record (EHR) to reduce long prescriptions.

Methods: Cluster randomized trial of a SSTI order panel with default antibiotic durations (implemented 12/2021), compared to a control panel (no decision support) in 14 pediatric primary care clinics.

From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management.

The high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden associated with the disease make it a pressing issue. Machine learning (ML) can improve VTE prevention, detection, and treatment. The ability of this novel technology to process large amounts of high-dimensional data can help identify new risk factors and better risk stratify patients for thromboprophylaxis.

Towards a Multi-Stakeholder process for developing responsible AI governance in consumer health.

Introduction: AI is big and moving fast into healthcare, creating opportunities and risks. However, current approaches to governance focus on high-level principles rather than tailored recommendations for specific domains like consumer health. This gap risks unintended consequences from generic guidelines misapplied across contexts and from providing answers before agreeing on the questions.

Clinical Sentiment Analysis by Large Language Models Enhances the Prediction of Hepatorenal Syndrome in Decompensated Cirrhosis.

Background And Aims: Hepatorenal syndrome - Acute Kidney Injury (HRS-AKI) is a severe complication of decompensated cirrhosis that is challenging to predict. Sentiment analysis, a computational process of identifying and categorizing opinions and judgment expressed in text, may enhance traditional prediction methodologies based on structured variables. Large language models (LLMs), such as generative pretrained transformers (GPTs), have demonstrated abilities to perform sentiment analyses on non-clinical texts.

Public Health Data Exchange Through Health Information Exchange Organizations: National Survey Study.

Background: The COVID-19 pandemic revealed major gaps in public health agencies' (PHAs') data and reporting infrastructure, which limited the ability of public health officials to conduct disease surveillance, particularly among racial or ethnic minorities disproportionally affected by the pandemic. Leveraging existing health information exchange organizations (HIOs) is one possible mechanism to close these technical gaps, as HIOs facilitate health information sharing across organizational boundaries.

Objective: The aim of the study is to survey all HIOs that are currently operational in the United States to assess HIO connectivity with PHAs and HIOs' capabilities to support public health data exchange.

Positive impact of a diagnostic stewardship intervention on syndromic panel ordering practices and inappropriate treatment.

Objective: Multiplex polymerase chain reaction (PCR) panels for stool testing may be used to diagnose , which can circumvent more appropriate targeted testing, resulting in treatment of incidentally detected colonization. We sought to reduce diagnosis via a gastrointestinal pathogen panel (GIPP).

Design: Quasi-experimental, pre/post, retrospective cohort study from January 1, 2022, to January 31, 2024.

Trending in the right direction: critical access hospitals increased adoption of advanced electronic health record functions from 2018 to 2023.

Objectives: We analyzed trends in adoption of advanced patient engagement and clinical data analytics functionalities among critical access hospitals (CAHs) and non-CAHs to assess how historical gaps have changed.

Materials And Methods: We used 2014, 2018, and 2023 data from the American Hospital Association Annual Survey IT Supplement to measure differences in adoption rates (ie, the "adoption gap") of patient engagement and clinical data analytics functionalities across CAHs and non-CAHs. We measured changes over time in CAH and non-CAH adoption of 6 "core" clinical data analytics functionalities, 5 "core" patient engagement functionalities, 5 new patient engagement functionalities, and 3 bulk data export use cases.

Team and Electronic Health Record Features and Burnout Among Family Physicians.

Importance: In the context of a growing volume of electronic health record (EHR)-based work and post-COVID-19 pandemic staffing pressures, health system leaders need an up-to-date understanding of changes in family physicians' experiences of burnout, determinants of burnout, and how to enhance the family physicians' experience.

Objective: To evaluate the association of family physicians' perceptions of team structure and EHR experiences with burnout and identify modifiable practice structure factors associated with team and EHR experiences.

Design, Setting, And Participants: A serial cross-sectional survey study was conducted from December 1, 2016, to October 24, 2023.

Disparities in Diagnosis, Access to Specialist Care and Treatment for Inborn Errors of Immunity.

Inborn errors of immunity represent a rapidly expanding group of genetic disorders of the immune system. Significant advances have been made in recent years in diagnosis, including using genetic testing and newborn screening; treatment, including precision therapies, gene therapy and hematopoietic stem cell transplant; and development of patient registries to inform prevalence, understand morbidity of these disorders and guide the development of clinical trials. However, significant disparities due to age, race, ethnicity, socioeconomic status, or geographic location exist in all aspects of care of patients with inborn errors of immunity, beginning with delays in diagnosis and further compounded by impaired access to specialist care and treatment, leading to a notable impact on outcomes including morbidity and mortality.

: An Improved Python Package for Loading Datasets from the UCI Machine Learning Repository.

The University of California-Irvine (UCI) Machine Learning (ML) Repository (UCIMLR) is consistently cited as one of the most popular dataset repositories, hosting hundreds of high-impact datasets. However, a significant portion, including 28.4% of the top 250, cannot be imported via the package that is provided and recommended by the UCIMLR website.

Dissecting Secondary Immunodeficiency: Identification of Primary Immunodeficiency within B-Cell Lymphoproliferative Disorders.

Distinguishing between primary (PID) and secondary (SID) immunodeficiencies, particularly in relation to hematological B-cell lymphoproliferative disorders (B-CLPD), poses a major clinical challenge. We aimed to analyze and define the clinical and laboratory variables in SID patients associated with B-CLPD, identifying overlaps with late-onset PIDs, which could potentially improve diagnostic precision and prognostic assessment. We studied 37 clinical/laboratory variables in 151 SID patients with B-CLPD.