The Ideal Human Care in Green ICU: An integrated AI framework for future ICU care.

Background: Intensive Care Units (ICUs) present a high-stakes environment where timely decision-making is critical for managing patients with life-threatening conditions. The continuous influx of complex data often challenges clinicians, increasing the risk of errors. Artificial Intelligence (AI) offers transformative potential to enhance ICU care by supporting data analysis, decision-making, and workflow efficiency.

Persistent inflammation, immunosuppression, and catabolism syndrome and sepsis in pediatric burns.

Objective: To determine the prevalence of persistent inflammation, immunosuppression, and catabolism syndrome (PIICS) and associated sepsis in children with burn injuries at a single, large institution over the past 25 years.

Background: Despite advances in care, sepsis after burn injury continues to have a significant contribution to morbidity and mortality. This risk is compounded by an altered immune state after burn injury that can induce PIICS.

School-Based Health Providers' Approaches to Social Risk Screening for Pediatric Asthma: A Qualitative Study.

Background: Asthma has a high prevalence among children and is associated with negative outcomes and extreme costs. Asthma exacerbations, often preventable, have been associated with social determinants of health and social risk factors. It is unclear whether school-based health providers consider social information for asthma, possibly related to a lack of knowledge or data availability.

Automating the Addiction Behaviors Checklist for Problematic Opioid Use Identification.

Importance: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Electronic health records (EHR) allow large-scale studies to identify a continuum of problematic opioid use, including opioid use disorder. Traditionally, this is done through diagnostic codes, which are often unreliable and underused.

Association of Opioid Use Disorder-Related Service Trajectories During Pregnancy and Postpartum Health Service Use: A Group-Based Multitrajectory Modeling Study.

Objective: The aim of the study was to examine the relationship between opioid use disorder (OUD)-related service trajectories during pregnancy and postpartum emergency department (ED) and hospitalizations.

Methods: We used the Merative MarketScan Commercial Claims and Encounters Database (2013-2021) to identify a cohort of pregnant individuals with OUD. We used group-based multitrajectory modeling to identify opioid-related treatment and service trajectories during pregnancy and examined their association with postpartum ED and hospital utilization.

Changes in Postpartum Opioid Prescribing After Implementation of State Opioid Prescribing Limits.

Importance: In response to the growing opioid crisis, states implemented opioid prescribing limits to reduce exposure to opioid analgesics. Research in other clinical contexts has found that these limits are relatively ineffective at changing opioid analgesic prescribing.

Objective: To examine the association of state-level opioid prescribing limits with opioid prescribing within the 30-day postpartum period, as disaggregated by type of delivery (vaginal vs cesarean) and opioid naivete.

Impact of chronic pelvic pain on quality of life in diverse young adults.

Purpose: To examine the relationship between quality of life (QoL) and chronic pelvic pain (CPP), including an evaluation of whether differences exist between reported races and coping mechanisms used.

Methods: We used a cross-sectional survey design and analyzed data using descriptive and inferential statistics. We administered two surveys: the World Health Organization Quality of Life-BREF (26 items) and the Impact of Female Chronic Pelvic Pain Questionnaire (8 items).

Teaching Data Science through an Interactive, Hands-On Workshop with Clinically Relevant Case Studies.

Background:  In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.

Objectives:  Addressing the limited exposure of health care providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.

Integrating Diversity, Equity, Inclusion, and Accessibility into a Data Storytelling Model for Health Informatics Education.

Background:  Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic electronic health records, supporting tailored learning activities in informatics education to enhance educational utility through a DEIA lens.

Factors Associated with Acute Telemental Health Consultations in Older Veterans.

Introduction: The United States Veterans Health Administration is a leader in the use of telemental health (TMH) to enhance access to mental healthcare amidst a nationwide shortage of mental health professionals. The Tennessee Valley Veterans Affairs (VA) Health System piloted TMH in its emergency department (ED) and urgent care clinic (UCC) in 2019, with full 24/7 availability beginning March 1, 2020. Following implementation, preliminary data demonstrated that veterans ≥65 years old were less likely to receive TMH than younger patients.

Use of noisy labels as weak learners to identify incompletely ascertainable outcomes: A Feasibility study with opioid-induced respiratory depression.

Objective: Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence.

Use of Noisy Labels as Weak Learners to Identify Incompletely Ascertainable Outcomes: A Feasibility Study with Opioid-Induced Respiratory Depression.

Objective: Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Background: Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Background: Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

Inpatient nurses' preferences and decisions with risk information visualization.

Objective: We examined the influence of 4 different risk information formats on inpatient nurses' preferences and decisions with an acute clinical deterioration decision-support system.

Materials And Methods: We conducted a comparative usability evaluation in which participants provided responses to multiple user interface options in a simulated setting. We collected qualitative data using think aloud methods.

Detecting Problematic Opioid Use in the Electronic Health Record: Automation of the Addiction Behaviors Checklist in a Chronic Pain Population.

Importance: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Large data sets, such as electronic health records, are required for conducting studies that assist with identification and management of problematic opioid use.

Objective: Determine whether regular expressions, a highly interpretable natural language processing technique, could automate a validated clinical tool (Addiction Behaviors Checklist) to expedite the identification of problematic opioid use in the electronic health record.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

Objectives: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

Methods: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021.

Telemental health in emergency care settings: A qualitative analysis of considerations for sustainability and spread.

Objectives: Following rapid uptake of telehealth during the COVID-19 pandemic, we examined barriers and facilitators for sustainability and spread of telemental health video (TMH-V) as policies regarding precautions from the pandemic waned.

Methods: We conducted a qualitative study using semistructured interviews and observations guided by RE-AIM. We asked four groups, local clinicians, facility leadership, Veterans, and external partners, about barriers and facilitators impacting patient willingness to engage in TMH-V (reach), quality of care (effectiveness), barriers and facilitators impacting provider uptake (adoption), possible adaptations to TMH-V (implementation), and possibilities for long-term use of TMH-V (maintenance).

The effect of telemental versus in-person mental health consults in the emergency department on 30-day utilization and processes of care.

Objectives: We sought to characterize how telemental health (TMH) versus in-person mental health consults affected 30-day postevaluation utilization outcomes and processes of care in Veterans presenting to the emergency department (ED) and urgent care clinic (UCC) with acute psychiatric complaints.

Methods: This exploratory retrospective cohort study was conducted in an ED and UCC located in a single Veterans Affairs system. A mental health provider administered TMH via iPad.

Identifying problematic opioid use in electronic health record data: Are we looking in the right place?

Objective: To examine the value of data obtained outside of regular healthcare visits (clinical communications) to detect problematic opioid use in electronic health records (EHRs).

Design: A retrospective cohort study.

Participants: Chronic pain patient records in a large academic medical center.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.

Background: The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review.

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses.

Assess, Prevent, and Manage Pain; Both Spontaneous Awakening and Breathing Trials; Choice of Analgesia/Sedation; Delirium: Assess, Prevent, and Manage; Early Mobility; Family Engagement and Empowerment Bundle Implementation: Quantifying the Association of Access to Bundle-Enhancing Supplies and Equipment.

Unlabelled: Describe the physical environment factors (i.e., availability, accessibility) of bundle-enhancing items and the association of physical environment with bundle adherence.