Kenrick Cato

Unanticipated transfer to the intensive care unit (ICU), typically defined as an unscheduled escalation of care from inpatient general wards, is widely used in clinical research as a marker of adverse events, clinical deterioration, and system failure. However, its conceptual meaning, definition, and operationalization vary substantially across the literature, complicating interpretation and cross-study comparison. We conducted a scoping review following PRISMA-ScR guidelines to map how unanticipated ICU transfers are defined, framed, and used in empirical research. Searches of PubMed, CINAHL, and Web of Science identified 4,019 studies through August 7, 2025. After removing duplicates, 2,092 studies were screened for title and abstract, 565 underwent full-text review, and 219 studies met the inclusion criteria. Most studies were observational studies and terminology varied widely, with “unplanned admission to ICU” being the most common. Definitions were heterogeneous but generally focused on unscheduled ICU admission from inpatient wards, often excluding transfers from the emergency department or operating room and sometimes incorporating triggers such as rapid response activation or time-based thresholds. Unanticipated ICU transfer was predominantly used as a dependent variable and was overwhelmingly framed as a negative outcome or proxy for clinical deterioration. However, a smaller body of literature conceptualized these transfers as appropriate or beneficial escalations of care, particularly when occurring early in the course of deterioration. Our findings highlight definitional and conceptual inconsistency and suggest that unanticipated ICU transfer is a context-dependent process measure influenced by both patient condition and institutional factors. Greater standardization and a reframing toward appropriateness and timing of escalation may improve its utility in research and practice.

Background: Childhood asthma places substantial burdens on caregivers, particularly in communities facing structural barriers to health care access. Patient portals, the patient-facing component of electronic health record systems, are primarily designed for clinical workflows, limiting their usefulness for caregivers managing their child’s asthma. Objective: This qualitative study aimed to elicit primary caregivers’ perspectives on managing childhood asthma and their experiences with patient portals to inform the development of a community-centered and family-centered medical platform. Methods: The authors conducted a qualitative study using in-depth, semistructured interviews between July and October 2024 with 15 Black primary caregivers of children with asthma living in the Greater Philadelphia area. Participants were recruited through purposive sampling in partnership with Philly Thrive, a community-based organization. Interviews were conducted both in person and virtually and lasted 30 to 90 minutes. A semistructured guide with 3 core questions explored experiences with managing childhood asthma, interactions with medical documentation and patient portals, and recommendations for redesigning patient portals. The number of interviews was determined by data richness and fulfillment of study aims. Data were analyzed using reflexive thematic analysis, guided by the Standards for Reporting Qualitative Research. Results: Two themes were generated. Theme 1, lived experiences of managing childhood asthma in families and communities, revealed that caregiving involves profound emotional work, daily routines, and community knowledge sharing that health systems largely do not recognize or support. Theme 2, caregivers’ perceptions and experiences of navigating patient portals for childhood asthma, revealed that caregivers used portals selectively at key care moments rather than routinely. Persistent barriers included confusing medical terminology, outdated medication records, and limited communication channels. Caregivers recommended direct provider messaging, integration of environmental trigger data, streamlined medication refill processes, and age-appropriate portal features to support children’s gradual self-management. Conclusions: Effective childhood asthma management requires information systems that honor caregiver expertise, provide transparent and accurate documentation, and actively work to reduce epistemic injustice. Community-centered platform design should integrate caregiver knowledge, environmental data, and child-appropriate tools to support equitable asthma care.

Background: Natural language processing can extract meaningful information from clinical notes. However, human annotation is time-consuming and costly, and scarce data poses a challenge. Objective: This study aimed to explore a semantic similarity search approach to extract exemplars of stigmatizing and positive language in obstetric clinical notes. Methods: We used electronic health record data from labor and birth admissions at 2 hospitals in the United States from 2017 to 2019. We used a semantic similarity search approach, which used 200 randomly selected true exemplars, stratified by language categories, as queries to search for similar exemplar candidates. We extracted the top 5 candidates with the highest cosine similarities, which were assessed for accuracy. Results: We retrieved 1000 candidates. An average precision of 0.69 was achieved when candidates with cosine similarity thresholds of 0.75 or higher were included, at which point 68.8% (64/93) of exemplar candidates accurately represented true cases. At the 0.75 threshold, the proportion of true cases was higher for preferred language (41/56, 73.2%) and unilateral/authoritarian decisions (5/7, 71.4%). The proportion of true cases was lower for difficult patients (2/7, 28.6%) and marginalized identities (3/9, 33.3%). Conclusions: The semantic similarity search approach shows promise in efficiently extracting exemplars while reducing the annotation burden, laying the groundwork for future applications in other domains.

BACKGROUND Artificial intelligence (AI) systems in health care increasingly rely on electronic health record (EHR) data for predictive modeling. However, EHR data often fail to capture real-world nursing workflows and clinical reasoning. In inpatient settings, gaps in glucose management documentation may reflect either care delivered but not documented (missing data) or care not delivered (missed care), yet these scenarios appear indistinguishable in EHR data. Without understanding how care is delivered, AI models may misclassify missing documentation as missed care, reducing accuracy and creating safety risks. OBJECTIVE To explore how inpatient nurses manage and document insulin-related care, informing clinical knowledge graphs of nursing decision-making. METHODS We conducted a qualitative study using semi-structured interviews and focus groups with inpatient registered nurses across the United States. Data were analyzed using a hybrid deductive–inductive approach informed by the Theory of Missed Nursing Care, with GPT-assisted thematic synthesis reviewed by nurse experts. RESULTS Nineteen nurses participated across 10 sessions. Six themes explained why EHR gaps arise and are misinterpreted: (1) technology friction and interoperability gaps; (2) competing demands forcing documentation workarounds; (3) clinical decisions adjusting to mealtime variability; (4) informal, undocumented communication; (5) under documented but necessary protocol deviations; and (6) narrative documentation supplementing inadequate structured fields. CONCLUSIONS Documentation gaps often reflect adaptive, context-dependent nursing work rather than missed care. Structured EHR fields fail to capture workarounds and informal reasoning, meaning AI models built without domain expertise risk conflating undocumented care with absent care, threatening both accuracy and patient safety. Inpatient insulin management is context-dependent and cannot be reliably interpreted without adequate domain expertise. Distinguishing missing data from missed care requires representing these dependencies explicitly, offering a practical foundation for safer, more interpretable AI models built on nurse-generated EHR data.

Like most organizations, The Children’s Hospital of Philadelphia (CHOP) generates a lot of data of varying types, from clinical to staffing and operations. However, these datasets are separated, preventing effective analytics. To improve workforce management and care delivery, CHOP’s Nursing department collaborated with the Data & Analytics team to create a dashboard-based platform for analyzing workforce metrics and patient outcomes, aiding nursing leaders in decision-making. This project involved multiple phases with contributions from Nursing, Data & Analytics, CHOP’s Data Trust Office, and Human Resources. Key performance indicators were identified, and a variety of data sources were aggregated to provide a comprehensive view of the nursing enterprise. The resulting platform offers automated, current, and reliable analytics on various aspects, including nursing demographics, education, survey results, staffing actuals by job group, and patient and family experience data. The platform’s usability was assessed using a modified Health Information Technology Usability Evaluation Scale survey, with a 29% response rate primarily from senior directors and managers. The findings showed high usability and satisfaction, indicating the dashboard is a valuable decision-support tool. Lessons learned include the need for analytics education for nurses and mid-managers, the inclusion of critical nursing-specific metrics (and development of the data pipelines making them possible), and the integration of multidisciplinary team metrics for comprehensive nursing analytics.

Background: Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets. Objective: This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression. Methods: This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Results: The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network. Conclusions: The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: During the COVID-19 pandemic in 2020, hospitals encountered numerous challenges that compounded their difficulties. Some of these challenges directly impacted patient care, such as the need to expand capacities, adjust services, and use new knowledge to save lives in an ever-evolving situation. In addition, hospitals faced regulatory challenges. Objective: This paper presents the findings of a qualitative study that aimed to compare the effects of reporting requirements on a small independent hospital and a large network hospital during the COVID-19 pandemic. Methods: We used both quantitative and qualitative analyses and conducted 51 interviews, which were thematically analyzed. We quantified the changes in regulatory reporting requirements during the first 14 months of the pandemic. Results: Reporting requirements placed a substantial time burden on key clinical personnel at the small independent hospital, consequently reducing the time available for patient care. Conversely, the large network hospital had dedicated nonclinical staff responsible for reporting duties, and their robust health information system facilitated this work. Conclusions: The discrepancy in health IT capabilities suggests that there may be significant institutional inequities affecting smaller hospitals’ ability to respond to a pandemic and adequately support public health efforts. Electronic certification guidelines are essential to addressing the substantial equity issues. We discuss in detail the health care policy implications of these findings.