Standardizing Data Elements for Implementation of ICU Liberation Bundle

Applied Clinical Informatics · 2026-01-01

Getting patients out of intensive care units (ICUs) is a major goal for acute care clinicians, as prolonged stays increase the risk of complications and strain critical resources such as staff, equipment, and beds. The ICU Liberation Bundle, or the ABCDEF (A-F) care bundle, is an evidence-based framework for improving outcomes in critically ill patients by addressing pain, sedation, delirium, mobility, and family engagement. However, variability in documentation and a lack of standardized data elements hinder effective implementation and evaluation of adherence to bundle components.This study aims to characterize data elements of the A-F liberation bundle using a large, single-center critical care database and to develop standardized bundle cards that map bundle components to controlled vocabularies.We conducted a retrospective analysis of data elements related to the A-F bundle using the MIMIC-IV database. Clinical concepts were mapped to standardized vocabularies and aligned with the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Bundle cards were developed for each component to provide structured, accessible documentation of assessment tools, adherence criteria, and terminology mappings.Pain assessments were documented in over 11,000 patients, with a median of 23 assessments per day. Sedation levels for nearly 59,000 patients were evaluated, with 37.7% meeting the Society of Critical Care Medicine (SCCM) adherence criteria. Delirium assessments followed standardized protocols incorporating Richmond Agitation-Sedation Scale (RASS) and CAM-ICU scores. Components E and F lacked formal compliance specifications; bundle cards for these components identified key activities and highlighted gaps in standardized vocabularies. Adherence analyses revealed variability likely due to non-standardized documentation practices.We developed and validated six ICU Liberation Bundle cards that map bundle components to standardized vocabularies and CDMs, enabling retrospective adherence evaluation in real-world data. These information resources promote consistent documentation, support interoperability, and provide a foundation for prospective monitoring to enhance bundle implementation in critical care.

SHREC: A framework for advancing next-generation computational phenotyping with large language models

PLOS Digital Health · 2026-02-13

Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review and limited automation. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To support next-generation computational phenotyping, we developed SHREC, a framework for integrating LLMs into end-to-end phenotyping pipelines. We applied and tested three lightweight LLMs (Gemma2 27 billion, Mistral Small 24 billion, and Phi-4 14 billion) to classify concepts and phenotype patients using phenotypes for ARF respiratory support therapies. All models performed well on concept classification, with the best (Mistral) achieving an AUROC of 0.896. For phenotyping, models demonstrated near-perfect specificity for all phenotypes with the top-performing model (Mistral) achieving an average AUROC of 0.853 for single-therapy phenotypes. In conclusion, lightweight LLMs can assist researchers with resource-intensive phenotyping tasks. Several advantages of LLMs included their ability to adapt to new tasks with prompt engineering alone and their ability to incorporate raw EHR data. Future steps include determining optimal strategies for integrating biomedical data and understanding reasoning errors.

FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program

ArXiv.org · 2026-04-07

Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently. FairLogue, a toolkit for intersectional fairness auditing, was applied across multiple clinical prediction tasks to evaluate disparities across combined demographic groups. Using the All of Us dataset, two published models were selected for replication and evaluation: (A) prediction of selective serotonin reuptake inhibitor associated bleeding events and (B) two-year stroke risk in patients with atrial fibrillation. Observational fairness metrics were computed across race, gender, and intersectional subgroups, followed by counterfactual analysis to evaluate whether disparities were attributable to group membership. Intersectional evaluation revealed larger disparities than single-axis analyses; however, counterfactual diagnostics indicated that most observed disparities were comparable to those expected under randomized group membership. These results highlight the importance of intersectional fairness auditing and demonstrate how FairLogue provides deeper insight into bias in clinical machine learning systems.

Development and evaluation of an ontology for non-invasive respiratory support in acute care

PLoS ONE · 2026-05-04

Managing patients with respiratory failure increasingly involves non-invasive respiratory support (NIRS) strategies to support respiration, often preventing the need for invasive mechanical ventilation. However, despite the rapidly expanding use of NIRS, there remains a significant challenge to its optimal use across all medical circumstances. It lacks a unified ontological structure, complicating guidance on NIRS modalities across healthcare systems. In this study, we introduced NIRS ontology to support knowledge representation in acute care settings by providing a unified framework that enhances data clarity and interoperability, laying the groundwork for future clinical decision-making research. We developed NIRS ontology using the Web Ontology Language (OWL) and Protégé to organize clinical concepts and relationships. To enable rule-based clinical reasoning beyond hierarchical structures, we added Semantic Web Rule Language (SWRL) rules. We evaluated logical reasoning using a sample of 6 patient scenarios and used SPARQL queries to retrieve and test targeted inferences. The ontology has 145 classes, 11 object properties, and 18 data properties across 949 axioms that establish concept relationships. To standardize clinical concepts, we added 392 annotations, including descriptive definitions based on controlled vocabularies. SPARQL queries successfully validated all test cases (rules) by retrieving appropriate patients' outcomes: for instance, a patient treated with HFNC (high-flow nasal cannula) for 2 hours due to acute respiratory failure may avoid endotracheal intubation. This NIRS ontology formally represents domain-specific concepts, including ventilation modalities, patient characteristics, therapy parameters, and outcomes. SPARQL query evaluations across clinical scenarios confirmed the ontology's ability to support rule‑based reasoning and therapy recommendations, providing a foundation for consistent documentation practices, integration into clinical data models, and advanced analysis of NIRS outcomes. In conclusion, this proof-of-concept NIRS ontology demonstrates how clinical concepts can be formally represented and queried, offering a foundation for future validation, EHR integration, and evidence-based refinement of NIRS practices.

FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models

arXiv (Cornell University) · 2026-04-06

Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may miss compounded disparities affecting intersectional populations. This study introduces Fairlogue, a toolkit designed to operationalize intersectional fairness assessment in observational and counterfactual contexts within clinical settings. Methods: Fairlogue is a Python-based toolkit composed of three components: 1) an observational framework extending demographic parity, equalized odds, and equal opportunity difference to intersectional populations; 2) a counterfactual framework evaluating fairness under treatment-based contexts; and 3) a generalized counterfactual framework assessing fairness under interventions on intersectional group membership. The toolkit was evaluated using electronic health record data from the All of Us Controlled Tier V8 dataset in a glaucoma surgery prediction task using logistic regression with race and gender as protected attributes. Results: Observational analysis identified substantial intersectional disparities despite moderate model performance (AUROC = 0.709; accuracy = 0.651). Intersectional evaluation revealed larger fairness gaps than single-axis analyses, including demographic parity differences of 0.20 and equalized odds true positive and false positive rate gaps of 0.33 and 0.15, respectively. Counterfactual analysis using permutation-based null distributions produced unfairness ("u-value") estimates near zero, suggesting observed disparities were consistent with chance after conditioning on covariates. Conclusion: Fairlogue provides a modular toolkit integrating observational and counterfactual methods for quantifying and evaluating intersectional bias in clinical machine learning workflows.

FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models

arXiv (Cornell University) · 2026-04-06

Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may miss compounded disparities affecting intersectional populations. This study introduces Fairlogue, a toolkit designed to operationalize intersectional fairness assessment in observational and counterfactual contexts within clinical settings. Methods: Fairlogue is a Python-based toolkit composed of three components: 1) an observational framework extending demographic parity, equalized odds, and equal opportunity difference to intersectional populations; 2) a counterfactual framework evaluating fairness under treatment-based contexts; and 3) a generalized counterfactual framework assessing fairness under interventions on intersectional group membership. The toolkit was evaluated using electronic health record data from the All of Us Controlled Tier V8 dataset in a glaucoma surgery prediction task using logistic regression with race and gender as protected attributes. Results: Observational analysis identified substantial intersectional disparities despite moderate model performance (AUROC = 0.709; accuracy = 0.651). Intersectional evaluation revealed larger fairness gaps than single-axis analyses, including demographic parity differences of 0.20 and equalized odds true positive and false positive rate gaps of 0.33 and 0.15, respectively. Counterfactual analysis using permutation-based null distributions produced unfairness ("u-value") estimates near zero, suggesting observed disparities were consistent with chance after conditioning on covariates. Conclusion: Fairlogue provides a modular toolkit integrating observational and counterfactual methods for quantifying and evaluating intersectional bias in clinical machine learning workflows.

Characterizing Fungal Infections in the All of Us Research Program

ArXiv.org · 2025-12-26

Fungal infections, such as Coccidioidomycosis, Aspergillosis, and Histoplasmosis, represent a growing public health concern in the United States. The rising incidence of these mycoses is linked to climate shifts, demographic changes, and social determinants of health. However, the actual burden of these infections is often underestimated by traditional surveillance methods. Therefore, this study aims to characterize these infections within the All of Us Research Program and evaluate the quality of clinical and health data related to fungal infections. We constructed three fungi cohorts of Coccidioidomycosis (n=1,173), Aspergillosis (n=687), and Histoplasmosis (n=345) among over 400,000 participants using electronic health record data. We analyzed geographic and sociodemographic distributions and performed a data quality assessment on ten key laboratory biomarkers to evaluate data completeness, unit conformance, and measurement concordance within a 90-day window of diagnosis. Our analysis confirmed known epidemiological patterns, including the geographic distributions of Coccidioidomycosis in the Southwest and Histoplasmosis in the Midwest. Fungal infections disproportionately affected older adults, males, and White non-Hispanic individuals. The data quality assessment revealed high completeness for general hematology markers (e.g., Hemoglobin > 70%) but limited availability for biomarkers, such as Beta 1,3 Glucan (< 15%). While measurement concordance was strong (e.g., hemoglobin-hematocrit correlation, r = 0.94), unit conformance was poor for key inflammatory markers, such as erythrocyte sedimentation rate. In conclusion, the All of Us dataset is a valuable resource for characterizing fungal infections. However, significant data quality issues related to completeness and conformance for specialized biomarkers must be addressed to enhance their applicability for robust clinical research.

Design and Development of an Integrated Virtual Reality (VR)-Based Training System for Difficult Airway Management

IEEE Journal of Translational Engineering in Health and Medicine · 2025-01-01 · 8 citations

For over 40 years, airway management simulation has been a cornerstone of medical training, aiming to reduce procedural risks for critically ill patients. However, existing simulation technologies often lack the versatility and realism needed to replicate the cognitive and physical challenges of complex airway management scenarios. We developed a novel Virtual Reality (VR)-based simulation system designed to enhance immersive airway management training and research. This system integrates physical and virtual environments with an external sensory framework to capture high-fidelity data on user performance. Advanced calibration techniques ensure precise positional tracking and realistic physics-based interactions, providing a cohesive mixed-reality experience. Validation studies conducted in a dedicated medical training center demonstrated the system's effectiveness in replicating real-world conditions. Positional calibration accuracy was achieved within 0.1 cm, with parameter calibrations showing no significant discrepancies. Validation using Pre- and post-simulation surveys indicated positive feedback on training aspects, perceived usefulness, and ease of use. These results suggest that the system offers a significant improvement in procedural and cognitive training for high-stakes medical environments.

Developing and sustaining inclusive language in biomedical informatics communications: an AMIA Board of Directors endorsed paper on the Inclusive Language and Context Style Guidelines

Journal of the American Medical Informatics Association · 2025-06-16 · 2 citations

OBJECTIVES: In 2023, AMIA's Inclusive Language and Context Style Guidelines (the "Guidelines") were approved by the Board of Directors and made a publicly available resource. This work began in 2021 through AMIA's DEI Task Force and subsequent DEI Committee; many members provided input, feedback, and time to create the Guidelines. In this paper, the authors provide a transparent account of the origin, development, contents, and dissemination of the Guidelines and share plans for their future development and use. MATERIALS AND METHODS: Our approach to drafting, refining, and distributing the Guidelines included consulting existing language guides, AMIA member reviews, external expert reviews, webinars, and workshops. Through an iterative approach to drafting and refining the Guidelines, the authors consulted relevant language guidelines and many experts throughout and beyond the AMIA community. RESULTS: The Inclusive Language Context Guidelines were formally approved by the AMIA Board of Directors on February 15, 2023. The Guidelines included four principles to be considered in scientific communications: Plurality, Precision, Transparency, and Destigmatization. DISCUSSION: A moment of vulnerability where an AMIA member raised concerns about the use of harmful language during a presentation resulted in the creation of a principled approach to support inclusive language within biomedical and health informatics communications. We envision that the Guidelines will support health equity by challenging dominant public narratives around health, fostering stronger interdisciplinary collaboration and critical thinking about the impact of language, and creating a more welcoming environment for the broader AMIA community. This work could not have been completed without the support of many AMIA members and other researchers in biomedical and health informatics. The Guidelines are a living document that will continue to be updated with input and feedback from the AMIA community into the future.