[Medical informatics].
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Delivery of Health Care↗
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[The place of the medical informatics in a large general and university hospital (author's transl)].
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Hospitals, General↗
Data security--a challenge to medical informatics.
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Computers↗
[Modern medical documentation and the role of medical informatics in solving its problems (author's transl)].
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Information Systems↗
MUMPS as an education tool.
The department of Medical Informatics at the Free University is, in addition to research, responsible for the education of medical students in medical-information processing. The Department has a PDP 11/70 running part-time under MUMPS-11 V4B, and since 1978 has had a PDP 11/60. A set of medical education programs has been developed consisting of: (a) A simple introduction to MUMPS. (b) A patient-census module. (c) A neonatal module. (d) A dental data-base. In this paper the educational programs are outlined and examples of their use are given.
Bile Ducts↗
Graduate education in computer systems in medicine--a conceptual model. Canadian Organization for Advancement of Computers in Health.
A conceptual model of the graduate educational programme in medical informatics is discussed. Problems involved in different types of informatics training can be avoided by stressing the integrative aspects of a systems approach. This programme is designed to lessen the gulf between a higher level of hardware capability and the ability to use it. The increasing educational demands in medical information systems have put more pressure on educational establishments to respond more expertly.
Biomedical Engineering↗
Computer assisted monitoring in intensive medicine.
Intensive Medicine is always associated with the problem of handling the mass and assuring the quality of information on vital signs, fluid and blood balance, laboratory data, physiological calculations, etc., required in patient care. A computer based monitoring system for intensive care was introduced in 1973 at the Academic Hospital in Leuven. The basic software was developed at the Peter Bent Brigham Hospital of the Harvard Medical School and the medical division of the Hewlett Packard Company; the computer used was a H.P. 2100 central processor with 32K of core memory. Initially, the program allowed mainly acquisition, storage and retrieval of bedside monitored and manual data of cardiac and circulatory function. Very soon however, the software was extended and modified by the division of "Medical Informatics" in order to meet new or different requirements. In the present situation our vision on the use of computer-assisted monitoring has changed and our present program has been extended as follows : 1. On-line collection and retrieval of bedside monitored data including heart rate, arterial blood pressure (systolic-diastolic-mean) left atrial pressure, central venous pressure, pulmonary artery pressure, intracranial pressure. Trend analysis of those data, with calculation of mean values, standard variation and corresponding t-tests. 2. Computer assistance in performing time consuming calculations on off-line data such as : -- clearance-values (renal function), -- temperature-correction of blood-gasvalues, -- hour-to-hour fluid balance, including calculation of in-sensible losses, -- blood-balance. 3. Data transmission of laboratory results as soon as available in the central laboratory through a direct link between laboratory and I.T.U. 4. Computer assisted E.C.G. analysis. The three first objectives are realised, on-line E.C.G.-analysis is being developed. The same computer serves the remotely located medical and coronary care units and one bed in the emergency department. An assessment of computer assistance in intensive therapy, on nursing labor and on quality of patient care is made.
Clinical Laboratory Techniques↗
Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.
BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.
Humans↗
CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.
This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.
Precision Medicine↗
Implementing a training resource for large-scale genomic data analysis in the All of Us Researcher Workbench.
A lack of representation in genomic research and limited access to computational training create barriers for many researchers seeking to analyze large-scale genetic datasets. The All of Us Research Program provides an unprecedented opportunity to address these gaps by offering genomic data from a broad range of participants, but its impact depends on equipping researchers with the necessary skills to use it effectively. The All of Us Biomedical Researcher (BR) Scholars Program at Baylor College of Medicine aims to break down these barriers by providing early-career researchers with hands-on training in computational genomics through the All of Us Evenings with Genetics Research Program. The year-long program begins with the faculty summit, an in-person computational boot camp that introduces scholars to foundational skills for using the All of Us dataset via a cloud-based research environment. The genomics tutorials focus on genome-wide association studies (GWASs), utilizing Jupyter Notebooks and the Hail computing framework to provide an accessible and scalable approach to large-scale data analysis. Scholars engage in hands-on exercises covering data preparation, quality control, association testing, and result interpretation. By the end of the summit, participants will have successfully conducted a GWAS, visualized key findings, and gained confidence in computational resource management. This initiative expands access to genomic research by equipping early-career researchers from a variety of backgrounds with the tools and knowledge to analyze All of Us data. By lowering barriers to entry and promoting the study of representative populations, the program fosters innovation in precision medicine and advances equity in genomic research.
Humans↗
[Study of interventricular septal defects with equal aortic and pulmonary artery pressures. Classification by clinical and computer methods of 70 cases].
Application of various methods of classification to a group of 70 cases of ventricular septal defect with high pulmonary artery hypertension allowed a comparative study between the various methods aiming at distinguishing the forms with low from high pulmonary artery resistance. The reference clinical classification provides supplementary informations derived from the natural or post-operative course and eventually from the microscopic examination. The first automatic classification relies on the study of a single criterion: the pulmonary arteriolar resistance and the systemic resistance ratio. A second classification is based on the attribution of points to some clinical or haemodynamic signs resulting in a score orienting the classification of every individual. Multifactorial analysis methods deal with all the available informations for the overall group, and suppose the use of a computer. The informatic methods make it possible to study the classifying value of every sign. Correlations were established between these various techniques and the medical classification.
Adolescent↗
AI In Leukemia Diagnostics: Complementing the Pathologist's Role.
Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.
Humans↗
Sex Differences in Health Conditions Associated with Sexual Assault in a Large Hospital Population.
INTRODUCTION: Sexual assault is an urgent public health concern with both immediate and long-lasting health consequences, affecting 44% of women and 25% of men during their lifetimes. Large studies are needed to understand the unique healthcare needs of this patient population. METHODS: We mined clinical notes to identify patients with a history of sexual assault in the electronic health record (EHR) at Vanderbilt University Medical Center (VUMC), a large university hospital in the Southeastern USA, from 1989 to 2021 (N = 3,376,424). Using a phenome-wide case-control study, we identified diagnoses co-occurring with disclosures of sexual assault. We performed interaction tests to examine whether sex modified any of these associations. Association analyses were restricted to a subset of patients receiving regular care at VUMC (N = 833,185). RESULTS: The phenotyping approach identified 14,496 individuals (0.43%) across the VUMC-EHR with documentation of sexual assault and achieved a positive predictive value of 93.0% (95% confidence interval = 85.6-97.0%), determined by manual patient chart review. Out of 1,703 clinical diagnoses tested across all subgroup analyses, 465 were associated with sexual assault. Sex-by-trauma interaction analysis revealed 55 sex-differential associations and demonstrated increased odds of psychiatric diagnoses in male survivors. DISCUSSION: This case-control study identified associations between disclosures of sexual assault and hundreds of health conditions, many of which demonstrated sex-differential effects. The findings of this study suggest that patients who have experienced sexual assault are at risk for developing wide-ranging medical and psychiatric comorbidities and that male survivors may be particularly vulnerable to developing mental illness.
Clinical informatics↗
RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR.
BACKGROUND: Rare diseases affect a significant portion of the global population, yet patients often endure a lengthy diagnostic odyssey, frequently missing the opportunity for timely diagnoses with exome or genome sequencing (ES/GS). Existing informatics tools often rely on pre-identified patients or rigid, institution-specific rule sets, failing to address the broader operational question of clinical utility and feasibility. METHODS: We introduce RESCUE (Rare Disease Detection and Escalation Support via a Learning Health System), an end-to-end, multi-agent LLM-powered workflow designed for proactive rare-disease diagnosis across the entire electronic health record (EHR). RESCUE utilizes a team of specialized agents including Ontology, Modeling, Screening, and Review, to automate the screening process to identify candidates for diagnostic testing based on their clinical features. The Ontology Agent classifies clinical data into a four-tier genetic-evidence taxonomy; the Modeling Agent builds a positive-unlabeled (PU) XGBoost classifier to identify potential cases; the Screening Agent applies these models across the EHR population; and the Review Agent evaluates candidates by sampling clinical notes to ensure medical necessity and operational feasibility for genomic testing. RESULTS: Using electronic medical record data from a pediatric hospital, our retrospective evaluation on a holdout set (n=12,591) demonstrates strong discrimination between patients who received diagnostic genomic testing and those who did not (AUC 0.808). Of nearly 500,000 patients in the institutional base, 175,842 met inclusion criteria for screening; among these, RESCUE-flagged candidates were 7.4-fold more likely to receive subsequent genomic assessments compared to controls. Blinded manual chart reviews confirmed that RESCUE identifies previously missed, medically appropriate patients for ES/GS with 80% precision, while simultaneously accounting for prior testing history. CONCLUSIONS: By decoupling expert roles into modular agents, RESCUE offers a flexible, scalable, and adaptable framework for screening patients for rare-disease diagnostic genomic testing. This approach overcomes the limitations of traditional rule-based methods and provides a reproducible, agentic pathway to reduce diagnostic delays and improve patient care at an institutional scale.
Journal Article↗