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PROTEMPA: a method for specifying and identifying temporal sequences in retrospective data for patient selection.

OBJECTIVE: To specify and identify disease and patient care processes represented by temporal patterns in clinical events and observations, and retrieve patient populations containing those patterns from clinical data repositories, in support of clinical research, outcomes studies, and quality assurance. DESIGN: A data processing method called PROTEMPA (Process-oriented Temporal Analysis) was developed for defining and detecting clinically relevant temporal and mathematical patterns in retrospective data. PROTEMPA provides for portability across data sources, "pluggable" data processing envir

Development and evaluation of methods for structured recording of heart murmur findings using SNOMED-CT post-coordination.

OBJECTIVE: This study evaluated an existing SNOMED-CT model for structured recording of heart murmur findings and compared it to a concept-dependent attributes model using content from SNOMED-CT. METHODS: The authors developed a model for recording heart murmur findings as an alternative to SNOMED-CT's use of Interprets and Has interpretation. A micro-nomenclature was then created to support each model using subset and extension mechanisms described for SNOMED-CT. Each micro-nomenclature included a partonomy of cardiac cycle timing values. A mechanism for handling ranges of values was also de

Monitoring device safety in interventional cardiology.

OBJECTIVE: A variety of postmarketing surveillance strategies to monitor the safety of medical devices have been supported by the U.S. Food and Drug Administration, but there are few systems to automate surveillance. Our objective was to develop a system to perform real-time monitoring of safety data using a variety of process control techniques. DESIGN: The Web-based Data Extraction and Longitudinal Time Analysis (DELTA) system imports clinical data in real-time from an electronic database and generates alerts for potentially unsafe devices or procedures. The statistical techniques used are

Creating a text classifier to detect radiology reports describing mediastinal findings associated with inhalational anthrax and other disorders.

OBJECTIVE: The aim of this study was to create a classifier for automatic detection of chest radiograph reports consistent with the mediastinal findings of inhalational anthrax. DESIGN: The authors used the Identify Patient Sets (IPS) system to create a key word classifier for detecting reports describing mediastinal findings consistent with anthrax and compared their performances on a test set of 79,032 chest radiograph reports. MEASUREMENTS: Area under the ROC curve was the main outcome measure of the IPS classifier. Sensitivity and specificity of an initial IPS model were calculated based

The HL7 Clinical Document Architecture.

Many people know of Health Level 7 (HL7) as an organization that creates health care messaging standards. Health Level 7 is also developing standards for the representation of clinical documents (such as discharge summaries and progress notes). These document standards make up the HL7 Clinical Document Architecture (CDA). The HL7 CDA Framework, release 1.0, became an ANSI-approved HL7 standard in November 2000. This article presents the approach and objectives of the CDA, along with a technical overview of the standard. The CDA is a document markup standard that specifies the structure and sem

Integrating data from legacy systems using object linking and embedding technology: development of a reporting system for heavy metal poisoning results.

Integrating data that reside in different systems remains an often laborious process, requiring either manual steps or complicated programming. This paper describes a method for state-mandated reporting of childhood blood lead testing results that makes use of object linking and embedding technology and readily available software products to pull together information from different legacy systems. A terminal session emulator employs object linking and embedding automation to extract host data, and Visual Basic routines specify the user interface and database manipulation. This system has signi

A reliability study for evaluating information extraction from radiology reports.

GOAL: To assess the reliability of a reference standard for an information extraction task. SETTING: Twenty-four physician raters from two sites and two specialties judged whether clinical conditions were present based on reading chest radiograph reports. METHODS: Variance components, generalizability (reliability) coefficients, and the number of expert raters needed to generate a reliable reference standard were estimated. RESULTS: Per-rater reliability averaged across conditions was 0.80 (95% CI, 0.79-0.81). Reliability for the nine individual conditions varied from 0.67 to 0.97, with cen

Validation of the Medicare-Enhanced Laboratory and Demographics (MELD™) Dataset: A Comprehensive Psychometric, Epidemiologic, and Predictive-Utility Assessment of a 60-Million-Patient Real-World Evidence Resource.

BackgroundReal-world evidence studies increasingly rely on integrated data resources that combine Medicare fee-for-service claims with electronic medical records (EMRs), laboratory results, and patient-reported outcomes. The Medicare-Enhanced Laboratory and Demographics (MELD™) dataset, developed by Columbia Data Analytics, spans 2020 through 2024 and contains 60 million unique patients, roughly 1 billion visits, and more than 241 000 clinicians. Its data-quality properties must be characterized before regulatory-grade use.ObjectiveTo provide a preregistered, multidomain validation of MELD, qu

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… Relative to the Observational Medical Outcomes Partnership/Observational Health Data Sciences and Informatics (OMOP/OHDSI) Data Quality Dashboard, 37 the framework reported here additionally incorporates linkage-quality quantification via Fellegi-Sunter estimation 22 and calibration of downstream risk-prediction … Fremont A.. 2009 9 2 69 83 10.1007/s10742-009-0047-1 37 Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers Stud Health Technol Inform Hripcsak G. Duke J. D. Shah N. …

From data to personas: a multi-source approach to tailored online health information recommendation for ovarian cancer patients and caregivers.

OBJECTIVE: Ovarian cancer (OvCa) patients and caregivers face complex, evolving information needs, yet digital health platforms often lack tailoring. This study developed and validated computational personas to support a tailored recommender system (Health E-Librarian with Personalized Recommendations [HELPeR]) and address the cold-start problem, in which limited information about new users reduces the system's ability to generate relevant recommendations. MATERIALS AND METHODS: We used a mixed-methods design integrating 4 data sources: (1) online OvCa forum posts (n = 457; 60 users), (2) sem

A harmonized SNOMED Clinical Terms components quality assurance framework.

OBJECTIVES: The objective of this study was to construct a harmonized SNOMED Clinical Terms (SNOMED CT) components quality assurance framework for identifying, categorizing, and assessing data quality issues in SNOMED CT components. MATERIALS AND METHODS: We constructed the framework through an iterative process that analyzed SNOMED CT's core components, synthesized quality factors from prior literature, reviewed inactivation reasons, and SNOMED CT release validation framework, and refined error categories through a targeted review of SNOMED CT error studies. The framework was tested using a

CiteSure: retrieval-augmented large language models for faithful biomedical citation recommendation.

OBJECTIVES: Accurate citation of relevant publications is essential for scientific integrity in biomedical research. Large language models (LLMs) excel at text generation but often hallucinate fabricated or inaccurate citations. Retrieval-augmented generation (RAG) can mitigate these errors, yet current approaches lack semantic precision in evidence retrieval. This study aims to develop a domain-specific RAG system for reliable, context-specific biomedical citation recommendations. MATERIALS AND METHODS: We introduce CiteSure, a sentence-level citation recommendation tool designed to deliver

Five safes task execution service: a standards-based architecture and reference implementation for secure federated analytics across trusted research environments.

OBJECTIVE: Design, implement, and evaluate a standards-based architecture for secure federated analytics across heterogeneous Trusted Research Environments (TREs) that aligns with the Five Safes Framework, without requiring TREs to modify their internal governance, infrastructure, or operational practices. MATERIALS AND METHODS: The Five Safes task execution service (TES) architecture standardizes the format of analysis requests and handles analysis execution and release of outputs consistent with the Five Safes Framework. The Global Alliance for Genomics and Health TES standard is used for s

Adapting machine learning models to temporal drift: oral mucositis prediction in head and neck cancer patients receiving proton and carbon ion therapy.

AIM: Temporal drift, defined as changes over time in underlying data distributions, can degrade the performance of clinical prediction models. In head and neck cancer (HNC) radiotherapy, evolving proton and carbon ion therapies may shift the risk of oral mucositis over time. This study aimed to compare machine learning (ML) strategies for mitigating temporal drift in predicting grade ≥2 oral mucositis among patients treated with particle therapy. METHODS: This retrospective cohort included 1751 adults with HNC treated with particle therapy between May 2015 and December 2022 at a single proton

Augmenting clinical decision-making with large language models: evaluation across general and specialty tasks.

OBJECTIVE: Large language models (LLMs) have shown promise in clinical applications, yet prior studies mainly evaluated their standalone performance on benchmarks or examinations. This study assesses how LLMs support clinicians across specialties, disease contexts, experience levels and decision-making stages. METHODS: We evaluated 3 LLMs (Deepseek-R1, GPT-4o-mini and LLaMA-4) using 2 task types: (1) general-disease tasks spanning specialties and disease incidence levels, and (2) prostate disease scenario covering the clinical workflow, including diagnosis, treatment planning, postoperative r

Evaluation of a clinical decision support design guideline.

OBJECTIVES: Human Factors (HF) principles are essential for safe and effective clinical decision support (CDS), yet existing guidance is fragmented and rarely evaluated in real world settings. A novel, evidence-based, vendor-agnostic HF-informed guideline was developed to address this gap. This study evaluated its perceived usefulness, usability, and impact on CDS design and optimization. MATERIALS AND METHODS: A 2-phase explanatory sequential mixed methods approach was used. Phase 1 involved a survey of guideline users (n = 25) assessing usefulness, ease of use, satisfaction, and influence o

Explainable temporal machine learning of multimorbidity trajectories after acute myocardial infarction: complementing clinical risk scores with mechanistic phenotypes.

OBJECTIVE: This study aimed to identify trajectories of multimorbidity following acute myocardial infarction (AMI), using explainable temporal machine-learning methods, and assess their clinical, prognostic, and biological significance. MATERIALS AND METHODS: Dynamic Time Warping k-means clustering was applied to post-AMI diagnostic sequences from 12 701 UK-Biobank participants. Latent Dirichlet Allocation characterised cluster themes. Multiclass classifiers (CatBoost, XGBoost, random forest, logistic regression) trained on pre-AMI diagnoses and demographics predicted trajectory membership, w

Reasoning or reciting? A temporal contamination audit of large language models in clinical medicine.

OBJECTIVE: Evaluate whether large language models reason or simply regurgitate training data in clinical diagnosis. MATERIALS AND METHODS: We audited 2000 clinical case reports from PubMed Central: 1000 from 2021 to 2022 (within training data) and 1000 from 2025 (after training cutoffs). Five frontier LLMs generated diagnoses evaluated by an independent AI judge validated against physician consensus (n = 10 000 evaluations). RESULTS: Diagnostic accuracy was virtually identical across temporal cohorts (66.8% contaminated vs 66.9% clean), directly contradicting the memorization hypothesis. Lex

A comparison of Fast Healthcare Interoperability Resources and Observational Medical Mutcomes Partnership electronic health record data within the All of Us Research Program.

OBJECTIVE: This study compares the contents of two data standards; the Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR), highlighting their strength and weaknesses and serve as an initial step toward understanding how each standard supports secondary data analysis. MATERIALS AND METHODS: Participant electronic health record data in both OMOP and FHIR formats from the All of Us Research Program (AoURP) were compared, including codeable event volume, healthcare encounters, and person timelines. A phenotype-based assessment was also conducte

TRUST: an large language model-based dialogue system for trauma understanding and structured assessments.

OBJECTIVES: While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue systems for standard diagnostic interviews and assessments. This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior. MATERIALS AND METHODS: We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for post-traumatic stress disorder (PTSD) following the Clinician-Administered PTSD Scale

Guideline-grounded large language models for extracting genome-informed clinical recommendations from electronic health records.

BACKGROUND: Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automated extraction, but their performance in genome-informed clinical contexts remains incompletely characterized. OBJECTIVES: To evaluate guideline-grounded LLM approaches for extracting genome-informed clinical recommendations from EHR notes in the eMERGE study. MATERIALS AND ME