JAMIA Open

BackgroundExisting information resources about medicines and their indications have limited usefulness for health data analytics. The emerging potential of large language models (LLMs) to generate clinically accurate responses presents a novel opportunity to develop a comprehensive knowledge base of medicines and their clinical indications. MethodUnique medications from the English Prescribing Dataset (EPD) were extracted and included in a fine-tuned prompt pipeline using the GPT-4 and MedCAT L...

Medication product names in Swiss electronic health records are heterogeneous and often encode multiple attributes (e.g., ingredient, strength, dose form, packaging) in German free text. This limits interoperability and reduces the utility of ATC codes, which do not uniquely identify products. We compared two workflows for mapping Swiss medication products to RxNorm and RxNorm Extension: (i) an Observational Health Data Sciences and Informatics (OHDSI) USAGI workflow with lexical similarity and ...

We describe a new custom feature within our Epic Systems electronic health record (EHR) that automates stratified randomization at the point-of-care or order. As a demonstration use-case, we conducted a randomized trial of a provider-facing alert for short-interval HbA1c orders. Over 3 months the alert dramatically reduced repeat orders. This transportable clinical informatics application transforms health systems ability to conduct pragmatic clinical trials and deliver clinical care within the ...

We present ARCADE (Adversarial Critique Architecture for Document Evaluation), a multi-agent architecture addressing three limitations of traditional retrieval-augmented generation for automated document analysis: incomplete information extraction, shallow analytical depth, and framework paraphrasing. We compared ARCADE against Single-Pass RAG using 95 policy documents (50 National Cancer Control Plans and 45 Cardiovascular Disease plans) evaluated on 36 metrics across six capabilities: Natural ...

BackgroundAlbuminuria is associated with increased risk of cardiovascular disease (CVD), heart failure, and progression of chronic kidney disease (CKD). Early detection of albuminuria, done through spot urine albumin creatinine ratio (UACR) testing, enables more accurate risk stratification and timely use of preventative therapies. It remains unacceptably low in the hypertension population. MethodsWe evaluated two EHR-embedded clinical decision support (CDS) strategies at Geisinger Health Syste...

Long-term adherence to prescribed therapies remains a persistent challenge in chronic and ultra-rare conditions where clinical outcomes depend on continuous medication use. Even brief gaps in therapy can compromise disease control, yet patients frequently encounter structural barriers including high out-of-pocket costs, prior-authorization (PA) delays, annual re-verification cycles, and refill logistics that disrupt persistence. This study evaluates a patient-centric Markov-chain framework for a...

PurposeStudies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes in EHR notes. The purpose of this study was to assess two natural language processing (NLP) tools to extract phenotypes from unstructured EHR notes, and to evaluate the added value of integrating NLP-derived phenotypes with structured EHR data at a health system scale. MethodsThis retrospective study is based on inpatient and outpatient EHR data fro...

BackgroundInterprofessional teams are central to high quality patient care. However, identifying the clinician primarily responsible for a patient requires labor-intensive methodologies. Although electronic health record (EHR) audit logs offer a scalable alternative, its use for identifying frontline clinicians is underdeveloped. ObjectiveTo develop and validate an algorithm utilizing EHR audit logs to identify the primary frontline clinician per patient day of an encounter and to describe care...

The emergence of Janus kinase (JAK) inhibitors, a relatively new class of medications for autoimmune and inflammatory conditions, has been accompanied by reports of adverse effects observed during clinical trials. However, uncertainty over their safety and efficacy in wider, unselected populations has led to discussion and speculation on social media such as Reddit. Social networks represent a novel, rich source of real-world pharmacovigilance data. They are also an environment where unverified ...

Acquiring insights from electronic health records (EHRs) is slowed by manual analytical workflows that limit scalability and reproducibility. We present LATCH (LLM-Assisted Testing of Clinical Hypotheses), an agentic framework that converts natural language clinical hypotheses into fully auditable analyses on structured EHR data. LATCH integrates LLM-assisted semantic layers with deterministic execution pipelines to automate cohort construction, statistical analysis, and result reporting, while ...

ObjectiveSystematic clinical phenotyping using Human Phenotype Ontology (HPO) is central to rare disease diagnosis. However, current disease prioritization (ranking candidate diseases from HPO for a patient) methods face key challenges: they often fail to account for the hierarchical structure of HPO terms, ignore dependencies among correlated terms, and do not adjust for batch effects arising from systematic differences in phenotype documentation across cohorts, institutions, or clinicians. We ...

BackgroundThe accuracy and safety of generating medication orders by large language models (LLMs) must be demonstrated. Without standardization, performance evaluation is limited to time and resource-intensive clinician grading. This evaluation aimed to develop a standardized medication format that supports automated performance evaluation (MedMatch). MethodsFirst, a survey of 40 medication prompts was given to clinicians to assess agreement in medication order communication. Second, a clinicia...

The validation of methods is an integral part of statistical research, defining conditions under which methods can yield reliable results. When validation is carried out empirically, it requires a solid data basis that allows the control and management of relevant characteristics. In particular, depending on the context, the data must meet specific requirements regarding, e. g. sample size, dimensionality, completeness and underlying dependency structures. Real-world data often fails to meet the...

While Americans are using herbal dietary supplements (natural products) more than ever, the consumption of natural products with prescription drugs can lead to harmful interactions. Pharmacovigilance of natural products depends on careful expert review and interpretation of a wide variety of evidence. In prior work, we demonstrated the value of knowledge graph (NP-KG) for assisting with natural product safety investigations. However, scaling the NP-KG from 33 natural products to the thousands on...

Large language models (LLMs) have shown incredible promise in medicine. While LLMs may be particularly useful in areas requiring extensive review of clinical records, their use remains limited due to their tendency to hallucinate and fabricate information. Hallucination issues, as well as their consequences, are exacerbated in low-probability, high-stakes scenarios such as rare adverse safety events or medical errors. We present SAFE-AI (Structured and Automated Framework for Explainable AI), a ...

BackgroundThe use of large language models (LLMs) is increasing in the medical field; however, LLMs are often subject to "confabulations." Notably, LLMs have vulnerability to adversarial attacks, or fabricated details within prompts, which is concerning given both health misinformation and inadvertent errors in the medical record. This purpose of this study was to determine the effect of adversarial attacks by embedding one fabricated medication into a list of existing medicines. MethodsA total...

The Health Insurance Review and Assessment Service Korean Nationwide Claims OMOP-CDM database (HIRA K-OMOP) is a nationwide data resource formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and derived from South Koreas National Health Insurance claims data. It includes patient-level information and insurance claims for the entire population of South Korea from 2015 to 2024, providing population-scale coverage (56,416,773 patients). The HIRA K-OMO...

In pharmacovigilance, analyzing drug safety cases is often time consuming due to the abundance of laboratory data, complex medical histories, and intricate temporal relationships. Agentic AI systems can significantly reduce case processing time by assisting medical reviewers in surfacing clinically relevant evidence. However, previous studies have highlighted that large language models alone lack causal reasoning and evidence-based interpretability. To address these limitations, we present a kn...

BackgroundTraditional pharmacovigilance methods based on biostatistical approaches systematically exclude outliers and rare events, potentially missing critical safety signals. These methods fail to detect micro-clusters of adverse events and comorbidity patterns that may indicate serious but low-frequency adverse drug reactions (ADRs). We introduce the concept of absurdity signal detection - the identification of statistically anomalous but clinically significant adverse event patterns that co...

Clinical decision-making for multi-morbid patients requires synthesizing evidence from lengthy, fragmented records--a task that exposes the limitations of standard Retrieval-Augmented Generation (RAG) and long-context Large Language Models (LLMs), which often lose critical information or lack auditability. We introduce the Clinical-Recursive Language Model (C-RLM), a framework that reframes evidence synthesis as a structured, recursive compilation process rather than a single-pass retrieval task...