JMIR Medical Informatics

ObjectivesCase reports and case series comprise a significant portion of the biomedical literature, yet unlike case reports, the National Library of Medicine does not index case series as a Publication Type. This hurts clinicians and researchers ability to retrieve, identify and analyze evidence from this type of study. Materials and MethodsPubMed articles mentioning "case series" in title or abstract were characterized to learn what are considered to be case series by the authors themselves. W...

Certain diseases require rapid treatment to avoid long-term consequences for patients. However, they may be difficult to recognize, especially if the symptoms are ambiguous and compatible with multiple possible diagnoses. Completing all necessary examinations often takes time, thereby prolonging patient suffering. Data-driven approaches, such as single-label classification (SLC) and multi-label classification (MLC), can help accelerate the diagnostic process and improve accuracy. These two appro...

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...

BackgroundIn-hospital cardiac arrest on general wards is often preceded by detectable physiological deterioration, yet conventional early warning scores demonstrate limited discrimination. We developed and performed preliminary validation of a transformer-based cardiac arrest prediction system for general ward patients. MethodsThis retrospective study was conducted among general ward patients at a tertiary academic hospital in South Korea (Severance Hospital, 2013-2017). We developed Cardiac Ar...

IMPORTANCEAlthough angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are recommended for people with chronic kidney disease (CKD), they remain underused. Barriers to adherence, such as adverse effects or patient refusal, are frequently embedded within unstructured clinical narratives and are therefore inaccessible to structured data analytics. Scalable natural language processing (NLP) approaches are needed to identify these barriers and support guideline-...

BackgroundRare neuromuscular diseases such as polyneuropathy (PN) and myopathy (MY) often share symptomatic characteristics, leading to diagnostic challenges and delays. Machine learning applied to routine care data of electronic health records (EHRs) offers the potential for accelerating accurate diagnosis. ObjectiveTo develop and evaluate machine learning models to distinguish between patients with PN and MY using EHR data, as a step toward tools that could support improved diagnostic process...

ObjectiveTo comprehensively evaluate the validity of ICD-10-CM codes for both prevalent diagnoses and less common diseases, and to assess the performance of a large language model (LLM)-based system in validating these codes. Materials and MethodsThis retrospective study analyzed hospital admissions from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We developed a validated LLM-based system using GPT-4o, refined through iterative prompt engineering, to assess ICD-10-CM co...

Rationale, Aims and ObjectivesUnwarranted clinical variation (UCV) in patient care often arises from contextual factors and contributes to increased costs, unnecessary treatments, and deviations from evidence-based practice. Detecting UCV is challenging due to the complexity of care decisions. Current approaches rely on centralized data aggregation and mixed-effects regression, which estimate relative variation but cannot detect absolute variation. Moreover, machine learning (ML) methods leverag...

ObjectiveTo systematically identify and characterize methodological heterogeneity in sepsis case detection methods using the MIMIC-III database or the eICU-CRD, and to quantify the resulting variability in sepsis detection rates. Materials and MethodsWe conducted a PRISMA-guided systematic review of PubMed and Web of Science (2016-2024), and stratified studies by cohort definition to obtain comparable subsets. We extracted information on sepsis case detection methodology across six domains: par...

BackgroundIntegrating advanced artificial intelligence (AI) into clinical decision-support often requires the sharing of sensitive patient data with external services, raising privacy concerns. Homomorphic encryption (HE) allows computing directly on encrypted data, without revealing the underlying patient information. ObjectivesTo develop a large language model (LLM)-assisted diagnosis framework while preserving patient privacy in the clinical text analysis, by leveraging HE and using rare dis...

Urinary tract infections (UTIs) represent a substantial burden in emergency department (ED) settings, where diagnostic delays and the limitations of traditional clinical assessments often result in suboptimal treatment decisions. This study develops an interpretable machine learning framework to enhance real-time UTI prediction accuracy. We analyzed a retrospective dataset of 80,387 ED patient encounters from four institutions (2013-2016), encompassing 220 clinical variables. Four machine learn...

The fragmentation of medical imaging data across isolated Picture Archiving and Communication Systems (PACS) creates significant barriers to interoperability. This paper presents a functional Proof of Concept (PoC) for a decentralized, patient-centric medical image exchange system. By combining an Ethereum-based smart contract layer for access control and settlement with an off-chain Node.js "Worker" bridge, the system enables automated, peer-to-peer transfer of DICOM studies between disparate O...

PurposeNatural Language Processing (NLP) has the potential to extract structured clinical knowledge from unstructured Electronic Health Records (EHRs). However, the limited availability of annotated datasets for algorithm training restricts its application in clinical practice. This study investigates the use of transformer-based NLP models to structure Italian EHRs in cardiac settings, addressing this gap. MethodsWe implemented and evaluated three named entity recognition algorithms: SpaCy, Fl...

BackgroundHeart failure (HF), including heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), remains a major global health challenge, particularly among aging populations. Timely and accurate prediction of severe adverse outcomes associated with HF is critical for optimizing care, reducing disease burden, and improving outcomes. Although social determinants of health (SDoH) have been recognized as key drivers of HF disparities and assoc...

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...

BackgroundNursing documentation patterns may reflect patient acuity and clinical deterioration, yet their prognostic value remains underexplored. We developed the Intensive Documentation Index (IDI), a novel framework quantifying temporal documentation rhythms, and evaluated its ability to enhance ICU mortality prediction.58 MethodsWe analyzed 26,153 ICU admissions of heart failure patients from the MIMIC-IV database (2008-2019). Nine IDI features capturing documentation rhythm, volume, and sur...

BackgroundTuberculosis (TB) remains a major public health challenge in Nepal, with incidence rates substantially higher than global estimates. Accurate forecasting of TB incidence is essential for early warning systems, resource allocation, and targeted interventions. This study aimed to develop and validate a hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA) and Convolutional Neural Network Auto-Regressive (CNNAR) model for TB incidence forecasting in Nepal. MethodsMonthly TB i...

BackgroundSepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths annually. Natural language processing (NLP)-driven clinical decision support systems (CDSS) have been deployed in several NHS Trusts to enable automated early detection of sepsis from unstructured clinical notes, yet causal evidence of their effectiveness at the hospital level remains limited. ObjectiveTo estimate the causal effect of implementing N...

ObjectiveTo develop and content-validate a brief, expert-informed Survey for Human-AI Performance Evaluation (SHAPE-AI) for near-real-time assessment of how clinical AI affects human performance. BackgroundAI-enabled clinical decision support can improve outcomes only when aligned with clinician workflows, and cognitive demands. Existing evaluations measure technical performance and adoption, providing limited assessment of how AI shapes human performance. There is a lack of concise, operationa...

"Black box" deep learning models for medical image interpretation limit clinical trust and analysis of performance degradation. Here, we introduce Concept-Level Embeddings for Auditable Radiology (CLEAR), an auditable foundation model based on clinical concepts. Trained on over 0.87 million image-report pairs from 239,091 patients, CLEAR learns a visual representation and projects chest X-rays into a semantically rich space defined by large language model embeddings, making every prediction trac...