JMIR Medical Informatics

Diagnostic errors, including misdiagnoses and delayed clinical diagnoses, could affect outcomes of a significant patient population, particularly individuals presenting with rare diseases or non-specific symptoms. From rule-based diagnostic decision supporting systems (DDSS) to large language model (LLM) based tools for clinical reasoning have been developed to address these limitations. However, existing DDSS are often proprietary and difficult to integrate, and recent LLM-based tools remain hindered by operational challenges such as cost, resources constraint, and privacy concerns. Moreover, existing systems interpret electronic medical records (EMR) and generate diagnoses separately, limiting continuous evidence-based analysis and imposing repeated clinician involvement. In this paper, we present DDx-Finder, an open-source framework that leverages Model Context Protocol (MCP) servers for direct EMR and literature access, enabling prompt-driven clinical state extraction and reliable case-report re- trieval via generating searching query by LLM, while addressing limitations related to resource demands and privacy concerns. A clinical case study demonstrates the systems feasibility and its potential to provide accessible, transparent, and systematic differential diagnostic support for complex cases.

The openEHR standard provides an open, vendor-neutral architecture for clinical data repositories (CDRs), yet its real-world deployment has not been systematically documented. We conducted a dual-perspective survey combining a vendor survey of openEHR CDR providers with a community survey of openEHR practitioners. Eleven vendor organisations reported deployments across 22 countries and over 100 institutions and health regions. A complementary community survey (n=29, 17 countries) provided context on regulatory environments, adoption drivers, and barriers. Combined, the surveys cover 28 countries, 26 of them with a reported openEHR CDR deployment. Three findings emerge: openEHR has achieved national-scale presence through two distinct channels. Through vendor-market convergence, openEHR-based systems cover the majority of regional health authorities without a national mandate, including 19 of 21 Swedish regions, 3 of 4 Norwegian health regions, and 16 of 21 Finnish wellbeing services counties. Through national health record adoption, governments have built or procured national systems on openEHR as their technical foundation, including Ireland, Malta, Greece, Jamaica and Slovenia. Across Europe, this constitutes an openEHR-based interoperability infrastructure already in place across multiple EU member states. We identified no country in which openEHR is named in binding national regulation, creating structural fragility and an unrealised opportunity for alignment with the European Health Data Space (EHDS). Second, 61% of deployments serve primary use only, and 12% support both primary and secondary use. Third, lack of openEHR-specific knowledge is the most consistent adoption barrier across all geographies and deployment tiers. Adoption is driven by practitioner need and innovation, not by regulatory mandate.

Background: Unstructured free-text narratives in electronic health records (EHRs) contain critical clinical information that is not captured by structured standard codes. In Catalonia, primary care notes follow a semi-structured format known as MEAP (in Catalan). However, extracting structured variables using cloud-hosted commercial large language models (LLMs) raises substantial data privacy concerns and incurs high operational costs. Objective: To develop and validate a privacy-preserving local hybrid pipeline combining a lightweight small language model (SLM) with deterministic regular expressions for extracting six uncoded urinary tract infection (UTI) clinical features (fever, nitrites, leukocytes, lumbar pain, abdominal pain, and haematuria) from primary care EHR narratives. Methods: We deployed the open-weight model (Phi4-mini, 3.8B parameters) within the institutional firewall using Ollama. Prompts were iteratively refined in collaboration with clinicians, and regular expressions were applied post-inference. Validation was conducted across two independent arms: (1) a double-blind clinician gold standard review of 60 real patient records (consensus-resolved cases), and (2) an adversarial synthetic dataset of 720 notes enriched with linguistic noise, generated using GPT-4.1 and Grok-4.1. Point estimates and 95% confidence intervals (CIs) were computed for key performance metrics. Results: The pipeline processed 15,498 MEAP narratives from a matched cohort of 2,962 primary care patients and identified 4,663 clinical feature occurrences. Patients progressing to acute pyelonephritis (cases) presented a higher features burden than non-progressing controls (71.3% vs. 55.8%; standardized mean difference [SMD] = 0.326), particularly for fever (33.0% vs. 9.0%; SMD = 0.616) and lumbar pain (29.0% vs. 9.8%; SMD = 0.502). In real-world clinician validation, overall performance yielded 93.8% accuracy (95% CI 90.6%-96.1%), 83.6% sensitivity (95% CI 73.0%-91.2%), 96.6% specificity (95% CI 93.6%-98.4%), 87.1% positive predictive value (PPV; 95% CI 77.0%-93.9%), and 95.5% negative predictive value (NPV; 95% CI 92.3%-97.7%). In synthetic stress-testing, the pipeline demonstrated 87.2% accuracy (95% CI 84.6%-89.6%), 73.9% sensitivity (95% CI 68.9%-78.5%), near-perfect specificity (99.5%, 95% CI 98.1%-99.9%), and 99.2% PPV (95% CI 97.2%-99.9%). Conclusions: A privacy-preserving local hybrid framework combining a compact open-weight SLM with regular expression rules achieves high specificity and precision for extracting six uncoded clinical features from multilingual primary care narratives. Running entirely within institutional servers, this approach keeps patient data secure, meets privacy standards, and avoids Application Programming Interface (API) costs, making it a practical tool for EHR research, though with lower sensitivity for narratively complex descriptions.

Background: Japan faces critical challenges in medical data interoperability, conceptualized as the "Ten Walls" obstructing the Japanese Health Data Space (JHDS) [1]. The Japanese Intensive Care Patient Database (JIPAD) - Japan's largest national ICU registry with 151 participating facilities - represents a high-quality critical care dataset that remains isolated from international data ecosystems. Objective: To develop a formal mapping of all 122 JIPAD variables to HL7 FHIR R4, characterize the nature and magnitude of semantic gaps, and assess the feasibility of JIPAD integration into the JHDS. Methods: All 122 JIPAD variables (Data Dictionary v3.7.2; Linkage Items List 20231020) were evaluated using ISO 21564 [8]-based semantic equivalence scoring across three tiers: High (direct FHIR R4 Core mapping), Partial (mapping via JP-Core Implementation Guide extensions [3]), and Low/No Equivalence (structural institutional gap). Semantically identical multi-instance fields (e.g., secondary disease codes x5) were consolidated into single mapping entries, yielding 114 mapping entries. Pseudonymization architecture was characterized from primary documentation. Results: Of 114 mapping entries representing the 122 JIPAD variables, 97 (85.1%) achieved High Equivalence via LOINC/SNOMED CT, and 12 (10.5%) achieved Partial Equivalence via JP-Core extensions, value-set translation, or FHIR R4 Core extension mechanisms - yielding a combined technical feasibility of 95.6% (109/114). Only 5 entries (4.4%) were classified as Low/No Equivalence, all attributable to Japan's proprietary disease classification system (288 adult codes; 165 pediatric codes) embedded in the DPC reimbursement framework, plus one Japan-specific procedure (PMX endotoxin adsorption) absent from international terminology systems. Variable-level mapping details are provided in Supplementary Table S1. Critically, JIPAD employs pseudonymization with record-linkage capability, enabling 99% DPC data matching - demonstrating that technical and design-level barriers to FHIR integration have already been resolved. Conclusion: JIPAD is technically and architecturally ready for FHIR integration at a 95.6% level. The remaining 4.4% barrier is exclusively institutional - rooted in MHLW policy frameworks governing the DPC disease classification system [6] - rather than technical. FHIR integration would further unlock pharmacoepidemiological and social epidemiological research currently inaccessible due to data isolation. As the sole national ICU registry providing high-acuity anchor data unavailable in general health records, JIPAD integration is essential for a clinically meaningful JHDS by 2027.

Background: Clinical research often requires reviewing large volumes of unstructured electronic medical record (EMR) data, a time-consuming task demanding skilled personnel. Large language models (LLMs) like ChatGPT can efficiently analyze and summarize text, potentially accelerating chart review research. However, concerns about personal health information (PHI) leakage limit use of commercial chatbots. Secure, in-house LLMs within fire-walled environments can address these concerns. Aim: To develop a user-friendly, scalable application leveraging an in-house ChatGPT 4o-mini on Emplify Health's Azure cloud to automate processing of tabular clinical data while protecting PHI. Methods: The application imports tabular data (Excel or text), guides users through prompt engineering, and automatically submits data row-by-row to the LLM. It retrieves and tabulates results as specified by users, enabling both automated extraction of clinical parameters, and data summarization/interpretation. Results/Conclusions: Initially designed to identify cancer cases and extract related parameters from pathology reports, a fully generalized application was developed which has found utility in analyzing diverse data sources including cancer registries, cardiology CT reports, imaging narratives, and clinical notes. This tool has significantly accelerated research by reducing data retrieval time, allowing staff to focus on higher-value tasks like data analysis, while maintaining PHI protection.

Background: Large language models (LLMs) have demonstrated strong performance on standardized medical examinations, with recent studies reporting performance approaching or exceeding that of senior medical residents. However, examination accuracy alone does not establish how models arrive at their answers or the relative contributions of the clinical vignette, imaging, and answer options to model performance. We evaluated the contribution of these question components to LLM performance on the Orthopaedic In-Training Examination (OITE). Methods: We evaluated three open-source Ministral-3 models (3B, 8B, and 14B) and five proprietary models (Claude Haiku-4.5, Sonnet-4.6, Opus-4.8, GPT-5.6 Luna, and GPT-5.6 Terra) using 792 OITE questions from 2020 through 2024, comprising 434 questions containing clinical images and 358 without images. Question components were sequentially removed across five image-containing and three non-image-containing conditions. Accuracy was summarized using the median and IQR and compared using paired analyses with Holm adjustment. Semantic similarity between model-generated explanations and reference discussions was assessed using BioMedBERT. Results: For image-containing questions, pooled accuracy was 59.13% (58.21-60.08) with complete information and 59.13% (58.18-60.11) without images. Accuracy decreased to 49.05% (48.07-50.00) without the clinical vignette, 45.68% (44.70-46.69) without the vignette and images, and 36.49% (35.51-37.44) when the vignette, images, and question were removed. For non-image-containing questions, accuracy decreased from 72.94% (72.10-73.85) with complete clinical context to 53.53% (52.41-54.68) without the vignette and 37.36% (36.28-38.48) with answer options alone. OpenAI GPT-5.6 Terra achieved the highest full-context accuracy in both image-containing 81.80% (80.41-82.95) and non-image-containing 94.13% (93.30-94.97) questions. Removal of images alone did not significantly affect accuracy for any model, whereas removal of the clinical vignette significantly reduced accuracy for most models. When provided with only the answer options, without the question stem, clinical context, or images, the observed accuracy for every model exceeded the 25% expected from random selection. Specifically, the highest accuracy was 45.62% (44.24-47.24) for Claude Opus 4.8 on questions originally containing images and 46.09% (44.41-47.77) for GPT-5.6 Terra on questions without images. Although closed-source models achieved substantially higher accuracy than open-source models, all models performed above chance when provided answer options alone. BioMedBERT similarity remained high despite substantial differences in accuracy and was not significantly associated with accuracy by Spearman correlation ({rho}=-0.17; P=0.29). Conclusion: Clinical vignettes were the primary contributor to improving LLM performance on OITE whereas images had minimal effect. The high accuracy when only answer options were given suggests the models may be learning spurious correlations. Furthermore, BioMedBERT semantic similarity remained high across models despite wide differences in accuracy and did not consistently distinguish correct from incorrect responses. Thus, high examination performance may not directly reflect clinical reasoning, highlighting the importance of assessing how LLMs use the information provided when interpreting benchmark performance.

Extracting clinical information from Dutch free-text medical notes requires language-specific annotation resources, yet Dutch primary care lacks a reusable event-annotation framework for infections, post-acute infection syndromes (PAIS), and related symptoms. We adapted the COVID-19 Annotated Clinical Text (CACT) framework to Dutch and applied it to GP notes for PAIS event extraction. The framework has three annotation layers: a DiagnosticExpression typology covering acute infections, post-acute syndromes, and relevant comorbidities; an eleven-subtype Evidence inventory grounded in Dutch primary-care testing practice; and explicit decision rules for the SOEP structure of Dutch general practitioner (GP) notes (Subjective, Objective, Evaluation, Plan), including the distinction between clinician hedging and patient-side hypotheticals. On a 200-note pilot, span-level F1 under the Lybarger criterion reached 0.51 [95% CI: 0.47, 0.55] across six core entities; restricted to spans both annotators noticed, conditional F1 reached 0.78 [0.75, 0.80], indicating that most disagreement stems from annotation coverage rather than label assignment. The adaptation illustrates how an English event-based clinical annotation framework can be extended to a new language and clinical setting, yielding a reusable resource for Dutch clinical NLP; which steps generalise beyond this case (CACT to Dutch primary care) and which are specific to Dutch or PAIS remain to be tested.

Introduction: Rare and heterogeneous disease research increasingly relies on privacy-enhancing technologies such as federated learning (FL) to enable cross-institutional collaboration across fragmented datasets. However, data quality assurance in FL can be limited, as individual-level data may not be directly accessible. While schema-dependent inspection offers a partial solution, it requires standardised schemas or resource-intensive frameworks, hindering scalability and collaboration. To address these challenges, we implemented two complementary dashboards and evaluated their interplay. Methodology: We adapted a recognised data quality control framework for re-use of electronic health record (EHR) data using breast cancer records from Centre Leon Berard into two dashboards: (1) an in-house dashboard for intra-clinic quality assessment, and (2) a federated dashboard for inter-clinic quality assessment. Artificial inconsistencies were introduced into distributed datasets mirroring the in-house source to evaluate detection capabilities. Results: The in-house dashboard provided granularity and reliability, pinpointing individual-level inconsistencies, while the federated dashboard enabled cross-institutional pattern detection - trade-offs inherent to their designs. The federated system revealed ecosystem-wide trends inaccessible to single-institution tools, whereas the in-house dashboard provided local validation and thoroughness. Discussion: Our findings confirm a complementary relationship: FL dashboards provide scalable, collaborative oversight but may require additional quality checks, while in-house tools ensure thoroughness at the cost of scalability. A combined model seemingly offers the optimal balance, accommodating both institution-specific needs and collaborative research requirements in evolving, multi-institutional ecosystems.

Objectives: Searching a reference terminology such as SNOMED CT confronts two retrieval technologies of fundamentally opposite nature. Deterministic lexical matching is precise, order-independent and character-level, and lets a clinician refine a query incrementally, but it is confined to the wording and language of the curated descriptions. Learned semantic matching bridges paraphrase and language, but poorly represents abbreviations and partial, as-typed input. Their requirements and failure modes are so different that combining them naively can let the channel that is wrong for a given query degrade the one that is right. The conventional way to avoid adding a semantic channel at all has been to strengthen the lexical one (curating an additional local interface vocabulary so that more expressions match by surface form), but such vocabularies are labour-intensive to maintain and their quality degrades over time. We ask whether these two opposite paradigms can instead be made to *coexist* in one retrieval flow (each serving the input regime it is strong at, neither harming the other) over SNOMED CT's own curated descriptions, and study the query interpretation, rank fusion and re-ranking design that makes this coexistence safe. Materials and Methods: We implemented one reproducible instance of this architecture over the SNOMED CT International edition. It combines deterministic, order-independent multi-prefix search with three learned components: BioLORD-2023-M embeddings for semantic retrieval, an optional BGE cross-encoder for re-ranking, and a local LLM (gemma 4) for query normalization and structured clinical entity extraction. Lexical and semantic rankings are combined with Reciprocal Rank Fusion, and results can be constrained with a hierarchy (descendant) filter. As an external evaluation we measured search-only linking accuracy on 542 disease mentions from the DisTEMIST corpus (Spanish, zero-shot), resolving gold codes through SNOMED CT historical associations and ablating query pre-processing, re-ranking, and each retrieval channel in isolation; and, on a second corpus of English real-EHR discharge notes (the SNOMED CT Entity Linking Challenge, 12,897 mentions), we estimated field-scoped typeahead performance and tested a context-aware pre-process. Results: Illustrative tests showed complementary behaviour: the lexical and semantic paths resolved different query types, while LLM pre-processing supported cross-lingual and abbreviated input. On a search-only linking evaluation over 542 disease mentions from the DisTEMIST corpus (Spanish, zero-shot), the best and computationally cheapest configuration, semantic search with re-ranking and *no* LLM query pre-processing, placed the exact concept first for 60% of mentions (accuracy@1 = 0.60) and within the top-10 candidates a picker would show for 80% (recall@10 = 0.80). Because these search-only figures are computed on gold mention spans, they isolate normalization from mention detection and are not directly comparable to the DisTEMIST shared task's end-to-end scores. LLM query pre-processing helped dirty, lay input but slightly hurt clean terminology-like queries, and a near-miss analysis showed that many remaining cases retrieved a parent or child of the intended concept. Isolating the channels showed the two coexist without a trade-off: on this clean-normalization corpus the semantic channel carried the accuracy and adding the lexical channel neither helped nor harmed it (accuracy@1 0.60 vs 0.61), because rank fusion followed by re-ranking suppresses whichever channel is off-task; the lexical channel's own strength, partial as-typed input, is a regime this corpus does not test. On the English real-EHR corpus, a field-scoped typeahead (each query constrained to its entry field's domain) placed the concept on the top-10 picker list for 73% of 12,897 mentions across all clinical domains (accuracy@1 0.52; findings 0.54, close to DisTEMIST). Most residual errors were soft (the gold on the list but not first, or a hierarchical neighbour); the hard misses were dominated by ambiguous EHR shorthand, and a single context-aware pre-processing call (normalizing a mention with its surrounding note text) roughly doubled recovery of those hard misses, though, as with LLM normalization on the clean terms of DisTEMIST, applying it to every mention slightly lowered overall accuracy by perturbing the easy majority, so it is best used as a selective rescue. Results are specific to the evaluated configuration, not an architecture-independent estimate. Discussion: The findings support the technical feasibility of a reference architecture that computes additional access routes over SNOMED CT's curated descriptions, and indicate that making the lexical and semantic paradigms *coexist*, rather than choosing between them, is what makes those computed routes safe to add. This shifts rather than eliminates maintenance, from local phrase authoring toward governance and validation of retrieval services. Conclusion: Hybrid concept retrieval lets lexical precision and semantic flexibility coexist over SNOMED CT's maintained descriptions without a trade-off between them, offering an implementation pattern that may reduce additional local lexical curation where direct search is appropriate, while curated interface vocabularies remain useful in selected guided workflows.

Haemoglobinopathy registries support research and outcome monitoring. Still, reuse is limited due to heterogeneous structures, registry-specific coding, and incomplete semantic representation. We designed and implemented a FAIRification workflow for the INHERENT haemoglobinopathy platform, an international genotype-phenotype registry, as part of the HemaFAIR project. The workflow was extended to the Cyprus Haemoglobinopathy Patient Registry to demonstrate its applicability across a second registry. Source data and metadata were transformed through two independent but complementary harmonisation branches executed in parallel: one producing an OMOP CDM representation, and the other generating a CARE-SM representation. The workflow generated graph-based semantic resources, predefined query services, public aggregate dashboards, and application programming interface (API) endpoints. Registry metadata were published through the European Rare Disease Registry Infrastructure and a FAIR Data Point. These outputs support findability, interoperable analysis and controlled reuse while preserving existing governance over patient-level data.

Background: Digital systems are central to regulating access to care in Brazil's Unified Health System (SUS), but fragmented data can hinder coordination. Objective: This study examines the database structure of SUSfacilMG and identifies modernization priorities, using CORE Saude MG as a comparative reference. Methods: We assessed metadata from 1,586 SUSfacilMG tables, focusing on naming patterns, relationships, and repeated attributes. We then compared architectural features described for CORE Saude MG. Results: Approximately 40% of the assessed tables repeated descriptive attributes, including patient identifiers, suggesting substantial duplication. Metadata alone cannot establish operational failures, security incidents, or effects on care. Conclusion: A phased review of data models, interfaces, and governance is warranted. Standardized exchange, referential integrity, and controlled access should be evaluated alongside service continuity and data quality.

Objective: Retrospective evaluation of clinical pathway adherence using electronic medical record (eMR) data is limited by heterogeneous data structures and statistical measures that do not account for patient-specific context, care sequence, or timing. We designed a modular event-based architecture to dynamically evaluate the New South Wales Adult Sepsis Pathway over time. Method: Data from eMRs were collected from adult emergency department encounters across four hospitals between July 2022 and June 2024 and processed through three sequential layers: event-stream transformation, pathway-relevance filtering, and state-based pathway evaluation. Heterogeneous records were standardized and organized into a chronological timeline for each encounter. The framework then updated the pathway state as new clinical information became available and assessed whether recommended care was delivered within the relevant timeframe and sequence. Result: The architecture processed 451,124 encounters. This included 57,631,084 clinical events extracted and transformed into encounter-level event streams. After filtering for pathway relevance, 43,951,146 events remained (76.3%). A total of 129,907 (34.6%) encounters entered the sepsis pathway, 50,118 (38.6%) entered YELLOW state, indicating progression to suspected sepsis, and 3,349 (2.6%) encounters progressed to RED state from GREEN, indicating the initial shock. Of 50,118 encounters entering YELLOW state, 2,426 (4.8%) progressed to shock, 8,700 (17.4%) encounters were recognized with sepsis through a blood culture order, and 4,287 (8.6%) encounters were recognized with sepsis through an antibiotic order. This demonstrates the ability of the architecture to prioritize higher-acuity states during evaluation rather than going through an intermediate state. Conclusion: The architecture developed shows the feasibility of transforming fragmented routine eMR data into a reusable longitudinal representation that supports context-dependent pathway evaluation. Separating data preparation from pathway rules improves transparency, maintainability, and adaptability and provides a foundation for future interoperable, near-real-time pathway monitoring

Objectives Transformer models for electronic health records require converting clinical data into token sequences, however standardized tokenization and evaluation frameworks are lacking. We introduce DBToken, an open-source library, and bits-per-row (BPR), a metric for comparing tokenization strategies. Materials and Methods DBToken accepts Medical Event Data Standard (MEDS)-compatible input and supports multiple text, numeric, and temporal tokenization strategies. BPR extends the bits-per-byte metric used in language models to enable comparison across tokenization strategies. Results DBToken efficiently tokenized data across configurations. BPR identified the vocabulary size associated with the best clinical outcome performance and localized differences in numeric tokenization performance by token class. Discussion Optimal tokenization strategies for medical foundation models are a subject of active research. DBToken enables reproducible tokenization experiments, while BPR efficiently screens vocabulary sizes and numeric representations before downstream evaluation. Conclusion DBToken and the BPR metric provide open-source infrastructure for reproducible EHR tokenization and cross-strategy evaluation.

Background International Classification of Diseases (ICD) codes are widely used for reimbursement, health-service planning, and disease surveillance. Automated systems can identify diagnoses and supporting evidence across clinical documents. Yet coding is more than finding diagnoses. Decisions based on isolated information may not fully reflect the evolving clinical picture during admission. Provisional, superseded, or conflicting diagnoses can therefore persist in the final code set, complicating review and correction. We therefore developed and evaluated Clinico, a coding agent that organises evidence by time and clinical relationships to adjudicate diagnoses and update their status during admission. Methods We did a retrospective multicentre study with internal and external validation using inpatient records from two Shanghai hospitals. Routine coder-finalised codes served as references. Using 5,000 development stays, we refined Clinico's prompts and workflow without fine-tuning model parameters. We compared Clinico with three prespecified comparators, two trained on 80,340 stays. Clinico maintained an evidence-linked diagnostic ledger across each admission and reconciled related or conflicting claims before finalising the code set and selecting the principal diagnosis. The primary outcome was principal-diagnosis exact-match agreement; complete code-set micro-F1 was the key secondary outcome. Findings Evaluation included 5,000 internal and 4,109 external test stays. Principal-diagnosis exact-match agreement was 83.6% (95% CI 82.6-84.6) internally and 52.0% (50.5-53.5) externally; the next-highest observed estimates were 74.7% and 31.5%. Complete code-set micro-F1 was 90.6% (95% CI 90.2-91.0) internally and 42.6% (42.0-43.2) externally; the next-highest observed estimates were 90.3% and 25.5%. Interpretation In both hospital test sets, Clinico had the highest observed estimates of principal-diagnosis agreement and complete code-set micro-F1 among evaluated methods. Its longitudinal adjudication process links decisions to source evidence, allowing them to be checked against the record. Prospective studies should assess its effects in clinical workflows using independently adjudicated references. Funding None.

Background Chronic postsurgical pain (CPSP) affects between 7-23% and 13-44% of patients after hip and knee arthroplasty, respectively. Standardised methods of pain assessment provide superior evaluation of pain, including the Oxford Joint Score Pain Subscale (OJS-PS). We aim to estimate the proportion of patients with a phenotype consistent with CPSP through a k-medoids clustering technique and identify a threshold on the OJS-PS to highlight such patients at a population level. Methods In this cross-sectional study Patient Reported Outcomes Measures data 6-months after hip and knee arthroplasty from 2017 to 2025 were examined. An adapted k-medoid clustering technique utilising subsampling, batch assignment and probabilistic consensus allocated clusters. A receiver operator characteristic analysis identified a threshold on the OJS-PS noting the lowest scoring cluster. Our categorisation was compared to self-reported severe or moderate pain; sensitivity, specificity and accuracy of this categorisation were calculated. Results We analysed 109,542 hip and 113,799 knee arthroplasty patients; three clusters were used in each analysis. After hip arthroplasty: 14.4% of patients were assigned to the cluster with the lowest median OJS-PS of 11 [IQR 8 - 13]. A threshold of 15.5 classified patients as severe or moderate pain with 60.6% sensitivity, 91.0% specificity and 85.7% accuracy. Similarly, after knee arthroplasty, 25.3% were assigned to the cluster with the lowest median OJS-PS of 14 [IQR 11 - 16]. A threshold of 18.5 on the OJS-PS had an 85.4% sensitivity, 88.4% specificity and 87.8% accuracy for classifying patients with self-reported severe or moderate pain. Conclusions This robust and scalable clustering technique on ordinal clinical data estimates the proportion of patients reporting a phenotype consistent with CPSP. On a population level the thresholds identified on the OJS-PS could aid screening for potential CPSP patients 6 months after hip and knee arthroplasties.

Aims To develop a 'core' dataset of diabetes related variables to support reproducible research using UK routinely collected health data. Methods A workshop was conducted bringing together diabetes healthcare professionals, researchers, and patient and public representatives to discuss and prioritise variables for inclusion in the Diabetes Core Dataset. Core variables were those considered to be highest priority for diabetes research and available at high quality in NHS data routinely used for research (primary care [GP] and Hospital Episode Statistics [HES] data). Candidate variables for inclusion in the Diabetes Core Dataset were from a review of existing core datasets and expert opinion. Participants scored variables anonymously based on priority for diabetes research. Results 25 variables from existing diabetes core datasets and 87 other candidate variables were considered for inclusion in the Diabetes Core Dataset. All 25 of those from existing diabetes core datasets and 5 of the 87 candidate variables met the core requirements for inclusion. In addition, 7 variables were identified as high priority but not included in the core dataset as they are not currently available in GP/HES data; these were labelled as 'future high priority' variables for diabetes research. Conclusions A new diabetes core dataset for UK EHR research has been developed using a consensus-based process. The core dataset is openly available and can be flexibly applied in UK EHR (https://healthdatagateway.org/en/tool/426), including in new NHS Research Secure Data Environment platforms, to enhance reproducible research to improve the clinical care of people with diabetes and associated conditions.

As the use of electronic health records in drug repositioning research increases, so does the need for a well-curated resource describing real-world drug-diagnosis relationships. This need is particularly important in the context of polypharmacy. Although literature-based drug-disease maps exist, they are typically based on mechanistic disease ontologies, which are not widely used in clinical settings and do not align well with the ICD system, which is most often used in healthcare. Here, we used real-world primary and secondary healthcare data from approximately 1.5 million individuals to identify drug-diagnosis co-occurrences (~736,000 pairs), significant associations (7,763 pairs), and assess direct drug usage through medical expert curation. The final resource comprises 7,763 associations with odds ratios [≥]3.5, manually annotated by six independent clinicians (3 in the UK and 3 in Denmark). Finally, the clinician annotations were scored using an Expectation-Maximization-based framework providing a confidence score for each pair. Our resource shows that the vast majority of significantly associated drugs and diagnoses in healthcare records are not due to direct treatment of the diagnosis. Additionally, through the annotation results, we demonstrate the importance of accounting for systematic differences among annotators when working with real-world data.

Background. Health systems answer most questions by having expert analysts hand-write queries against a complex electronic health record data warehouse, a slow, resource-intensive process. Whether an autonomous coding agent can do this accurately is unknown. Methods. In a single-center quality-improvement evaluation, we posed ten questions about a common pediatric infection, acute otitis media. The questions went to analysts, whose adjudicated answers were the reference, and to an autonomous coding agent (OpenAI Codex), which wrote and ran read-only queries on a full copy of the production data warehouse (Epic Caboodle). We ran the agent under four conditions: an autonomous baseline (each question answered three times at two reasoning-effort settings), a variant in which it listed its assumptions, interactive analyst feedback, and reuse of a corrected definition across related questions. Outcomes were accuracy, patient-level agreement (F1), reproducibility, and cost. Results. Working autonomously, the agent wrote valid queries and never fabricated data, but rarely produced the exact answer. At medium effort, it came within 5% of the reference on 27 of 30 runs but exact on only 10. Higher effort produced no improvement. Reproducibility was the greater weakness, with the three runs returning an identical answer on only 3 of 10 questions. When a run matched the reference, it had found the same patients (F1 = 1.00); the exception was an over-counted procedure (F1 = 0.72). With analyst feedback on four questions, it answered two exactly, and reusing the corrected definition on related questions restored reproducibility and accuracy. Conclusions. A general-purpose coding agent matched an adjudicated analyst reference on most routine questions but not reproducibly, because key definitions depended on warehouse knowledge that the data dictionary omits. Supplying that knowledge, by prompt or feedback, restored reproducibility. Under expert analyst supervision, the agent is already a capable drafting aid and a promising step toward broader hospital analytic support.

Background: Public facing large language models (LLMs) are increasingly used for health guidance, including triage recommendations. We evaluated whether augmenting LLM prompts with standardized clinical concepts from the Unified Medical Language System (UMLS) could improve the safety and robustness of clinical triage recommendations. Methods: We used a publicly available dataset comprising 60 clinician-authored clinical vignettes, each represented in 16 demographic and narrative variations, yielding 960 vignette-factor combinations. Clinical entities were extracted using a two-stage pipeline combining ClinicalBERT-based named entity recognition with rule-based identification of laboratory abnormalities. Extracted entities were mapped to UMLS Concept Unique Identifiers (CUIs). Negated concepts were excluded. A confidence-weighted CUI voting classifier was trained using empirical associations between CUIs and clinician-assigned triage categories. We compared five approaches: CUI-only classification, MedGemma 27B, MedGemma 27B augmented with CUIs, GPT-4o-mini, and GPT-4o-mini augmented with CUIs. Outcomes included overall accuracy, under-triage, over-triage, emergency-case accuracy, and sensitivity to anchoring statements. Results: CUI augmentation decreased under-triage but increased over-triage in both models tested (GPT-4o-mini and MedGemma 27B). It improved high-acuity recognition while reducing recognition of low-acuity cases. CUI augmentation had mixed effects on overall triage accuracy; accuracy increased for MedGemma 27B but decreased for GPT-4o-mini. Emergency-case accuracy improved from 73.0% to 80.7% for GPT-4o-mini and from 60.5% to 68.5% for MedGemma 27B. CUI augmentation also reduced susceptibility to anchoring statements. These findings suggest that the principal value of CUI augmentation may be shifting model behavior toward safety-oriented behavior rather than uniformly improving overall accuracy. Conclusion: Ontology-grounded prompt augmentation shifted LLM triage recommendations toward greater sensitivity to high-acuity presentations and reduced overall under-triage. These safety gains were accompanied by increased over-triage and mixed effects on overall accuracy. A hybrid architecture combining LLM-based language understanding with interpretable UMLS-derived clinical concepts may improve the safety and robustness of AI-assisted triage. Further evaluation using real-world patient communications and clinical outcomes is warranted.

AO_SCPLOWBSTRACTC_SCPLOWGetting clinical data from different sources to "talk" to each other within the OMOP Common Data Model (CDM) is arguably the most tedious part of multi-center research. While this integration is essential, the transformation process is frequently a manual grind, requiring a rare overlap of deep clinical knowledge and technical expertise. In this paper, we present a framework designed to alleviate some of the burden on the researcher by automating data harmonization through two distinct steps: structural schema mapping and terminological standardization. For the structural piece, we moved away from "black box" logic in favor of a stateful workflow managed by large language models (LLMs) and directed acyclic graphs. By profiling EHR data at the source, our system generates context-aware dictionaries that offer ranked mapping suggestions alongside confidence scores. While our benchmarking showed a 97.5% agreement rate at the schema level and an 84% agreement rate at the value level when compared with human experts, the system appears most effective when treated as a "co-pilot" rather than a total replacement for human oversight. To handle value-level standardization, we implemented a hybrid search strategy that pairs the semantic depth of SapBERT embeddings with the literal precision of fuzzy string matching. By using FAISS for rapid similarity retrieval, the engine attempts to resolve messy or "noisy" clinical descriptions to standard OMOP concepts. This approach seems particularly promising for handling the non-standardized labels that often plague smaller, local datasets. Ultimately, our results suggest that this guided approach can shift the timeline for OHDSI-compliant warehousing from weeks of manual curation to a more manageable and scalable pipeline, potentially lowering the barrier to entry for smaller research teams.