Knowledge Tree
electronic health records
Also known as: EMR, electronic health records, electronic medical records, electronic health record systems, electronic health records (EHRs), medical records, electronic health record, Electronic Health Record, EHR
Facts (21)
Sources
A framework to assess clinical safety and hallucination rates of LLMs ... nature.com May 13, 2025 5 facts
referenceShahbodaghi, A., Moghaddasi, H., Asadi, F., and Hosseini, A. authored 'Documentation errors and deficiencies in medical records: a systematic review', published in the Journal of Health Management in 2024 (Volume 26, pages 351–368).
claimUsing prompts with function calling in Large Language Models is useful for ensuring that generated outputs adhere to the specific structures required for different electronic health records.
procedureFunction calls and JSON-based output (Experiments 6, 9, 10, 11) instructed LLMs to generate responses in structured JSON format to optimize integration with primary care electronic health record systems.
referenceJia et al. (2025) introduced medIKAL, a framework that integrates knowledge graphs as assistants for large language models to enhance clinical diagnosis on electronic medical records.
referenceOverhage, J. M., Qeadan, F., Choi, E. H. E., Vos, D., and Kroth, P. J. authored 'Explaining variability in electronic health record effort in primary care ambulatory encounters', published in Applied Clinical Informatics in 2024 (Volume 15, pages 212–219).
A Comprehensive Benchmark and Evaluation Framework for Multi ... arxiv.org Jan 6, 2026 5 facts
claimThe integration of Retrieval-Augmented Generation (RAG) and Multi-Agent Systems (MAS) enables patient agents to interact with simulated Electronic Health Records (EHR) and external diagnostic tools.
referenceYixing Jiang, Kameron C. Black, Gloria Geng, Danny Park, James Zou, Andrew Y. Ng, and Jonathan H. Chen developed MedAgentBench, a realistic virtual electronic health record environment designed to benchmark medical large language model agents, in 2025.
claimThe Patient-Zero framework enables the generation of synthetic patient data without accessing real-world medical records, which strictly preserves patient privacy.
claimThe MedDialogRubrics framework synthesizes realistic patient records and chief complaints from underlying disease knowledge without accessing real-world electronic health records, thereby mitigating privacy and data-governance concerns.
referenceAgentClinic is a multimodal agent environment that treats clinical decision-making as a sequential task involving external tools and electronic health records, demonstrating that interactive diagnosis is more challenging for Large Language Models than static answering.
Medical Hallucination in Foundation Models and Their ... medrxiv.org Mar 3, 2025 3 facts
procedureConsistency Analysis uses Natural Language Inference (NLI) and Question-Answer Consistency techniques to detect Decision-Making Hallucinations and Diagnostic Hallucinations in Clinical Decision Support Systems (CDSS) and Electronic Health Record (EHR) Management.
claimHegselmann et al. (2024b) state that inconsistencies in clinical datasets, such as electronic health records and physician notes, propagate errors into Large Language Model training.
referenceModels used in patient-trial matching, as described by Yuan et al. in 2023, improve compatibility between electronic health records (EHRs) and clinical trial descriptions to refine model accuracy in real-world clinical settings.
Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org Nov 2, 2025 2 facts
claimLarge Language Models used in patient-trial matching improve compatibility between electronic health records and clinical trial descriptions, refining model accuracy in real-world clinical settings.
claimClinical datasets, such as electronic health records and physician notes, frequently contain noise in the form of incomplete entries, misspellings, and ambiguous abbreviations.
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 2 facts
claimThe Health Knowledge Graph Builder (HKGB) is a platform designed to semi-automatically construct clinical knowledge graphs with heavy human-in-the-loop involvement, consuming Electronic Medical Records (EMR) as input and producing graph data in OWL and RDF formats.
procedureThe human interaction process in the HKGB platform involves four steps: (1) inspecting new concepts or relations to approve recommendations, (2) adding medical synonym entities based on instances, (3) annotating unstructured data based on instances and relations of the current knowledge graph, and (4) defining mapping rules from Electronic Medical Records to RDF and extracting concepts, entities, and relations.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 1 fact
referenceThe paper 'Diagnosis of chronic diseases based on patients’ health records in IOT healthcare using the recommender system' was authored by Y.A. Nanehkaran, Z. Licai, J. Chen, Q. Zhongpan, Y. Xiaofeng, Y.D. Navaei, and S. Einy, and published in Wireless Communications and Mobile Computing in 2022.
Understanding LLM Understanding skywritingspress.ca Jun 14, 2024 1 fact
procedureResearchers at McGill and MILA used deep learning to interpret clinician thinking by pre-training on hundreds of millions of general sentences and applying large language models to over 4,000 free-form health records to distinguish confirmed from suspected autism cases.
Day-5 | Anu Anuja - LinkedIn linkedin.com Feb 20, 2026 1 fact
claimThe 'integration' roadblock in HealthTech AI involves the difficulty of embedding standalone AI systems into existing clinical infrastructure, such as Electronic Health Records (EHRs) and legacy systems that are 10-20 years old.
Hallucination Causes: Why Language Models Fabricate Facts mbrenndoerfer.com Mar 15, 2026 1 fact
claimValuable scientific and specialized knowledge is often excluded from large language model training data because it is behind paywalls, in subscription journals, or contained in private databases like electronic health records, legal databases, and proprietary financial data.