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#ehr-systems News & Analysis

9 articles tagged with #ehr-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

9 articles
AINeutralarXiv – CS AI · May 297/10
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MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings

Researchers introduced MedCase-Structured, a synthetic dataset that converts unstructured clinical text into standardized HL7 FHIR format for evaluating large language models in realistic healthcare settings. The study reveals that LLMs perform significantly worse on structured clinical data than plain text, highlighting a critical gap between academic benchmarks and real-world deployment requirements.

AIBullisharXiv – CS AI · May 17/10
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End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians

Researchers present a comprehensive governance framework for deployed clinical AI systems, demonstrated through Hyperscribe, an EHR-embedded audio transcription agent. The study shows that continuous monitoring, controlled experimentation, and multi-channel feedback mechanisms can improve system performance from 84% to 95% accuracy while maintaining operational efficiency and cost-effectiveness.

AINeutralarXiv – CS AI · Jun 236/10
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Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts

Researchers propose CAFM, a Cohort-Anchored Foundation Model framework designed to improve interpretability and clinical reliability of AI systems for electronic health records by elevating patient cohorts to a primary learning object. The four-stage framework addresses limitations in existing EHR models through better data curation, cohort-conditioned training, multimodal alignment, and clinician feedback, with case studies demonstrating applications across kidney injury prediction, cardiovascular risk assessment, and imaging analysis.

AINeutralarXiv – CS AI · Jun 196/10
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MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

MedRLM is a new AI framework designed to improve clinical decision support by recursively analyzing heterogeneous patient data across EHR records, medical images, sensor streams, and clinical guidelines. The system uses specialized agents and an evidence graph memory to coordinate reasoning tasks and trigger deeper analysis when abnormal physiological patterns are detected, moving beyond single-step medical AI systems toward more auditable, workflow-integrated clinical tools.

AINeutralarXiv – CS AI · Jun 196/10
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Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance

Researchers introduce PhysAssistBench, a new evaluation framework for testing large language models in real-world clinical settings where physicians, patients, and electronic health records interact simultaneously. The benchmark reveals that current leading LLMs struggle with coordinating medical knowledge, patient communication, and precise system interactions together, exposing a critical gap between isolated capability improvements and practical clinical assistance.

AINeutralarXiv – CS AI · May 276/10
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Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records

Researchers introduce EHR-ReasonCon, a benchmark dataset and EHR-Inspector, an LLM-based framework designed to verify consistency between unstructured clinical notes and structured data in Electronic Health Records. The work addresses a critical gap in healthcare data quality by moving beyond simple value matching to capture clinical reasoning, temporal relationships, and event interpretations that reflect real-world documentation practices.

AINeutralarXiv – CS AI · May 276/10
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Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline

Researchers developed a hybrid neural-symbolic pipeline for extracting clinical follow-up instructions from outpatient notes, pairing medical actions with future dates. The system significantly outperformed generative AI models (GPT-4o-mini and LLaMA-3) at linking actions to dates, achieving 99.7% F1 score on seen data versus 51-57% for baselines, demonstrating that symbolic reasoning outperforms pure language generation for structured clinical extraction tasks.

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AINeutralarXiv – CS AI · Apr 146/10
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Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange

A research study presents a readiness framework and practical deployment strategy for AI-based anomaly detection in multi-provider healthcare environments. The research combines organizational assessment criteria with machine learning performance evaluation, demonstrating that hybrid rule-based and isolation forest approaches optimize both detection coverage and alert efficiency in cross-provider EHR systems.