#ehr-systems News & Analysis

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

5 articles

AINeutralarXiv – CS AI · 2d ago7/10

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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 · 4d ago6/10

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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 · 4d ago6/10

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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.