Papers for

electronic health record developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Beacon-SP uses graph and ontology to improve suicide risk assessment

BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment

Abstract: We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention, where effective assessment requires integrating heterogeneous clinical, behavioral, social, and temporal evidence. BEACON-SP combines patient knowledge graphs with ontology-guided retrieval to support multi-hop reasoning across diagnoses, medications, risk and protective factors, life events, and temporal relationships. The framework is enabled by a comprehensive suicide prevention ontology that integrates the Three-Step Theory, the Integrated Motivational-Volitional Model, and the Suicide Social Determinants of Health Ontology into a unified representation of patient risk factors. We construct ontology-grounded patient knowledge graphs and evaluate BEACON-SP for clinician-facing question answering. Compared with a vector-based retrieval-augmented generation (RAG) baseline on a 1,500-query benchmark spanning 15 clinical categories and 100 patients, BEACON-SP improves completeness, clinical relevance, and evidence grounding under a corrected comparative evaluation protocol, with a small gain on factual accuracy. In paired criterion-level comparisons, GraphRAG is preferred in 76.4% of cases. These results demonstrate the potential of ontology-guided GraphRAG to provide structured, contextualized patient evidence for clinical decision support.

Tue 6 OctArtificial IntelligenceComputation and LanguageInformation Retrieval
The gist
Suicide risk assessment is complex because it requires combining many different types of information about a patient. The authors created BEACON-SP, a tool that uses graphs and a structured knowledge framework called an ontology to better organize and understand clinical and social information. This helps doctors answer questions about a patient's suicide risk by showing them clearer and more complete evidence. Compared to previous methods, BEACON-SP gives more relevant and well-supported answers for clinical decisions.
Open → 2610.09026v1

Large language model automates clinical note clarifications with mixed success

Closing Ambient Clinical Documentation Gaps with Automated Provider Queries

Abstract: Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate billing. Prior work automates note drafting, ICD-10 coding, and order extraction assuming a complete transcript, leaving these gaps unaddressed. We study whether an LLM can automate the query loop, termed DAU (Draft, Ask, Update), across those three tasks. An audit of 3,000 real visits identifies the sources of missing documentation, from which we build five transcript-degradation benchmarks on public data. Analyzing 21k clarification turns on real conversations, we find useful-question predictors are task-specific: oracle confidence dominates, but note completeness needs only simple recall questions while ICD-10 coding needs harder, multi-option ones. About 9% of turns hurt performance, driven by redundant questions and non-answers that still trigger a rewrite. Deployment depends on learning "when not" as much as "what to" ask.

Mon 5 OctComputation and Language
The gist
Medical notes sometimes miss important information that affects billing and patient care. The authors looked at using a large language model (LLM) to automatically ask doctors clarifying questions and update notes in three key tasks: writing notes, assigning billing codes, and identifying medical orders. They found that different tasks need different types of questions and that the system can both help and sometimes harm accuracy, especially if it asks unnecessary or unclear questions. The study highlights that knowing when not to ask questions is just as important as what to ask.
Open → 2610.07502v1

Dual-agent system improves analysis of clinical data with sql checks

Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation

Abstract: Objective: To develop and characterize CLEAR-Med, a dual-agent framework for natural-language analysis of structured clinical data that separates SQL-based invocation from independent validation. Methods: CLEAR-Med uses one agent to translate a question into executable Structured Query Language (SQL), retain the executed query and database result, and produce a draft. Deterministic checks and a separately invoked cross-provider Validation Agent then accept the draft, request one bounded repair, or abstain. We formalized the system as a bounded selective pipeline and evaluated CLEAR-Med's configuration and scalability, and the Invocation Agent's accuracy and consistency on a 25-query development benchmark, using a harmonized 21-site neonatal hypoxic-ischemic encephalopathy table containing 532 de-identified infant records and approximately 1,300 variables. Results: CLEAR-Med completed all six nominal scalability configurations, including 500x1300. Across 25 development-benchmark queries repeated five times, the Invocation Agent answered 83 of 125 responses correctly (66.4%; query-cluster bootstrap 95% CI, 48.0-83.2%), compared with 15 of 125 (12.0%; 95% CI, 3.2-22.4%) for the ungrounded ChatGPT baseline, a paired improvement of 54.4 percentage points (95% CI, 36.8-72.0%). Conclusion: CLEAR-Med provides a general architecture for traceable analysis of structured clinical data: numerical claims remain linked to executed SQL, and unresolved cases can fail closed. The reported experiments characterize CLEAR-Med's configuration and scalability and the Invocation Agent's accuracy, while the formal analysis establishes the encoded-property guarantee of the complete control flow; a prospective full-pipeline evaluation of the validation and abstention stages is the next stage of this work.

Mon 28 SeptArtificial Intelligence
The gist
Analyzing complex medical data accurately is challenging because computer systems need to understand and fetch the right information from databases. The authors created a system called CLEAR-Med that uses two agents: one translates questions into database commands and the other checks those answers independently. This setup helps catch mistakes and makes sure the answers are reliable by linking every answer to the exact data command that generated it. Their tests showed this approach gives much more accurate results than a typical chatbot. The system is designed to handle large datasets and can refuse to answer when not confident, improving trust in clinical data analysis.
Open → 2609.34039v1