JAMIA Open最新文献

{"title":"Registry Forge: an open-source end-to-end pipeline for patient-directed SMART on FHIR registries.","authors":"Danielle Boyce, Alan Premasiri, Shawn Sullivan, Beth Levine, Fernando G Vieira","doi":"10.1093/jamiaopen/ooag200","DOIUrl":"10.1093/jamiaopen/ooag200","url":null,"abstract":"<p><strong>Objectives: </strong>Patient-directed SMART on FHIR lets registries acquire longitudinal electronic health record data, but the payload requires substantial engineering before use. We present Registry Forge, an open-source pipeline that converts it into curation-ready outputs.</p><p><strong>Materials and methods: </strong>Registry Forge parses C-CDA, HTML, RTF, PDF, and FHIR inputs, joins records to a canonical identifier, and emits a browser dashboard, an OMOP CDM v5.4 data set, GA4GH Phenopackets v2, a code inventory, and disease-specific narrative extractions.</p><p><strong>Results: </strong>Applied to the ALS Research Collaborative Study (94 participants, 56 US health systems), it processed 22 686 files and 1791 FHIR Bundles (109 599 resources); only 15.0% of files were full C-CDA XML; HTML (48.4%) and RTF (34.3%) predominated.</p><p><strong>Discussion: </strong>Payloads are far more heterogeneous than often assumed; the architecture is designed for extension, though generalizability remains to be demonstrated.</p><p><strong>Conclusion: </strong>Registry Forge closes the gap between acquisition and curation-ready data with no server infrastructure and is openly available.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag200"},"PeriodicalIF":4.7,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalizing digital mental health: a micro-randomized trial analysis of cognitive behavioral therapy/dialectical behavioral therapy text messages to improve mood.","authors":"Caroline A Figueroa, Jing Xu, Marvyn R Arévalo Avalos, Bibhas Chakraborty, Adrian Aguilera","doi":"10.1093/jamiaopen/ooag156","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooag156","url":null,"abstract":"<p><strong>Purpose: </strong>Digital mental health interventions could improve population health. However, our understanding of how to improve their effectiveness through personalization is limited. We describe the outcomes of a micro-randomized trial (MRT) of a 60-day text messaging program, delivered during the final stages of the COVID-19 pandemic. We analyze the effects of 6 different types of cognitive behavioral therapy (CBT) and dialectical behavioral therapy (DBT) messages, and examine whether socio-demographic, health-related, and contextual factors (eg, timing of sending a message) moderate effectiveness.</p><p><strong>Methods: </strong>This secondary analysis included 373 adults aged 18 years or older (mean = 38.07), mostly English-speaking female, highly educated White individuals who reported being in good health, but having high stress and struggling to get their basic needs met. Participants were recruited between December 2021 and September 2022 through social media and a recruitment service (Health Match). We used the weighted and centered least squares method to examine the proximal effects of the text-message categories on daily mood, and whether sociodemographic, health-related factors, and the timing of message delivery moderated effectiveness.</p><p><strong>Results: </strong>None of the 6 CBT/DBT-message categories significantly improved mood ratings marginally. However, their effectiveness varied based on the time of day and self-reported health. Overall, sending a message in the afternoon was more effective. However, certain message categories showed significant effects when sent in the morning among participants with poorer self-reported health, while some showed lower effectiveness when sent in the late afternoon for participants with better health.</p><p><strong>Conclusion: </strong>Our analysis underscores the importance of considering the time of day and individual health status when developing digital mental health interventions. These insights can help in designing more personalized, adaptive, and effective digital interventions.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag156"},"PeriodicalIF":4.7,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13659918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Avoiding \"shiny objects\" and heeding \"red flags\": a sensemaking framework for emerging technology procurement.","authors":"Wiljeana Glover, Dessislava Pachamanova, Derick R Simmons, Robin Sodano, Eric Alper","doi":"10.1093/jamiaopen/ooag201","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooag201","url":null,"abstract":"<p><strong>Objectives: </strong>Health systems can be vulnerable to \"shiny object syndrome,\" adopting unneeded emerging technologies (ETs), or may ignore \"red flags\" if they rely on linear health technology procurement processes that exclude the influence of information exchanges between the health system, vendors, and industry partners.</p><p><strong>Methods: </strong>We conduct a post-hoc comparison of 2 procured communication-related clinical workflow ETs and examine ET-related IT Committee meetings within an academic medical center. We apply sensemaking theory, highlighting examples where sense-giving, sense-demanding, and sense-breaking were present or absent across stakeholders during procurement.</p><p><strong>Results: </strong>Present (or absent) sensemaking dynamics demonstrated reciprocity (or lack thereof) across all 3 sensemaking components. Sense-breaking particularly informed committee meetings, leading to new technological and organizational infrastructure.</p><p><strong>Conclusions: </strong>Our emergent sensemaking ET procurement framework offers 3 actionable recommendations: formalize multi-stakeholder information exchange roles, treat vendor responsiveness to sense-demanding as a selection criterion, and use each procurement cycle to surface and address foundational governance and infrastructure gaps.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag201"},"PeriodicalIF":4.7,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating chart review for identification of infected patients using natural language processing.","authors":"Afia Z Khan, Kevin G Buell, George Karway, Kyle Carey, Nicole Dussault, Jay Dumanian, Sivasubramanium Bhavani, Emily R Gilbert, Christopher Winslow, Nirav Shah, Majid Afshar, Dana P Edelson, Matthew M Churpek","doi":"10.1093/jamiaopen/ooag163","DOIUrl":"10.1093/jamiaopen/ooag163","url":null,"abstract":"<p><strong>Objective: </strong>Expert clinician chart review is the most accurate method to identify patients with infection but is laborious and costly. We aimed to develop and externally validate machine-learning models using structured and unstructured data to identify infection in hospitalized patients compared to chart review.</p><p><strong>Materials and methods: </strong>Adult inpatient encounters at 2 health systems (2006-2016) were manually annotated for suspected infection on admission. Data from one site were used to develop gradient-boosted machine-learning models. Structured features included labs, vital signs, and nursing assessments. Unstructured clinical notes were converted to concept unique identifiers (CUIs) using the clinical Text Analysis and Knowledge Extraction System. Unimodal and multimodal models were tuned to maximize the area under the receiver operating curve (AUROC) in the derivation site through cross-validation. Model discrimination and calibration were assessed in the external validation site.</p><p><strong>Results: </strong>Of the 3014 included admissions, 677 (22.5%) were classified as having suspected infection on admission. A multimodal model combining n-gram TF-IDF and structured data demonstrated the best performance, significantly outperforming the structured data only model (AUROC 0.88 vs 0.82, <i>P</i> < .001, area under the precision-recall curve 0.71 vs 0.58, <i>P</i> < .001). The most important features came from unstructured data.</p><p><strong>Discussion and conclusion: </strong>Our results highlight the value of unstructured text data for developing models that accurately replicate chart reviews in identifying patients with infection. By expediting the annotation of infection cases, our models can facilitate epidemiological research and the curation of datasets necessary for developing clinical decision support systems for early sepsis detection.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag163"},"PeriodicalIF":4.7,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ATRI LIMS: a cloud-native open-source laboratory information management system for biomarker-driven Alzheimer's disease clinical trials.","authors":"Gustavo A Jimenez-Maggiora, Stefania Bruschi, Sara Abdel-Latif, Paul S Aisen, Robert A Rissman","doi":"10.1093/jamiaopen/ooag198","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooag198","url":null,"abstract":"<p><strong>Objectives: </strong>This article summarizes the architecture, design principles, and implementation strategy of the ATRI Laboratory Information Management System (LIMS), a cloud‑native, open‑source platform supporting biomarker‑driven Alzheimer's disease (AD) clinical research. The system addresses challenges associated with managing large‑scale, multimodal biomarker, and biobanking data across multisite studies.</p><p><strong>Methods: </strong>The ATRI LIMS employs a Model-View-Controller architecture implemented in Django, with Single‑Page Application interfaces built in ReactJS. A hybrid database model integrates a normalized core schema with study‑specific extensions. Deployment on Amazon Web Services incorporates multifactor authentication, encryption, and a web application firewall. Regulatory support is provided through audit trails and electronic signatures. Development relied on collaboration between informatics engineers and laboratory personnel, with user acceptance testing guiding workflow and interface refinement.</p><p><strong>Results: </strong>As of February 2026, the ATRI LIMS supports seven clinical trials and observational studies and manages more than 1.2 million biospecimens. The platform synchronizes biospecimen metadata and assay outputs with clinical datasets, improves operational efficiency through automated workflows and quality control, and facilitates compliant sample sharing for secondary research. AI‑assisted development accelerated feature delivery while maintaining code quality.</p><p><strong>Discussion: </strong>The ATRI LIMS offers a scalable, adaptable informatics framework for biomarker‑intensive AD research. Its modular design, cloud‑native architecture, and comprehensive data management capabilities support accurate specimen tracking and coordinated data integration. An open-source release is planned to enable adoption and adaptation by other research programs.</p><p><strong>Conclusions: </strong>The ATRI LIMS enhances informatics infrastructure for modern AD research by enabling scalable, efficient, and compliant biomarker and biospecimen workflow management.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag198"},"PeriodicalIF":4.7,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13653371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RxReduce: a medication deprescribing agent framework leveraging large language models to optimize proton pump inhibitor use.","authors":"Benjamin Michaels, Reed Evans, Kris Riedman, Joanna Yang, Gray Selby","doi":"10.1093/jamiaopen/ooag167","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooag167","url":null,"abstract":"<p><strong>Objectives: </strong>Polypharmacy rates continue to rise which can lead to increased risk of adverse drug events. Deprescribing is essential but time-consuming, requiring extensive clinical note and chart review. We developed RxReduce, a large language model (LLM)-powered framework as a proof of concept that can assist with medication deprescribing.</p><p><strong>Materials and methods: </strong>RxReduce utilizes LLMs and retrieval-augmented generation (RAG) to evaluate patient data. Focusing on proton pump inhibitors, the system analyzes diagnoses and clinical notes to generate recommendations (Continue, Deprescribe, or Stop). To optimize accuracy, clinical notes were preprocessed into summaries before RAG retrieval.</p><p><strong>Results: </strong>The optimized system achieved an F1 score of 0.84, significantly outperforming the baseline (0.70).</p><p><strong>Conclusion: </strong>RxReduce demonstrates the viability of artificial intelligence-assisted deprescribing. By extracting key contextual insights, the framework reduces clinician burden and can be adapted for various medication classes to improve patient safety.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag167"},"PeriodicalIF":4.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedNLI-Pain benchmarks a minimally necessary requirement for large language model alignment.","authors":"Christopher Wong, Divy Kumar, Amiin Muse, Philip Wang, Finn Wintz, Sasha-Ann East, Jason Lyou, Naveena Yanamala, Zach Wood-Doughty","doi":"10.1093/jamiaopen/ooag143","DOIUrl":"10.1093/jamiaopen/ooag143","url":null,"abstract":"<p><strong>Objectives: </strong>To simultaneously evaluate and compare LLM performance in clinical reasoning with a minimally necessary criteria for clinical alignment with patients.</p><p><strong>Material and methods: </strong>We adapt the MedNLI dataset to create MedNLI-Pain, a new benchmark task that evaluates if LLMs that make accurate clinical inferences about patients can also anticipate when those patients are in pain. MedNLI-Pain consists of patient vignettes originating from MedNLI that are then annotated with physician assessments of the described patient's pain.</p><p><strong>Results: </strong>All LLMs had higher agreement with physicians in MedNLI than in MedNLI-Pain and its counterfactually augmented subset. This trend persisted when accounting for the higher difficulty of MedNLI-Pain by normalizing to human performance. Large language model performance improved with more training data in MedNLI but not MedNLI-Pain.</p><p><strong>Discussion: </strong>Large language models may lack necessary capabilities (eg, recognizing patients' pain) for clinical alignment. Success at a clinical reasoning task, in this case medical natural language inference, may not entail commensurate ability to align with patients' interests.</p><p><strong>Conclusions: </strong>Further research on alignment with patients is needed because the implications may direct future research into either closed-ended or agentic applications.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag143"},"PeriodicalIF":4.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-reported outcomes in radiotherapy (PRO-RT) utilizing an integrated patient portal; results from a prospective single-arm trial.","authors":"Yasamin Sharifzadeh, William S Harmsen, Jessica F Burlile, Kevin F Pyfferoen, Adam C Amundson, Mark R Waddle, Daniel J Ma, Satomi Shiraishi, Traci J Hammer, Nadia N Laack, Kimberly S Corbin","doi":"10.1093/jamiaopen/ooaf172","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooaf172","url":null,"abstract":"<p><strong>Objectives: </strong>This prospective single-arm trial assessed the use of an integrated patient platform (IPP) and effects on patient-reported outcomes survey (PROs) completion.</p><p><strong>Materials and methods: </strong>Head and neck, breast, and thoracic cancer patients undergoing radiotherapy (RT) were approached for registration for an IPP for PRO administration and integration of multiple health portals.</p><p><strong>Results: </strong>Seventy-two patients were approached, 27 (38%) enrolled, and 18 used the IPP for PROs, 2 of which utilized integrative features. IPP user PRO completion rates were 100%, 89%, 89%, and 94% at baseline, end of RT, and 3- and 6-months post-RT, higher than the standard interface. Of the 45 who declined, 24 (53%) reported information security concerns.</p><p><strong>Discussion: </strong>While the IPP increased PRO completion, many declined enrollment, citing security concerns. Few utilized the portal's integrative features.</p><p><strong>Conclusion: </strong>IPPs show promise in aiding PRO completion; continued efforts to demonstrate data safety and utility of IPPs are merited.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooaf172"},"PeriodicalIF":4.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal symptom severity tracking in vagus nerve stimulation patients: a 2-stage LLM-based pipeline with explainable AI.","authors":"Vedansh Thakkar, Greg M Silverman, Maura Putzer, Abhinab Kc, Sana Ikramuddin, Tanya Melnik, Nicholas E Ingraham, Nathan Keller, Samantha King, Emma Jones, Angela Bailey, Genevieve B Melton, Christopher Tignanelli","doi":"10.1093/jamiaopen/ooag195","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooag195","url":null,"abstract":"<p><strong>Objectives: </strong>To produce the first structured longitudinal dataset of symptom severity trajectories in vagus nerve stimulation (VNS) patients using a 2-stage large language model pipeline for automated symptom severity extraction from clinical notes, as part of the NIH-funded research evaluating vagal excitations and anatomical linkages (U54AT012307).</p><p><strong>Materials and methods: </strong>We analyzed 1427 annotated clinical notes from 95 patients across 5 corpora. LLaMA models (3.2-1B, 3.2-3B, and 3.3-70B) were fine-tuned to classify 13 symptoms as present, absent, or negated (stage 1), then assessed severity (indeterminate, mild, moderate, or severe; stage 2). Longitudinal trajectories were tracked quarterly in 23 VNS patients against a 10-patient surgical control cohort (NSQIP).</p><p><strong>Results: </strong>Severity F1-scores ranged from 0.76 to 0.96, and symptom classification reached 0.99 for high-prevalence symptoms, with the validation corpus (VNS vs NSQIP) as the dominant factor (29.4% of variance; no significant source-corpus effect), supporting cross-domain generalizability. Macro F1-scores were lower than weighted F1-scores for smaller models and NSQIP; suicidal-ideation sensitivity was lower on NSQIP than VNS (0.80 vs 1.00, largest model), limiting generalization for psychiatric risk detection. Equity analyses were exploratory given small subgroups (gender precision 0.95). The longitudinal dataset revealed a median time to clinically meaningful seizure improvement of 0.5 quarters, with 50% responders, 25% non-responders, and 25% worsened. Post-surgical decline was absent in the unmatched NSQIP control cohort, consistent with, but not confirming, VNS specificity.</p><p><strong>Discussion: </strong>To our knowledge, this is the first system producing structured symptom trajectories from free-text VNS documentation, supporting individualized over population-level monitoring.</p><p><strong>Conclusion: </strong>This dataset supports future treatment-response prediction, personalized device programming, and a reproducible clinical natural langauge processing benchmark for neuromodulation research.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag195"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-reflector: an agentic exploratory data analysis platform for researchers.","authors":"Moein Sabounchi, Nimay Hazare, Chris Capone, Mateen Jangda, Wonsuk Oh, Pushkala Jayaraman, Shashank Gupta, Ashwin Sawant, Zachary Kuschner, Prathamesh Parchure, Prem Timsina, Ben Kaplan, Aditi Vakil, Roopa Kohli-Seth, Patricia Kovatch, Alexis Zebrowski, Benjamin Glicksberg, Pulkit Agrawal, Bruce Darrow, Lisa Stump, Alexander Charney, Girish N Nadkarni, Ankit Sakhuja","doi":"10.1093/jamiaopen/ooag194","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooag194","url":null,"abstract":"<p><strong>Objectives: </strong>Exploratory data analysis (EDA) is foundational to clinical research yet inaccessible to clinician-scientists without programming training. Existing large language model (LLM) tools improve accessibility but struggle with hallucinations. We developed and evaluated data reflector (DR), a hybrid agentic platform pairing natural language interaction with deterministic statistical computation.</p><p><strong>Materials and methods: </strong>Data reflector restricts the LLM to intent interpretation, tool selection, and filter parsing while routing all numerical computation through predefined deterministic functions executed locally. We piloted DR with 6 participants of diverse technical backgrounds across 6 clinical (MIMIC-IV, eICU Collaborative Research Database, National Health and Nutrition Examination Survey) and nonclinical (energy, COVID-19, urban mobility) datasets, assessing usability (system usability scale, SUS), task completion time, analytical output correctness against participant-generated manual reference results, hallucination prevention, and independent reproducibility by a second operator. A head-to-head comparison with ChatGPT 5.3 chatbot was also conducted.</p><p><strong>Results: </strong>Data reflector achieved a mean SUS of 94.58, with reduced task completion time across all evaluable participants. Statistical outputs and generated distribution curves demonstrated complete agreement with participant-generated manual reference results across all evaluable outputs. Independent reexecution by a different operator using independently constructed prompts also demonstrated complete agreement with the original DR outputs across all evaluated tasks. Hallucination-prevention safeguards correctly declined out-of-scope and impossible queries. ChatGPT produced incomplete, internally inconsistent outputs on identical tasks where DR returned complete, accurate results.</p><p><strong>Discussion: </strong>Data reflector's architectural separation of LLM reasoning from numerical computation removes dependence on prompt phrasing, session history, and stochastic LLM sampling while preserving conversational accessibility, addressing reproducibility limitations of general-purpose LLM tools.</p><p><strong>Conclusion: </strong>Data reflector offers a practical pattern for reproducible, accessible artificial intelligence-assisted EDA across heterogeneous data sources in modern clinical and translational research.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"9 5","pages":"ooag194"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}