JMIR Medical Informatics最新文献

{"title":"Automatic Kidney Image Segmentation During Robot-Assisted Partial Nephrectomy Using a Deep Learning Model Based on a Multiannotator Dataset: Model Development and Validation Study.","authors":"Gaëlle Margue, Kilian Chandelon, Maxime Pattou, Alice Pitout, Abderrahmane Khaddad, Federico Rubat-Baleuri, Julie Desternes, Laura Richert, Nicolas Bourdel, Adrien Bartoli, Jean-Christophe Bernhard","doi":"10.2196/82540","DOIUrl":"10.2196/82540","url":null,"abstract":"<p><strong>Background: </strong>Augmented reality (AR) has emerged as a promising tool to enhance surgical precision during robot-assisted partial nephrectomy (RAPN), particularly by enabling the intraoperative overlay of 3D anatomical models. However, real-time AR implementation requires robust image segmentation of anatomical structures, such as the kidney, which remains technically challenging in dynamic laparoscopic environments.</p><p><strong>Objective: </strong>This study aimed to develop and validate a large, annotated image dataset to train deep learning models for automated segmentation of the renal parenchyma during RAPN, as a prerequisite for real-time AR guidance.</p><p><strong>Methods: </strong>We conducted a single-center, observational image annotation study using prospectively collected surgical videos from 131 RAPN procedures performed between 2022 and 2024. Patients had localized renal tumors, with 11 presenting with multifocal disease (160 tumors in total). A total of 48,000 frames were extracted based on image sharpness, diversity, and the exclusion of artifacts. A subset of 454 images was annotated by 9 contributors (surgeons, engineers, and nonexperts) after structured training. Interannotator agreement was assessed using the Dice similarity coefficient (DSC) and sensitivity against an expert reference. A convolutional neural network (AlbuNet-34) was trained using 12,546 annotated images and evaluated on a validation set of 3137 images. Model performance was analyzed overall and across surgical phases.</p><p><strong>Results: </strong>Annotators achieved high agreement, with median DSC values ranging from 0.91 to 0.95 and sensitivity consistently more than 0.89. The deep learning model achieved a mean DSC of 0.75 (SD 0.23) and a sensitivity of 0.71 (SD 0.24) on the validation set. Segmentation accuracy varied significantly across surgical phases, with lower performance observed during tumor resection and tumor bed reconstruction due to increased visual complexity.</p><p><strong>Conclusions: </strong>This study demonstrates the feasibility of automated renal parenchyma segmentation using deep learning in real-world intraoperative settings. Although current performance remains below that of expert-level annotations, the creation of a large, annotated dataset and the implementation of a structured multiannotator workflow represent key milestones toward reliable AR-assisted surgery. Ongoing refinements in annotation quality, dataset diversity, and neural network optimization are expected to enhance future real-time AR applications in urology.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e82540"},"PeriodicalIF":5.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13583486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Health Informatics System in the South Australian Public Health Network: Implementation Report.","authors":"James Malycha, Jake White, Adam Phillips, Lukah Dykes, Lukasz Wiklendt, Paul Lambert, Adelaide Boylan, Justin Beilby","doi":"10.2196/86564","DOIUrl":"10.2196/86564","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Following 2 decades of electronic medical record (EMR) adoption, most large public health systems hold comprehensive digital clinical data but lack the complementary informatics capability to return those data to clinicians, coders, and operational teams in a usable form. In South Australia (SA), the statewide Sunrise (Altera Digital Health) EMR system has digitized documentation and ordering since 2017; however, tools for back-end data extraction and enriched clinical information displays were not prioritized, and clinical, operational, and research users have reported ongoing difficulties accessing timely data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study is to describe the development, governance, and deployment of a cloud-native health informatics system (HIS) in the Central Adelaide Local Health Network (CALHN), which serves approximately 40% of SA public patients, and to report implementation outcomes in accordance with the iCHECK-DH (Guidelines and Checklist for the Reporting on Digital Health Implementations) guidelines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The HIS comprises 6 architectural layers deployed on Microsoft Azure with Red Hat OpenShift (IBM), extracting Sunrise EMR data in near real time under a read-only model in which all patient data remain within the SA Health network at all times. Implementation proceeded through 5 overlapping phases (2021-2026), governed across 4 domains: technical (security impact assessment, information asset classification, and independent cybersecurity review), clinical (a clinical governance committee that has met quarterly since May 2022), corporate (incorporation of HeartAI Pty Ltd in 2022, with conflicts of interest declared and managed under SA Health policy), and ethical (human research ethics committee [HREC], reference 18079 with subsequent amendments). Development was funded through approximately Aus $1.84 million (Aus $1=US $0.72 as of August 24, 2026) in competitive and institutional grants under a public-private model with CALHN and AusHealth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Unlabelled: &lt;/strong&gt;Three applications have reached deliberately different stages of maturity. The CALHN Critical Care Informatics System (CCCIS) has been implemented and evaluated: it has been deployed across the 2 CALHN intensive care units (ICUs; 54 beds, approximately 5000 admissions per year) since 2022, is in daily clinical use, and automates the submission of 115 variables to the Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation (ANZICS CORE) registry. In a formal evaluation by 8 senior intensivists, its mean System Usability Scale score was 76 of 100, and the overall workload was low (NASA Task Load Index: 21/100). CODEXA, an AI-assisted clinical coding application, is in operational validation: models trained on 500,000 episodes and tested on 50,000 held-out episodes achieved a pooled &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-score of 71% across the full case mix and","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e86564"},"PeriodicalIF":5.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13533040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Call Abandonment in a Health Care Call Center Using Nonpersonal Operational Data: Machine Learning Study.","authors":"Hilmi Al-Shakhshir, Sara Kier, Dan Hoke, Stephanie Hoiriis, Mary-Ellen Vicinus, Ravi Thadhani, Anant Madabhushi","doi":"10.2196/88441","DOIUrl":"10.2196/88441","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Call abandonment is a critical barrier to patient access in health care call centers; yet, predictive modeling efforts are limited by strict privacy regulations that restrict the use of personal or behavioral data. Whether abandonment can be accurately predicted using only anonymized operational metrics remains unclear.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study evaluated the feasibility, performance, and operational use of machine learning models trained exclusively on nonpersonal, routinely collected call center metrics to predict call abandonment across distinct organizational phases.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed 1,037,363 call records from a large academic health care system spanning 4 operational periods marked by workflow changes and skill consolidation. Features included temporal variables, skill identifiers, and rolling operational metrics (in-queue time, occupancy, handle time, after-call work time, and active agents). Random forest and CatBoost models were trained on 3 phases defined as: (T1 [January-April 2023; original workflows], T2 [May-August 2023; post skill consolidation, cross-training, and new workflows], and T3a [September-December 2023; optimized processes]) using 5-fold cross-validation with 3 imbalance-handling strategies (none, class weighting, and synthetic minority oversampling technique). Temporal generalizability was assessed by evaluating all models on all 4 phases, with T3b serving as an unseen holdout set. Performance was evaluated using area under the curve (AUC), precision-recall area under the curve (PR-AUC), Brier score, and calibration error. Shapley additive explanations (SHAP) values quantified feature contributions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Across all training phases and algorithms, adding operational metrics improved the area under the receiver operating characteristic curve by 0.03-0.13 vs models using only temporal and skill features. The best configuration was a CatBoost model trained on T2 with operational metrics and no imbalance correction (T3b AUC 0.767, PR-AUC 0.068, expected calibration error 0.006, and Brier 0.027). Models trained solely on preintervention data (T1) generalized poorly to postintervention periods when restricted to temporal and skill features (AUC 0.32-0.38) but achieved AUC 0.715 on T3b when operational metrics were included. SHAP analysis consistently identified in-queue time as the dominant predictor, with the number of logged-in agents, hour-of-day, and skill identifiers comprising the remaining top features. Abandonment declined from 8.7% (20,809/238,722) in T1 to 2.8% (8293/300,060) in T3b; model-based analyses of temporal features (day of week and hour of day) showed the highest risk on Mondays and between 11:00 and 16:00. Skill-level analyses showed marked improvement in high-volume imaging teams with high abandonment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Models trained on nonpersonal operational data predicted call aba","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e88441"},"PeriodicalIF":5.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13575546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Conceptual Model for Ambient AI Adoption: Perspectives From Academia and Industry.","authors":"Joshua Biro, Jesse M Pines, Sudha Jayaraman, Vamsi Chagari, Raj Ratwani","doi":"10.2196/91098","DOIUrl":"10.2196/91098","url":null,"abstract":"<p><strong>Unlabelled: </strong>Ambient AI technologies are increasingly marketed as solutions to reduce clinician burden and improve care efficiency; however, real-world performance varies widely across clinical settings. Health care provider organizations face challenges in determining which aspects of ambient AI performance matter most and how to obtain meaningful information about those aspects from vendors or through internal evaluation. This article presents a shared mental model to guide health system leaders in conceptualizing ambient AI performance across 3 interdependent dimensions: technical, interface, and system level. For each dimension, we outline the types of information relevant to assessment; what vendors should reasonably be expected to provide; and how health care provider organizations can conduct their own evaluations to contextualize, verify, or supplement vendor claims. By integrating both vendor and health system perspectives, this work offers a grounded, practical structure to support organizations of all sizes in understanding and making informed decisions about ambient AI technologies.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e91098"},"PeriodicalIF":5.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point-of-Care Ultrasound Integrated With Teleconsultation for Rural Home-Based Medical Care: Pilot Implementation and Financial Analysis.","authors":"Hung-Bin Tsai, Nin-Chieh Hsu, Sang Ju Yu, Feng-Jung Yang","doi":"10.2196/93876","DOIUrl":"10.2196/93876","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Population aging and geographic health disparities challenge health care delivery in rural settings worldwide. Point-of-care ultrasound (PoCUS) combined with teleconsultation may enhance home-based medical care by extending specialist expertise to underserved communities, yet evidence from real-world implementation in Asian settings remains scarce. Taiwan, with its high digital literacy and universal National Health Insurance (NHI) system, provides a unique context for evaluating integrated PoCUS-teleconsultation service delivery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to describe the implementation, clinical applications, and financial sustainability of an integrated PoCUS-teleconsultation service within a dedicated home-based medical care practice in rural eastern Taiwan.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective analysis of prospectively recorded routine care data at D Clinic, Taitung County, from April 2020 to May 2022. PoCUS examinations conducted in the clinic, during home visits, and through mobile outreach were recorded. A business-to-business-to-consumer (B2B2C) teleconsultation model linking on-site physicians with remote specialists via real-time ultrasound streaming was implemented from March 2021. Seven-year net present value (NPV) analyses evaluated financial viability across 24 equipment reimbursement scenarios.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among the 26 patients who received teleconsultation, the mean age was 79.4 (SD 9.2) years, 15 (58%) were male, and 20 (77%) were in the intensive home health care tier. A total of 676 PoCUS examinations (defined as single-region imaging studies) were performed by 3 trained physicians in the following settings: 375 (55.5%) in the clinic, 174 (25.7%) during home visits, and 127 (18.8%) as mobile outreach. Twenty-six teleconsultation sessions were conducted (n=15, 58% during home visits; n=7, 27% during mobile outreach; and n=4, 15% in the clinic), with 13 (50%) involving multitarget scanning (cardiac, pulmonary, and abdominal). Of the 26 sessions, 24 (92%) were completed without technical interruption, while 2 (8%) experienced transient 4G buffering but were completed. Monthly PoCUS volume reached a sustained plateau of ≥28 examinations from month 17 of implementation. Four clinical service domains were identified: hospital-at-home acute management, home-based hospice multiorgan support, green channel surgical referral, and chronic disease serial monitoring. NPV analysis demonstrated that wireless PoCUS equipment with the proposed, but not yet implemented, dual-specialist teleconsultation reimbursement achieved the most favorable projected return (NT $882,000; NT $1=US $0.036 in 2021 on average), requiring only 2.72 monthly cases to break even under the modeled assumptions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Integrating PoCUS with teleconsultation is operationally feasible in rural home-based care when wireless equipment is adopted","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":" ","pages":"e93876"},"PeriodicalIF":5.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13570238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relevance of the uMap Collaborative Platform as Support for Choropleth Mapping of a Traffic-Light Statistical Signal Atlas of All-Cause Mortality During the First French Lockdown: Geospatial Analysis.","authors":"Anne Quesnel-Barbet, Thierry Pages, Julien Soula, Gilles Maignant, Arnaud Hansske","doi":"10.2196/82855","DOIUrl":"10.2196/82855","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The growing need for and interest in geomatics in the medical sector, as well as the pandemic crisis, led us to create a France-wide geomatics project aimed at producing several atlases of all-cause mortality at the municipal and submunicipal district levels via uMap France, a free and open-source collaborative map-sharing platform. In 2020, we decided to circumvent the obstacle of accessing detailed COVID-19 data by adopting a mortality-based approach to map the consequences of the crisis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of the uMap study was to provide a webmapping platform with original visualization and knowledge as well as decision-making aids that complement existing information and are relevant to public and health care professionals. Our main hypotheses were as follows: (1) The medical sector could develop a private uMap platform dedicated to health; (2) interest in a municipal mortality atlas for France linked to the pandemic crisis will increase, even if it is produced after the pandemic; and (3) sharing the atlases with the uMap community will enhance their appeal and inspire the creation of similar atlases, owing to the new \"experimental choropleth layer\" recently developed by the uMap team.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This approach focused on 3 main parts-data management (data collection, cleansing, and scheduling) and geomatic engineering through a 2-step geomatic action plan to create atlases of the first lockdown period in France displayed on the uMap platform. A logarithmically transformed variable allowed us to obtain an immediate statistical signal of excess mortality or submortality via the Traffic-Light Atlas.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The uMap Traffic-Light Atlas display provides instant statistical signals at a glance, owing to the semantic interplay of colors. The atlas's double legends make it easy to compare specific regions (northeast, northwest, southeast, and southwest) with all of France. The atlas revealed excess mortality in 41.7% (14,503/34,833) of the municipalities. Of the municipalities, 35% (12,198/34,833) were in the green class (close to average to 2 times the average), 5% (1724/34,833) were in the orange class (2-4 times the average), and 1.7% (581/34,833) were in the red class (4-11 times higher than average).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We innovated, enriched, and reinforced the value of uMap for visual rendering by instantiating colored choropleth map atlases and double legends and showed its relevance to the health care sector. We focused on the Traffic-Light Atlas, which is the most relevant aspect because of the instant message it conveys and its interpretability for all audiences. The uMap community can share our all-cause mortality atlases. A second version of the atlas encompassing 4 periods in 2020 and containing a minor error will be updated using either the \"experimental choropleth layer\" feature recently developed by the uMap team or, ","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e82855"},"PeriodicalIF":5.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Analysis of AI-Driven Cost Savings in UK and Australian Health Care Systems: Cross-Sector Implementation Study.","authors":"Jayanta Sarkar, Christopher Drovandi, Sandeep Reddy","doi":"10.2196/89113","DOIUrl":"10.2196/89113","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Health care systems face growing fiscal pressure while AI reaches clinical parity in several domains. UK National Health Service expenditure rose by 52%, while Australia's health expenditure grew by 29% between 2019 and 2023. Yet large-scale cost savings from AI remain limited, largely because implementation constraints continue to outweigh technical capability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study develops a Bayesian budget-impact framework to estimate AI-driven gross cost savings in radiology, workflow optimization, and workforce optimization in the United Kingdom and Australia, explicitly accounting for adoption uncertainty, effectiveness, and implementation risk.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used a sequential Monte Carlo simulation with 1000 particles to estimate annual gross cost savings. The core savings function combined expenditure base, sector weight, adoption, effectiveness, and implementation risk. Priors were informed by a structured review of multiple studies per domain. The savings likelihood used a heteroscedastic noise specification in which the SD followed an exponential prior, with the mean set to 15% of each sector's observed savings estimate, ranging from US $12.0 million for Australian radiology to US $87.2 million for UK workforce optimization. The likelihood was also augmented with sector-specific observations that anchored effectiveness to published cost-reduction estimates and implementation risk to observed deployment failure rates. Scenario analysis applied multipliers to 1000 bootstrap posterior draws across optimistic, conservative, and pessimistic settings. Sensitivity analyses varied the σ scaling factor from 0.10 to 0.20 and perturbed prior means for adoption, effectiveness, and implementation risk by ±20%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Posterior annual savings were US $949 million (95% credible interval [CrI] US $720.6-US $1173.5 million) for the United Kingdom and US $737 million (95% CrI US $526.1-US $953.6 million) for Australia. Workforce optimization generated the largest share of savings in both countries, contributing 62.4% of UK savings (US $591.9 million, 95% CrI US $398.8-US $854.8 million) and 76.2% of Australian savings (US $561.4 million, 95% CrI US $331.9-US $815.3 million). Posterior implementation risk estimates ranged from 35.5% to 47.4%, below prior means of 49.1% to 57.2%, reflecting the empirical anchoring introduced through deployment-failure data. Across scenarios, projected savings ranged from US $357 million to US $1.845 billion in the UK and from US $267.2 million to US $1.454 billion in Australia. Baseline cumulative projections for 2024-2030 were US $10.1 billion for the United Kingdom and US $8.0 billion for Australia. The sensitivity analyses confirmed the robustness of the posterior savings estimates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AI could generate substantial expenditure reductions in both health systems, but implementation risk r","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e89113"},"PeriodicalIF":5.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13559155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148834787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal Digital Phenotyping of Traditional Chinese Medicine Constitution From Routine Health Examination Records: Clinical Informatics Study.","authors":"Yuzhi Huo, Gao Deng, Li Kang, Haizhi Qin, Li Xiao, Ying Deng, Xin Chen, Dan Ding, Fei Wang, Mei Zhang, Min Chen","doi":"10.2196/100063","DOIUrl":"10.2196/100063","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Background: Traditional Chinese Medicine (TCM) constitution is a structured health-state taxonomy used in preventive care, but its relationship with routinely collected health examination data, disease-related markers, and longitudinal change remains difficult to interpret in clinical informatics settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Objective: This study aimed to develop and evaluate a longitudinal clinical informatics framework for characterizing TCM constitution as a computable, explainable, record-based phenotype using routine health examination records.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Methods: We conducted a retrospective longitudinal analysis of 47,417 examination-constitution records from 11,355 older adults examined between 2017 and 2026. Baseline analyses used the first available record per participant, and longitudinal analyses used 32,648 adjacent annual visit pairs. The framework included bidirectional disease-constitution mapping, nonoverlapping multimarker burden modeling, lagged next-visit association models, constitution-state transition analysis, and temporal evaluation of routine-examination-based label prediction models. Additional sensitivity analyses evaluated participant overlap across calendar-year splits, participant-disjoint temporal evaluation, BMI-only and BMI-plus-waist baselines, exclusion of anthropometric predictors, adiposity adjustment of longitudinal models, and new-onset and persistence outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Results: Phlegm-dampness showed the clearest record-based signature and was associated with higher cardiometabolic marker burden than balanced constitution (incidence rate ratio 1.65, 95% CI 1.60-1.70). In lagged models, its associations with a subsequent abdominal ultrasound abnormality flag and cardiometabolic risk clustering persisted after BMI adjustment, whereas associations with diabetes-related markers, proteinuria, dyslipidemia, and glucose abnormalities were substantially attenuated. Of 11,355 participants, 8122 appeared in at least two original calendar-year splits. In a participant-disjoint temporal sensitivity analysis with 1054 new test participants, XGBoost identified phlegm-dampness label presence with an area under the receiver operating characteristic curve of 0.927 (95% CI 0.912-0.941). A BMI-only model achieved 0.923 (95% CI 0.907-0.938), whereas XGBoost without anthropometric predictors achieved 0.685 (95% CI 0.653-0.717).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Conclusions: Routine health examination data captured a reproducible, predominantly adiposity-centered phlegm-dampness label phenotype in this older adult cohort. High discrimination was retained in participant-disjoint evaluation but was nearly matched by BMI alone, and several longitudinal associations were explained by adiposity. The models should therefore be interpreted as decision-support and communication aids for an existing constitution assessment process, not as stand","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building a National Interoperable Rare Eye Disease Data Warehouse: Methodological Framework and Implementation Report From the French Rare Eye Disease Database (FREDD) Initiative.","authors":"Camille Beluffi Marin, Marilyne Oswald, Laura Ratenet, Matthieu Stoll, Kirsley Chennen, Nathalie Chabin, Julie Hubert, Grégoire Rey, Guylène Le Meur, Vasily Smirnov, Isabelle Meunier, Isabelle Audo, Hélène Dollfus","doi":"10.2196/99206","DOIUrl":"10.2196/99206","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Rare eye diseases are characterized by low prevalence, clinical heterogeneity, and fragmented data collection, which limit the reliability of analysis results and multicenter research. In France, the development of health data warehouses is strictly regulated by national data protection authorities. While international initiatives aim to harmonize rare disease registries, capturing hyperspecialized, multimodal clinical records and ophthalmology-specific imaging within a fully compliant and sustainable infrastructure remains a major operational challenge.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to describe the methodological framework, regulatory implementation, and early operational outcomes of FREDD (French Rare Eye Disease Database), a national interoperable health data warehouse dedicated to rare eye diseases, and to analyze its key success factors, technical bottlenecks, and long-term financial sustainability models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Developed under the oversight of the French National Institute of Health and Medical Research (Institut national de la santé et de la recherche médicale; INSERM), the warehouse implements a centralized 3-layer architecture encompassing data collection, processing, and research reuse environments. The dataset extends the national minimum rare disease dataset with detailed ophthalmology variables structured through a dynamic electronic case report form, as well as a dedicated collection of ophthalmic images. Interoperability is achieved using international domain ontologies and a custom parsing tool (FREDDEX) designed to automatically prefill clinical data from the existing French national rare disease registry. Centralized monitoring, data curation, and cross-center quality tracking are driven by a dedicated in-house dashboard (FREDDIn [FREDD Insights]), while unstructured retinal images are processed through a standardized pseudonymization and human-verified validation pipeline.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Unlabelled: &lt;/strong&gt;Official regulatory authorization was obtained in April 2024, and active data collection began in March 2025 across 5 pilot expert centers, focusing its initial phase on inherited retinal dystrophies, primarily retinitis pigmentosa. Over a 1-year period, the warehouse successfully integrated clinical data from 1649 patients-representing 20.3% of all retinitis pigmentosa cases registered nationally-and accumulated more than 10,000 ophthalmic images. The centralized dashboard maintained an overall data inconsistency rate of approximately 7%, chiefly reflecting logical dependencies. Among the included patients, 36.7% (605/1649) patients had associated imaging data, with fundus photography being the most widely available modality (768/1649, 46.6%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The implementation of this database demonstrates that a highly specialized, multimodal health data warehouse can be successfully deployed in a highly regulated environment","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e99206"},"PeriodicalIF":5.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPT-4o-Powered Preanesthetic AI: Development and Validation Study.","authors":"Bo-Han Lin, Cheng-Wei Lu, I-Ching Hou, Tzu-Yu Lin, Chin-E Liu, Chong-Sun Khoi","doi":"10.2196/83893","DOIUrl":"10.2196/83893","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Accurate preanesthetic assessment is essential for perioperative risk stratification, but conventional tools such as the American Society of Anesthesiologists (ASA) physical status classification and postoperative nausea and vomiting (PONV) risk scores may be affected by subjective judgment, incomplete documentation, and fragmented clinical data. Large language models may support preanesthetic assessment by integrating structured and unstructured clinical information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and retrospectively validate a GPT-4o (OpenAI)-powered AI system for preanesthetic assessment. The primary validation focus was the agreement between AI-generated and clinician-assigned ASA physical status classifications. PONV risk stratification was evaluated as an additional clinically relevant performance outcome. A secondary objective was to assess the incremental contribution of National Health Insurance (NHI) cloud data to model performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This single-center retrospective validation study included 600 adult surgical patients randomly sampled from 4404 eligible inpatient surgical patients between January and May 2025. The system processed structured and unstructured electronic health record data, with and without NHI cloud data, to generate preanesthetic assessment outputs. ASA agreement was assessed using Cohen κ with 95% CIs. PONV prediction was evaluated using sensitivity, specificity, overall accuracy, positive predictive value, negative predictive value, confusion matrix analysis, the 2-sided Fisher-Freeman-Halton exact test, and Cramér V.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among the 600 patients, clinician-assigned ASA classifications were ASA I in 39 patients, ASA II in 498 patients, ASA III in 57 patients, and ASA IV in 6 patients. Agreement between AI-generated and clinician-assigned ASA classifications was high when NHI data were incorporated (κ=0.883, 95% CI 0.841-0.946), whereas agreement was lower without NHI data (κ=0.518, 95% CI 0.412-0.691). A total of 49 (8.2%) patients experienced documented PONV within 24 hours after surgery. Using the high-risk category as the primary test-positive threshold, the model achieved a sensitivity of 34.7% (95% CI 22.9-48.7), a specificity of 99.1% (95% CI 97.9-99.6), a positive predictive value of 77.3%, and a negative predictive value of 94.5%. The association between the model-assigned PONV risk categories and observed PONV outcomes was statistically significant (&lt;i&gt;P&lt;/i&gt;&lt;.001, Cramér V=0.531).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The GPT-4o-powered preanesthetic assessment system demonstrated feasibility and high agreement with clinician-assigned ASA classifications when NHI cloud data were incorporated. For PONV risk stratification, the system showed high specificity and a high negative predictive value but modest sensitivity, indicating that it identified a small high-risk subgroup with a high observed","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e83893"},"PeriodicalIF":5.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13506160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}