JMIR Medical Informatics最新文献

{"title":"Multioutput Machine Learning Model for Predicting Postoperative Outcomes After Liposuction: Algorithm Development and Validation Study in a Multicenter Cohort.","authors":"Chaewoo Lee, Seoyoung Park, Jiyoung Hwang, Selin Woo, Youn Chan Park, Jae Won Seo, Sun Ho Lee, Jae Hyun Ahn, Dong Keon Yon, Sang Youl Rhee","doi":"10.2196/82145","DOIUrl":"10.2196/82145","url":null,"abstract":"<p><strong>Background: </strong>Liposuction is widely performed to remove localized fat deposits and improve body contour, yet individualized prediction of postoperative outcomes remains challenging. Existing machine learning (ML) studies have largely focused on single-outcome prediction, with limited attention to the interdependence between postoperative body weight and circumferential size.</p><p><strong>Objective: </strong>This study aimed to develop and validate a chained multioutput ML framework to jointly predict postoperative body weight and circumferential size after liposuction using a large multicenter cohort from the 365mc network.</p><p><strong>Methods: </strong>We analyzed a multicenter cohort of 7804 individuals who underwent liposuction in 2024 at 20 obesity specialty clinics in the 365mc network across South Korea. Using 15 predictors, we compared 8 individual ML models, an automated ML approach, 2 ensemble approaches, and chained multioutput regression models for predicting postoperative body weight and circumferential size. Models were developed using 5-fold cross-validation and evaluated on an independent test set. Performance was assessed using the coefficient of determination (<i>R</i>²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), and feature importance was evaluated using Shapley additive explanation (SHAP) values. The selected model was integrated into a web-based clinical decision support system (CDSS).</p><p><strong>Results: </strong>A total of 7804 individuals who underwent liposuction were included; of these, 7612 (97.54%) were female. The chained extra trees regressor model with a weight-to-size prediction order achieved an <i>R</i>² of 0.98, an RMSE of 2.36, an MAE of 1.24, and a MAPE of 2.19. The SHAP analysis identified preoperative weight as the main predictor of postoperative body weight and preoperative size and liposuction-related factors as key predictors of postoperative circumferential size. The final model was integrated into a web-based CDSS (365mc AI platform).</p><p><strong>Conclusions: </strong>We developed and validated a chained multioutput regression model to predict postoperative body weight and circumferential size after liposuction. Integrated into a web-based CDSS, the model may support patient-specific preoperative counseling and surgical planning.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e82145"},"PeriodicalIF":5.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032210","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: Retrospective 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;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;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;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 pairs of adjacent annual visits. 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;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 2 original calendar-year splits. In a participant-disjoint temporal sensitivity analysis with 1054 new test participants, extreme gradient boosting (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;Routine health examination data captured a reproducible, predominantly adiposity-centered phlegm-dampness label phenotype in this cohort of older adults. High discrimination was retained in participant-disjoint evaluation but was nearly matched by the BMI-only model, 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 and not as stand-alone d","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e100063"},"PeriodicalIF":5.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032215","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 Hierarchical Machine Learning-Based Framework for Clinical Decision Support in Foot Orthosis Prescription: Algorithm Development and Validation Study.","authors":"Ji-Yong Jung, Wooyeol Yang, Jung-Ja Kim","doi":"10.2196/92831","DOIUrl":"10.2196/92831","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Foot orthosis prescription is a complex clinical decision-making process that involves selecting and combining multiple structural, functional, and material components based on heterogeneous biomechanical information. In routine outpatient practice, detailed biomechanical assessments are often incomplete, creating substantial variability in prescription decisions, and limiting the applicability of conventional machine learning (ML) models that assume fixed feature availability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and evaluate a hierarchical ML-based clinical decision support framework for foot orthosis prescription that accommodates variable clinical information availability, supports multilabel prescription decisions, and incorporates safety-oriented recommendation strategies for routine outpatient practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A retrospective observational study was conducted using 6462 visit-level clinical encounters collected from a single institution between 2015 and 2020. Orthotic prescription was formulated as a multilabel prediction task involving 15 prescription components. A hierarchical modeling framework was implemented, consisting of a basic decision level using routinely available demographic and alignment variables, and an advanced level incorporating subtalar joint inversion and eversion range of motion measurements when available. Tree-based gradient boosting models were evaluated using 5-fold stratified grouped cross-validation. Model performance was assessed using component-wise area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), positive predictive value (PPV), macro-averaged Hamming loss, Top-K hit, and coverage rates based on probabilities calibrated using internal grouped Platt scaling, and component-specific safety-oriented threshold calibration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The hierarchical models demonstrated consistent predictive performance across both decision levels, although precision-recall-based analyses indicated greater variability for low-prevalence prescription components. The Level 4 and Level 8 models achieved mean component-wise AUROC values of 0.792 and 0.816, respectively, with macro-averaged Hamming loss of 0.113 and 0.111. Using component-specific Platt scaling within the 5-fold stratified grouped cross-validation framework, Top-K analysis demonstrated a Top-1 hit rate of 86.7% and a Top-3 hit rate of 97.4%. When all prescribed components were considered, Top-K coverage reached 71.7% at Top-3 and increased to 97.0% at Top-8. Safety-oriented threshold calibration was performed separately for each prescription component, selecting the threshold that achieved the highest sensitivity while maintaining a specificity of at least 95%. Under this component-specific calibration strategy, the aggregated operating profile achieved a microaveraged specificity of 96.00% with a microaver","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e92831"},"PeriodicalIF":5.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026832","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":"Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.","authors":"Jihye Kim Scroggins, Ismael Ibrahim Hulchafo, Veronica Barcelona, Anahita Davoudi, Hans Moen, Sarah Harkins, Danielle Scharp, Kenrick Cato, Michele Tadiello, Maxim Topaz","doi":"10.2196/85088","DOIUrl":"10.2196/85088","url":null,"abstract":"<p><strong>Background: </strong>Natural language processing can extract meaningful information from clinical notes. However, human annotation is time-consuming and costly, and scarce data poses a challenge.</p><p><strong>Objective: </strong>This study aimed to explore a semantic similarity search approach to extract exemplars of stigmatizing and positive language in obstetric clinical notes.</p><p><strong>Methods: </strong>We used electronic health record data from labor and birth admissions at 2 hospitals in the United States from 2017 to 2019. We used a semantic similarity search approach, which used 200 randomly selected true exemplars, stratified by language categories, as queries to search for similar exemplar candidates. We extracted the top 5 candidates with the highest cosine similarities, which were assessed for accuracy.</p><p><strong>Results: </strong>We retrieved 1000 candidates. An average precision of 0.69 was achieved when candidates with cosine similarity thresholds of 0.75 or higher were included, at which point 68.8% (64/93) of exemplar candidates accurately represented true cases. At the 0.75 threshold, the proportion of true cases was higher for preferred language (41/56, 73.2%) and unilateral/authoritarian decisions (5/7, 71.4%). The proportion of true cases was lower for difficult patients (2/7, 28.6%) and marginalized identities (3/9, 33.3%).</p><p><strong>Conclusions: </strong>The semantic similarity search approach shows promise in efficiently extracting exemplars while reducing the annotation burden, laying the groundwork for future applications in other domains.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e85088"},"PeriodicalIF":5.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026903","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":"Development and Validation of a Machine Learning-Based Model for Predicting All-Cause Mortality Risk in Patients With Type 2 Diabetes Mellitus Combined With Hypertension: National Cohort Study.","authors":"Wenlong Ding, Lei Fang, Cunming Fang","doi":"10.2196/85557","DOIUrl":"10.2196/85557","url":null,"abstract":"<p><strong>Background: </strong>Type 2 diabetes mellitus (T2DM) combined with hypertension significantly increases mortality risk, yet accurate risk prediction models remain limited.</p><p><strong>Objective: </strong>We aimed to develop and validate machine learning-based models to predict all-cause mortality in patients with T2DM and hypertension.</p><p><strong>Methods: </strong>We analyzed data from the National Health and Nutrition Examination Survey from 1999 to 2018 linked with mortality data up to December 31, 2019. Adult participants (aged ≥20 years) with concurrent T2DM and hypertension were included. Five machine learning algorithms were developed and compared: random forest, light gradient boosting machine, decision tree, extreme gradient boosting, and logistic regression. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis.</p><p><strong>Results: </strong>A total of 2428 participants were included (mean age 62.05, SE 0.33 years; n=1218, 50.15% female). During a median follow-up of 6.75 (IQR 4.20-8.90) years, among the 2428 patients, 719 (29.6%) deaths occurred. The random forest model demonstrated superior performance (AUC=0.873, 95% CI 0.856-0.891) compared to light gradient boosting machine (AUC=0.785), decision tree (AUC=0.732), extreme gradient boosting (AUC=0.792), and logistic regression (AUC=0.783). Key predictive features included age, race, chronic kidney disease, BMI, and blood urea nitrogen. The model exhibited excellent calibration and clinical utility across various risk thresholds.</p><p><strong>Conclusions: </strong>Our machine learning-based model provides accurate all-cause mortality prediction for patients with T2DM and hypertension, potentially supporting clinical decision-making and risk stratification in this high-risk population.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e85557"},"PeriodicalIF":5.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026800","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":"Development and Validation of Machine Learning Models for Postjudgment Estimation of High Compensation Ratios After Lower Limb Fracture Surgery: Retrospective Study.","authors":"Lixuan Song, Siwen Zhao, Yuanzheng Deng, Ruqin Yang, Hongyang Wang, Shumin Zhang, Wenjun Li, Wen Wen, Zijian Lv, Hongtao Lei, Taipin Guo","doi":"10.2196/93801","DOIUrl":"10.2196/93801","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Orthopedic surgery is the second most common subspecialty involved in medical malpractice claims, wherein lower limb surgery carries a higher risk of claims and involves higher compensation amounts. However, effective tools for postjudgment estimation of high compensation ratios and consistency assessment against prior similar cases after lower limb fracture surgery are currently lacking in medicolegal risk management and judicial practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and validate multiple machine learning (ML) models to estimate a medical malpractice compensation ratio of ≥50% in postjudgment, nonfinalized medicolegal cases, and to systematically evaluate the models' discriminative ability, stability, calibration performance, and medicolegal utility.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study developed binary classification models based on 451 medical malpractice cases after lower limb fracture surgery in China from 2004 to 2025. Among these cases, 360 cases from eastern, northeastern, and central China constituted the development dataset and were randomly split at a 7:3 ratio into a training set (n=251) and an internal test set (n=109), whereas 91 cases from western China were held out as an independent geographical external validation set. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection. Logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were trained using 5-fold cross-validation and grid search. Model performance was assessed using the receiver operating characteristic (ROC) curve, area under the receiver operating characteristic curve (AUROC), bootstrap resampling, calibration curves, and decision curve analysis (DCA).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;LASSO identified following 8 predictors: inappropriate surgical procedure, age, inadequate medical records, lack of informed consent, disability severity grade 1-4, sex, treatment delay, and inadequate preoperative preparation. In the test set, the LR model achieved an AUROC of 0.933, with recall, precision, accuracy, and F&lt;sub&gt;1&lt;/sub&gt;-score of 0.810, 0.940, 0.872, and 0.870, respectively. Inappropriate surgical procedure and lack of informed consent were the strongest model-associated factors. Bootstrap analysis showed stable LR discrimination, and calibration was favorable, with a Brier score of 0.105. DCA suggested a potential reference value across threshold probabilities. Considering discrimination, calibration, classification performance, parsimony, and interpretability, the LR model was selected as the final model. External validation provided preliminary support for cross-regional transportability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;ML-based models for postjudgment estimation may provide a nonbinding historical benchmark for estimating whether a compensation ratio of ≥50% is broadly consistent","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e93801"},"PeriodicalIF":5.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026916","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":"Development and Validation of an Interpretable Machine Learning Model to Predict Mortality in Patients With Sepsis-Induced Coagulopathy: Multicenter Cohort Study.","authors":"Jiaxuan Sun, Jingyuan Wang, Yuxin Dong, Jieyu Liu, Yiyun Ding, Shilin Fan, Dongyue Chen, Songtao Shou","doi":"10.2196/90285","DOIUrl":"10.2196/90285","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Sepsis-induced coagulopathy (SIC) is a common and severe complication in patients with sepsis, characterized by microvascular thrombosis, systemic endothelial damage, and markedly increased short-term mortality. Existing traditional clinical risk scoring systems demonstrate limited accuracy and fail to capture complex, nonlinear physiological interactions, underscoring the urgent need for advanced prognostic tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective of this study was to develop and validate an interpretable machine learning (ML) model using large-scale, multicenter databases to predict early mortality in intensive care unit (ICU) patients with SIC and to evaluate its predictive performance and clinical utility compared with traditional clinical risk scores.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The study retrospectively analyzed clinical data of patients with SIC from the Medical Information Mart for Intensive Care IV (MIMIC-IV), the eICU Collaborative Research Database (eICU-CRD), and Tianjin Medical University General Hospital. Feature selection was performed using LASSO (least absolute shrinkage and selection operator) regression, the Boruta algorithm, and recursive feature elimination with cross-validation, combined with multivariable logistic regression. Twelve ML algorithms were trained and compared with 4 traditional clinical scoring systems (Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II, and Oxford Acute Severity of Illness Score) to predict 28-day mortality after ICU admission. Model discrimination, calibration, and clinical utility were assessed using the area under the curve (AUC), calibration curves, and decision curve analysis. Furthermore, Shapley additive explanations (SHAP) values were used to ensure model interpretability and to identify individual pathophysiological drivers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 7980 (66.3%) patients with SIC from the MIMIC-IV database, 3815 (31.7%) from the eICU-CRD database, and 235 (2.0%) from Tianjin Medical University General Hospital were included in the study. Ten independent predictors were identified to construct the model. The XGBoost (extreme gradient boosting) model performed the best, with an AUC of 0.899 (95% CI 0.889-0.909) in the internal validation set and 0.882 and 0.904 in the 2 external validation sets, significantly outperforming traditional scoring systems and demonstrating higher clinical net benefit. SHAP analysis identified that the top 5 critical features were anion gap, red blood cell distribution width, lactate, total bilirubin, and age. Furthermore, an easy-to-use online tool, SIC Predict Streamlit, was developed based on this model to enable clinicians to quickly assess patient risk.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The ML model developed in this study demonstrated superior accuracy, robustness, and generalizability in predicting early mortal","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e90285"},"PeriodicalIF":5.0,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13592666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018810","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":"Hospital Human Resource Managers' Perspectives on Organizational Readiness for Generative AI Skills: Qualitative Descriptive Study.","authors":"Zhuo Gao, Deliang Liu, Yanxia He, Yan Zhao, Yan Zuo, Jianjun Zhang, Jingjing Zheng","doi":"10.2196/74339","DOIUrl":"10.2196/74339","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative AI (GenAI) is increasingly entering health care through documentation support, communication tools, educational content generation, and other knowledge-intensive functions. However, organizational adoption remains uneven, and concerns related to privacy, security, output reliability, governance, workflow fit, and infrastructure continue to limit broader implementation. Although the literature increasingly discusses the skills health care workers may need in the GenAI era, less is known about how hospital managers view the organizational readiness required before such skills can be expected across the workforce. Hospital human resource (HR) managers are especially important in this regard because they are involved in training, competency development, workforce planning, and organizational change.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore how hospital HR managers perceived the relevance of GenAI-related skills, the organizational barriers to broader implementation, and the forms of preparation they considered realistic in the current stage of adoption.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a descriptive qualitative study in 2 tertiary hospitals in China-1 in Beijing and 1 in Sichuan Province. Purposive sampling was used to recruit HR managers involved in staffing, training, competency development, or workforce planning. Semistructured telephone interviews were conducted between January 15 and February 9, 2025. Interviews were audio-recorded, transcribed verbatim, and analyzed using inductive thematic analysis. The researchers familiarized themselves with the transcripts, generated initial codes, grouped related codes into categories, and developed themes through iterative comparison, team discussion, and refinement. Rigor was supported through reflexive memoing, audit trail documentation, team debriefing, and participant validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Fifteen HR managers participated. Participants did not describe mature or formalized GenAI competency systems within their institutions. Instead, they described an early-stage organizational environment characterized by strategic recognition of GenAI, informal and uneven experimentation, uncertain governance, and substantial variation in readiness across roles and settings. Three themes were identified: (1) GenAI was viewed as increasingly inevitable and relevant to hospital work, but not yet as a stable universal competency requirement; (2) organizational unreadiness, including infrastructure limitations, lack of approved systems, unclear policy boundaries, and workforce heterogeneity, constrained any move toward universal mandates; and (3) participants preferred phased, role-specific preparation over immediate compulsory standards. They emphasized that readiness depended on secure access, governance, workflow alignment, and differentiated training pathways rather than broad top-down requirements.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclus","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e74339"},"PeriodicalIF":5.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992612","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":"Digitalization of the Mandatory Notifications System for Infectious Diseases in Germany: Implementation of DEMIS (the German Electronic Reporting and Information System).","authors":"Beneditta Suwono, Olaf Rode, Katarina Birghan, Holger Ahl, Eberhard Pape, Doreen Krause, Michaela Diercke, Claudia Sievers","doi":"10.2196/89400","DOIUrl":"10.2196/89400","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mandatory notifications of infectious diseases in Germany were paper based until the implementation of DEMIS (Deutsches Elektronisches Melde- und Informationssystem für den Infektionsschutz; German Electronic Reporting and Information System) in 2020. DEMIS provides technical infrastructure for electronic reporting of infectious diseases to public health authorities, with the goal of streamlining processes through automation and reducing the burden of manual data entry.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;DEMIS provides Fast Healthcare Interoperability Resources (FHIR)-based interfaces for mandatory notification systems that can be accessed by multiple authentication methods for different user groups, such as laboratories, hospitals, and public health authorities. In this paper, we describe the implementation of DEMIS from conceptualization to deployment and evaluation as well as the associated challenges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Conceptualization of DEMIS was initiated in 2012 by the Robert Koch Institute, Fraunhofer FOKUS, and the German Federal Ministry of Health. The implementation was accelerated during the COVID-19 pandemic in 2020 with support from gematik, Germany's national digital health agency. DEMIS uses FHIR interfaces and standardized terminologies, such as LOINC (Logical Observation Identifiers Names and Codes) and SNOMED CT (Systematized Nomenclature of Medicine-Clinical Terms), to digitally submit basic information on notifiers and patients along with specific diagnostics, clinical data, and epidemiological information from physicians and laboratories to public health authorities. The use of FHIR should ensure interoperability with other surveillance systems. DEMIS provides access either directly via an FHIR-based API or through a web portal. Then, DEMIS notifications will be integrated into the recipient's specialized software. The deployment of FHIR packages in DEMIS will be published on simplifier.net. Finally, the notification data coming via DEMIS will be monitored and evaluated thoroughly. The whole process from implementation to deployment should be supported with active communication between stakeholders.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Unlabelled: &lt;/strong&gt;The first surveillance module was deployed for SARS-CoV-2 notifications during the COVID-19 pandemic in 2020. Since then, DEMIS availability has been over 95% per month, and usually above 99%. As of August 2025, there were 697 laboratories, 1670 hospitals, and 870 other institutions such as private test centers, pharmacies, and general practitioners in Germany that have accessed DEMIS via the API. DEMIS has handled roughly 50 million pathogen notifications and 500,000 disease notifications to date, routed to all 376 local public health authorities. For notifiable pathogens, data quality has been routinely assessed and reported back to the laboratories. On average, 92% (92,378/100,579) of the notifications included a LOINC code.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclus","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e89400"},"PeriodicalIF":5.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992661","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":"Prediction Models for In-Hospital Delirium Using Routinely Collected Electronic Health Record Data: Systematic Review.","authors":"Hung-Min Huang, Chun-Shun Lu, Geng-Wei Chang, Ming-Hsu Tien, Yu-Kai Hsu","doi":"10.2196/91618","DOIUrl":"10.2196/91618","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Delirium is a common and clinically important form of acute in-hospital mental status deterioration. Electronic health record (EHR)-based prediction models may support early identification and targeted prevention, but their methodological quality, validation rigor, and clinical readiness remain uncertain.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This systematic review aimed to synthesize and critically evaluate prediction models for in-hospital delirium developed using routinely collected EHR data, focusing on model characteristics, validation strategies, performance, risk of bias, and clinical applicability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched PubMed, MEDLINE, Embase, PsycINFO, and Web of Science from inception to November 11, 2025. Eligible studies developed, validated, or evaluated multivariable prediction models using routinely collected EHR or administrative data to predict acute mental status deterioration during adult hospital admissions. Although eligibility criteria were broad, all included studies operationalized deterioration as delirium. Data extraction was informed by CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) or TRIPOD-artificial intelligence guidance. Model performance, validation, calibration, and implementation features were synthesized narratively. Risk of bias and applicability were assessed using PROBAST (Prediction Model Risk of Bias Assessment Tool).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Twenty-nine studies met the inclusion criteria. The evidence clustered into 4 overlapping prediction tasks: admission or early-stay risk stratification, perioperative or postoperative prediction, dynamic intensive care unit prediction, and external validation or workflow evaluation of existing tools. Most studies were retrospective cohorts (20/29, 69%) and were conducted in general ward, mixed ward-intensive care unit, intensive care unit, or emergency department settings. Machine learning or hybrid approaches were common (18/29, 62%), but more complex models did not consistently outperform statistical or rule-based approaches. Of 29 studies, internal discrimination was reported in 24 (83%; area under the receiver operating characteristic curve range 0.77-0.97) studies, whereas external discrimination was reported in 12 studies and calibration in 15 studies. Decision curve analysis was reported in 3 studies, and prospective evaluation or workflow integration remained limited. Overall risk of bias was low in 8 studies, unclear in 10 studies, and high in 11 studies, mainly because of analysis-domain limitations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Routinely collected EHR data can support delirium risk prediction across hospital settings, and many models show moderate to high discrimination. However, no single algorithm is ready for routin","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e91618"},"PeriodicalIF":5.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13581283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148984222","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}