Journal of Medical Systems最新文献

{"title":"Does System Choice Matter? Influence of Hospital Information Systems on Digital Maturity.","authors":"Jonas Backes, Alexander Geissler, Jonas Subelack, David Ehlig","doi":"10.1007/s10916-026-02450-w","DOIUrl":"10.1007/s10916-026-02450-w","url":null,"abstract":"<p><p>Hospitals worldwide invest heavily in digital infrastructure to improve efficiency, safety, and patient-centeredness. Hospital Information Systems (HIS) form the backbone of these efforts. However, the HIS provider market remains fragmented and heterogeneous, thus potentially influencing hospitals' ability to achieve digital transformation. Empirical evidence on how HIS choice relates to digital maturity remains limited. Using data from more than 1,600 hospitals participating in the German-wide DigitalRadar (DR) project (i.e., covering about 90% of all hospitals), we examined the relationship between HIS provider characteristics and hospitals' digital maturity. The DR dataset comprises 234 standardized items per hospital and provides a maturity score from 0 to 100 for each hospital. Four HIS provider features (i.e., module utilization, integration ratio, external provider variation, and maximum hospital size coverage) were derived and used for k-means clustering. Multivariate linear regressions, controlling for hospital characteristics, quantified associations with digital maturity. K-means clustering identified four clusters of HIS providers. Higher module utilization (β = 6.87, p < 0.05) and integration ratios (β = 14.35, p < 0.01) were consistently associated with greater digital maturity. External provider variation and maximum hospital size coverage were not significantly associated with overall digital maturity. The choice of HIS provider is associated with hospitals' digital maturity, particularly with respect to broader module coverage and stronger integration. Providers with low module utilization and limited integration are associated with lower maturity. Our findings highlight the need for strategic HIS procurement, targeted system/organizational improvements, and policy incentives to accelerate digital transformation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13452863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697642","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":"Temporal Variability in Diagnosis Code Distributions Across Extraction Time Points in a Multicenter Integrated EHR Database: A Snapshot Comparison Study.","authors":"Kyunghee Lee, Chihiro Kumagai, Masato Komuro, Kengo Miyo, Hiroyuki Hoshimoto","doi":"10.1007/s10916-026-02447-5","DOIUrl":"10.1007/s10916-026-02447-5","url":null,"abstract":"<p><p>EHR data are widely used in clinical research; however, their temporal stability remains poorly understood. This study aimed to evaluate temporal variability in EHR data arising from differences in extraction time points. We conducted a retrospective, descriptive, observational study using diagnostic data from a multicenter integrated EHR database in Japan. The target period was 2022, and diagnosis records with a start date within this period were extracted at three time points: October 2023, October 2024, and October 2025. Diagnoses were mapped to the three-character level of the International Classification of Diseases, 10th Revision (ICD-10). Changes in diagnosis code distributions were quantified using the Jensen-Shannon distance (JSD). Changes in record counts per ICD-10 code between 2023 and 2025 were also evaluated. Five facilities were included in the analysis. In the integrated dataset, variations in total records and unique patients were minimal (total records: +0.06% in 2024 and + 0.05% in 2025; unique patients: +0.06% in 2024 and + 0.02% in 2025). JSD values for ICD-10 distributions were non-zero across all facilities, indicating that diagnosis code distributions differed across extraction time points even for the same target period. Across all facilities excluding Facility C, JSD was 0.0022 in 2024 and 0.0026 in 2025 relative to the 2023 baseline. In contrast, Facility C exhibited a markedly larger JSD in 2024 (0.0310), which persisted at a similar level in 2025 (0.0317). At the code level, H61 showed the largest absolute change (- 266 records; -19.98%). Overall, acute conditions tended to decrease, whereas chronic conditions increased across extraction time points. EHR data exhibit temporal variability across extraction time points, even for the same target period. These findings highlight the importance of documenting extraction timing and dataset version, and suggest the need for study designs that explicitly account for such variability.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13442496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678932","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":"When Useful Clinical AI Exceeds Meaningful Oversight.","authors":"Jonas Ver Berne, Reinhilde Jacobs","doi":"10.1007/s10916-026-02446-6","DOIUrl":"10.1007/s10916-026-02446-6","url":null,"abstract":"<p><p>Human oversight is widely invoked as a safeguard in clinical AI, yet it is often treated as a single, stable property. In practice, clinicians remain responsible for AI-assisted decisions without remaining able to meaningfully review the basis of the output. This tension becomes sharper as systems become multimodal, agentic, and cognitively ambitious. We propose a distinction between cognitive and normative oversight and discuss its implications for AI evaluation and deployment.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648837","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":"Automated Multi-Label Adverse drug Reaction Extraction from Malaysia's National Pharmacovigilance Narratives.","authors":"Sze Gee Lim, Maizatul Akmar Ismail","doi":"10.1007/s10916-026-02448-4","DOIUrl":"10.1007/s10916-026-02448-4","url":null,"abstract":"<p><p>Extracting multiple adverse drug reaction (ADR) terms from unstructured narratives remains challenging, particularly under severe label imbalance that limits the detection of rare ADRs. This study aimed to develop and evaluate a multi-label natural language processing framework for automated ADR extraction within Malaysia's national pharmacovigilance reporting system. We evaluated classical machine learning and transformer-based models within a common framework, incorporating domain-specific preprocessing and imbalance-aware optimization strategies to improve the detection of rare ADRs. The framework was applied to 28,980 real-world ADR narratives annotated with 80 Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs) from the Skin and Subcutaneous Tissue Disorders System Organ Class (SOC). Performance was evaluated using micro-F1, macro-F1, precision, recall, and label coverage using a predefined 80/20 train-test evaluation strategy, complemented by pharmacist pilot review. Transformer-based models achieved the strongest performance, with the augmented model attaining a micro-F1 of 0.88, macro-F1 of 0.55, recall of 0.88, and the highest label coverage, correctly predicting 60 of 80 PTs (75%). However, a 70/10/20 sensitivity analysis on the improved transformer model yielded lower micro-F1 and precision, highlighting the impact of further data partitioning on model performance. Improved classical models also showed substantial gains over their baseline models, particularly in micro-F1, macro-F1, recall, and label coverage, consistent with improved detection of rare ADRs. During the pilot review, pharmacists accepted the model-preselected PTs without modification in 19 of 25 narratives (76%), while additional PTs were added in the remaining cases. Although the study was limited to a single SOC and one national pharmacovigilance database, the findings demonstrate that optimization strategies, domain-specific feature enrichment, and targeted augmentation can substantially improve multi-label ADR classification under severe imbalance. These findings support further evaluation of AI-assisted pharmacovigilance with pharmacist oversight in real-world practice.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630690","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":"Governing Clinical Readiness Claims Derived from Medical AI Benchmark Results.","authors":"Yin Dong, Jiwei Cheng, Chao Ding, Renjie Lu","doi":"10.1007/s10916-026-02445-7","DOIUrl":"10.1007/s10916-026-02445-7","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604094","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":"A Blueprint for Agentic Workflow Support in Dynamic SPECT Myocardial Perfusion Imaging.","authors":"Enyu Yang, Teck Soon Seah, Si Yong Yeo, Yong Jin Tan, Abigail Pc Wong, Xuefen Teng, Felix Yj Keng, Ru-San Tan, Angela S Koh","doi":"10.1007/s10916-026-02444-8","DOIUrl":"10.1007/s10916-026-02444-8","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549455","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":"Multi-Axial Analysis of Clinical Reasoning in Large Language Models: Inter-Verifier Disagreement and Its Implications for Automated Evaluation.","authors":"Hyunjung Byun, Dahyoun Lee, Munyoung Jung, Beakcheol Jang","doi":"10.1007/s10916-026-02440-y","DOIUrl":"https://doi.org/10.1007/s10916-026-02440-y","url":null,"abstract":"<p><p>Evaluating clinical reasoning in large language models (LLMs) poses two open challenges: reference-oriented semantic metrics do not directly assess whether a model's stated diagnosis is supported by the evidence in its own justification, and the increasingly popular LLM-as-judge approach rests on a largely untested assumption-that independent verifier LLMs agree with one another. We assess three generator LLMs (HuatuoGPT-o1-8B, Meta-Llama-3.1-8B-Instruct, Meta-Llama-3.3-70B-Instruct) on 1,000 MIMIC-IV hospital-stay cases along four complementary axes (medical concept grounding, semantic similarity, semantic uncertainty, and evidence-conclusion coherence), with coherence judged independently by three frontier verifiers (Claude Sonnet 4.6, Gemini 2.5 Pro, GPT-5.4 mini). Two findings emerge. First, coherence reveals a dissociation that reference-oriented metrics do not capture: a model can score well on those axes yet still produce rationales that do not support its own conclusions. Second, inter-verifier agreement on coherence is consistently low (Fleiss' κ 0.087-0.223; disagreement 62.2%-74.3%), so the same rationale can be judged supported or unsupported depending on the verifier. A preliminary validation in which a physician adjudicated 50 cases echoed this: agreement with the physician varied across verifiers, underscoring that no single LLM reliably stands in for clinical assessment. Together, these results suggest a single LLM verifier lacks sufficient reliability to serve as a stand-alone judge of clinical reasoning at scale, and that structured human oversight remains essential. The unanimous-agreement tier offers a candidate for selective automation, but its clinical reliability remains to be confirmed in larger, multi-clinician adjudication studies.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549451","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":"Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients' Historical Data.","authors":"Seung Jin Maeng, Ye Rim Lee, Se Uk Lee, Jae Yong Yu, Jung Won Choi, Guntak Lee, Jong Eun Park, Tae Gun Shin, Sung Yeon Hwang, Hee Yoon, Won Chul Cha, Taerim Kim, Minha Kim, Hansol Chang, Sejin Heo","doi":"10.1007/s10916-026-02443-9","DOIUrl":"10.1007/s10916-026-02443-9","url":null,"abstract":"<p><p>This study aimed to develop an interpretable machine learning-based scoring system for predicting sepsis and septic shock among febrile patients at emergency department (ED) triage using longitudinal data. This retrospective, single-center study included adult patients, presented to ED of tertiary academic hospital with fever from January 2016 to December 2021. Using the AutoScore framework, we developed a novel scoring system for predicting sepsis and septic shock at the triage stage, incorporating nine variables and a maximum score of 29. The predictive performance of our score was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and its performance was compared with that of two existing scoring systems: the quick Sequential Organ Failure Assessment (qSOFA) and the Modified Early Warning Score (MEWS). Our model incorporated nine variables including initial vital signs, age, baseline platelet count, total bilirubin, and creatinine levels. Among these, initial systolic blood pressure was identified as the most important predictor. AUROC of our model was 0.844 (95% confidence interval [CI], 0.812-0.875) in predicting septic shock and 0.703 (95% CI, 0.687-0.720) for sepsis. Compared to qSOFA ≥ 2 (AUROC: 0.605) and MEWS ≥ 5 (AUROC: 0.678), our scoring system demonstrated superior predictive performance for septic shock. For comparable specificity levels (ranging from 0.50 to 0.95), our scoring system achieved higher sensitivity than MEWS. Our scoring system is an interpretable and practical scoring tool for predicting sepsis and septic shock among patients with documented fever at ED triage using patient's longitudinal data.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549394","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":"Measuring Digital Transformation in Chinese Hospitals: Development and Validation of a Digital Maturity Evaluation Framework.","authors":"Xinyi Liu, Xiao Han, Jianjun Chen, Guanghua Zhou, Guohong Li, Xianqun Fan","doi":"10.1007/s10916-026-02438-6","DOIUrl":"10.1007/s10916-026-02438-6","url":null,"abstract":"<p><p>Digital transformation is a key priority for modernizing China's public hospitals. However, a standardized and context-specific framework to evaluate their digital maturity remains absent. This study aims to develop and validate a comprehensive, multidimensional evaluation framework tailored to Chinese tertiary public hospitals to support systematic assessment and inform policy decisions. Based on systematic literature review and policy analysis, we constructed a framework comprising Digital Readiness, Technology Application, and Data Management Capability, with 11 subdimensions and 65 indicators. Indicator weights were derived using a two-round Delphi consultation, analytic hierarchy process, and criteria importance through intercriteria dependence method. The framework was applied to 1,361 tertiary public hospitals across 28 mainland provincial-level divisions in China. Digital maturity scores were analyzed using global sensitivity analysis (GSA), k-means clustering, and logistic regression with Firth's penalized likelihood. Digital Readiness received the largest combined weight (34.9%), followed closely by Technology Application (34.7%) and Data Management Capability (30.4%), suggesting that hospital digital maturity reflects a balanced combination of organizational readiness, technology-enabled service application, and data governance. GSA further revealed discrepancies between combined weights and empirical sensitivity rankings, indicating that these approaches captured different aspects of indicator importance. Clustering and regression analyses showed that higher-maturity hospitals had higher values across many indicators, particularly in clinical digital applications and data quality management, whereas data sharing and exchange remained relatively weak across maturity groups. Robustness checks using alternative weighting and clustering methods generally supported the stability of the main findings, while also indicating residual sensitivity to methodological choices. The proposed framework provides a structure- and process-oriented diagnostic tool for assessing digital maturity in Chinese tertiary public hospitals. It can support policy monitoring, institutional benchmarking, and targeted improvement of hospital digital transformation. Future research should update the framework with more recent data and validate maturity scores against healthcare quality, safety, efficiency, patient experience, and equity outcomes.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520596","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":"Mapping Rare Disease Registries in Brazil: Situational Analysis and Proposal for National Unification.","authors":"Filipe Andrade Bernardi, Bibiana Mello de Oliveira, Natan Monsores de Sá, Domingos Alves, Têmis Maria Félix","doi":"10.1007/s10916-026-02442-w","DOIUrl":"10.1007/s10916-026-02442-w","url":null,"abstract":"<p><p>Rare disease registries in Brazil remain fragmented across federal, state, and local initiatives, limiting the availability of reliable epidemiological information to support diagnosis, care planning, research, and public policy. This study aimed to map existing rare disease registry entities and registry-related initiatives in Brazil and to propose practical guidelines for their unification into an integrated national registry. We conducted a descriptive, exploratory mapping study combining a structured literature search with documentary analysis of public policies, health information systems, registry portals, institutional reports, and legislative documents related to rare diseases in Brazil. PRISMA-S was used to report the search component, and a PRISMA-style flow diagram documented source identification and selection. We identified a rapidly evolving legislative landscape, including federal bills proposing a national monitoring system or registry and recent state-level statutes related to identification and observatories. Using an expanded, auditability-oriented inventory definition, we mapped 28 registry entities and registry-related initiatives. Of these, 24 are implemented, three are legislative proposals, and one is under development. Among the 24 implemented initiatives, 16 are national or multicentre, Brazil-based initiatives; three are state-level; four are regional/local; and one is a transnational registry with documented participation of a Brazilian cohort. Registry creation and registry-related activity accelerated after 2018, particularly between 2020 and 2026. We conclude that Brazil exhibits substantial data fragmentation across uncoordinated systems. A unified approach should integrate epidemiological data from existing networks, state notification systems, specialised hospital registries, and technology appraisal information under coordinated governance, while embedding privacy-by-design and information security safeguards.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520543","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}