Methods of Information in Medicine最新文献

{"title":"The Roles of Biomedical Informatics for an Equitable Learning Health System: Lessons from a Year of Scientific Informatics Literature.","authors":"Humayera Islam, Pushkala Jayarman, Nick F H Reid, Pratishtha Guckhool, Malin Britt-Lalich, Parand Shams, Zihan Xu, James J Cimino","doi":"10.1055/a-2937-0980","DOIUrl":"10.1055/a-2937-0980","url":null,"abstract":"<p><strong>Background: </strong>Biomedical informatics plays a central role in supporting learning health systems (LHSs) that aim to continuously improve care by transforming data into knowledge and knowledge into action. However, health care data reflect existing disparities in access, delivery, and outcomes, raising concerns about whether the LHS cycle can improve care equitably without intentional design.</p><p><strong>Objective: </strong>This study aims to describe the recurring theme of health equity found in recent biomedical informatics literature, using a convenience sample of articles contributed to the 2025 American Medical Informatics Association (AMIA) Year in Review (YIR).</p><p><strong>Methods: </strong>In developing the 2025 AMIA YIR presentation, we found that 240 articles submitted by AMIA experts could be organized into multiple themes to allow summarization and synthesis for efficient presentation of 140 of them. Many themes could themselves be organized around the concept of the LHS, which further streamlined the presentation. We were struck by how frequently biomedical informatics literature addressed health equity in relation to LHSs. We therefore applied our own expertise to distill relevant articles from the larger set to explore this recurrent issue.</p><p><strong>Results: </strong>Here, we present 56 papers selected from 9 of the 19 themes that illustrate how biomedical informatics can support equity at each stage of the LHS. Mature LHS implementations provided large-scale examples of how learning cycles can be designed for equity. Real-World Evidence and Inclusive Data themes showed that equitable learning depends on whether data are complete enough to represent the populations the system intends to serve. Natural language processing and artificial intelligence (AI) modeling showed how data are transformed into knowledge and translated into clinical action through decision support. Trust and justice in data use, health equity as infrastructure, and equitable access emerged as interdependent conditions that influence whether the learning cycle operates equitably and effectively.</p><p><strong>Conclusion: </strong>Equity emerged as a condition influencing every stage of the LHS cycle. The value of informatics interventions cannot be judged by technical performance alone, but by whether they build learning cycles that are inclusive, accountable, and equitable for the populations they serve.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revising the TIGER 2018 Health Informatics Competency Framework: Updated Core Competency Areas for Nurses in a Digital Health Era.","authors":"Ursula H Hübner, Nicole Egbert, Kaija Saranto, Polun Chang, Ann Kristin Rotegård, Inge Cort Madsen, Zulma González, Elske Ammenwerth, Jen Bichel-Findlay, Marion J Ball","doi":"10.1055/a-2927-7621","DOIUrl":"10.1055/a-2927-7621","url":null,"abstract":"<p><strong>Background: </strong>In 2018, the \"TIGER International Recommendation Framework of Core Competencies in Health Informatics for Nurses\" was published and used worldwide as a global guidance for educators and students to navigate educational requirements in an increasing digital working environment.</p><p><strong>Objective: </strong>Recent developments in digital health point to the need to revise these recommendations.</p><p><strong>Methods: </strong>This update followed the three-step logic of the 2018 framework development process, comprising (1) a global survey capturing the relevance of 43 core competency areas based on the ratings of 117 experts from all continents, (2) four regional expert workshops, and (3) 21 educational case studies. To ensure comparability with the 2018 framework, the same role-based structure was retained, covering Clinical Nursing, Nursing Management, Quality Management in Nursing, Coordination of Interprofessional Care, and Information Technology IT-Management in Nursing.</p><p><strong>Results: </strong>These recommendations underpin the high relevance level of health informatics for all nursing roles, characterizing each role by an informatics pattern: in Clinical Nursing patient issues, personal skills of working properly in a digital environment and a thorough understanding of the legal and ethical background of one's own work dominate the pattern. In Nursing Management, a similar composition prevails extended by management knowledge. The skill mix for Quality Management emerged as a blend of Clinical Nursing and Nursing Management, whereas Coordination of Interprofessional Care strongly emphasized coordination and teamwork capacities. IT Management in Nursing required basic and specialized computer skills incorporating problem solving, communication, and teamwork. These recommendations strongly suggest artificial intelligence (AI) topics should be addressed in all domains and for all roles of nurses.</p><p><strong>Conclusion: </strong>This updated recommendation framework provides guidance for focusing on areas that gained further priority such as patient safety, electronic health records, making use of its data and AI while retaining a core of invariant components of health informatics education for nurses.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"13-27"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-Driven Norepinephrine Control Using Adaptive Cardiac Digital Twin for Critical Care Patients.","authors":"Vidya S Nair, G D Heshan Niranga, Thushara Madathil, Rahul Krishnan Pathinarupothi","doi":"10.1055/a-2927-7553","DOIUrl":"10.1055/a-2927-7553","url":null,"abstract":"<p><strong>Background: </strong>Infusion pumps are used in critical care settings to deliver medicines such as norepinephrine (NE), insulin, and certain nutrients. Currently, clinicians preset the infusion rate in the device, usually for 1 hour, resulting in a constant drug delivery. However, a patient's response to a preset infusion can vary over time depending on their vital changes. In the case of NE, dosage errors are reported due to delayed infusion and interpatient variability in hemodynamics. To automate the infusion process, fuzzy logic, and proportional integral derivative controllers have been used, but they have fixed algorithms and lack clinical validation.</p><p><strong>Objectives: </strong>This study proposes a data-driven artificial intelligence (AI)-digital twin adaptive framework for NE infusion to reduce the risks of hypotension and hypertension.</p><p><strong>Methods: </strong>For personalized infusion, data were collected from patients supported with NE. A random forest decision model (RFDM) was used to predict the NE rates based on varying hemodynamics. The model performance at each time step is evaluated using a long short-term memory network that works as an adaptive digital twin cardiac model (DTCM). Virtual automation of the infusion process is performed using outputs of AI models.</p><p><strong>Results: </strong>The RFDM achieved strong predictive accuracy, with a mean absolute percentage error (MAPE) of 5.3% and an R² score of 0.97. MAPE values of DTCM for systolic, diastolic, and mean arterial blood pressure (BP) were 5.56, 5.79, and 5.36%, respectively. Compared with real-world infusion, the virtual automation reduced drug consumption by 13.82%.</p><p><strong>Conclusion: </strong>The adaptive DTCM resembles real-world hemodynamics and has improved performance across various infusion rates. RFDM combined with DTCM provides an alternative method for NE infusion. The simulated evaluation shows that the proposed framework maintains BP, automates the infusion process, enabling patient-specific personalized dosing.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"36-45"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deterministic Framework for Transforming Multi-Informant Observational Data into Structured Descriptors: A Feasibility Study.","authors":"Hiroshi Katsumori","doi":"10.1055/a-2916-2906","DOIUrl":"10.1055/a-2916-2906","url":null,"abstract":"<p><strong>Background: </strong>Multi-informant observational data obtained from parents and educators provide rich but context-dependent information. However, these data are rarely transformed into structured representations that can be consistently shared and interpreted across contexts without relying on diagnostic classification.</p><p><strong>Objective: </strong>This study aimed to propose and implement a deterministic framework for transforming multi-informant observational data into structured, interpretable descriptor sets, and to examine its feasibility under controlled conditions. The framework targets an intermediate representation layer for structuring observational data prior to interpretation or decision-making.</p><p><strong>Methods: </strong>Parent- and educator-reported questionnaire data were used as standardized inputs to a predefined deterministic framework. Child-centered signals were derived from parent reports, while educator reports were incorporated as educator-derived contextual signals. The framework applies a transparent rule-based mapping process to generate context-preserving sets of predefined structured descriptors without reliance on machine learning or statistical modeling. Structural behavior was examined using controlled simulated input conditions.</p><p><strong>Results: </strong>The framework consistently generated a limited and prioritized set of descriptors for each input profile. Outputs were structurally stable and reproducible across all predefined input configurations, demonstrating consistent transformation of multi-informant data into structured representations.</p><p><strong>Conclusion: </strong>This study demonstrates the feasibility of a deterministic framework for organizing multi-informant observational data into structured, non-diagnostic descriptors. By introducing a reproducible intermediate organizational layer, the framework provides a transparent approach to cross-context information structuring that may be applicable to other multi-context observational settings involving multi-informant data integration.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"28-35"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preventing Participant Leakage in Reissued Clinical Artificial Intelligence Benchmarks: A Biomedical Informatics Evaluation Protocol and DAIC-WOZ/E-DAIC Audit.","authors":"Minoru Hattori, Naoko Hasunuma","doi":"10.1055/a-2938-1280","DOIUrl":"10.1055/a-2938-1280","url":null,"abstract":"<p><strong>Background: </strong>Biomedical informatics increasingly reuses clinical artificial intelligence (AI) benchmarks, yet successor releases are often treated as independent external corpora. If the same participants reappear under new corpus names, features, or partition files, evaluation can become internal while reported as external.</p><p><strong>Objectives: </strong>The objective of this study is to define successor-release participant leakage as a benchmark-integrity threat, operationalize a preevaluation audit and leak-free protocol, and demonstrate its impact using the Distress Analysis Interview Corpus, Wizard-of-Oz condition (DAIC-WOZ), and the Extended DAIC (E-DAIC) depression-screening benchmarks.</p><p><strong>Methods: </strong>We audited release lineage and participant identity using persistent identifiers, content hashing, Patient Health Questionnaire-8 (PHQ-8) label reconciliation, and fold-transition cross-tabulation. We compared leaky and participant-disjoint E-DAIC-to-DAIC-WOZ protocols on the same heldout participants using paired bootstrap inference and derived conservative leak-free reference baselines across standard pipelines.</p><p><strong>Results: </strong>All 189 DAIC-WOZ participants reappeared in E-DAIC with byte-identical recordings and identical PHQ-8 totals. Across official partitions, 104 of 189 changed fold. Training on E-DAIC and evaluating on the DAIC-WOZ test set placed 47/47 test participants in the model-development pool; the reverse direction was clean. The leaky acoustic protocol yielded area under the receiver operating characteristic curve (AUROC) 0.797 versus 0.569 after deduplication, with paired Delta AUROC + 0.227 (95% confidence interval + 0.106 to + 0.372; two-sided <i>p</i> < 0.001). At matched training size, Delta AUROC was + 0.249. A generic visual classifier reached AUROC 0.887 on a blended external set but 0.562 on the unseen AI-only portion. Leak-free reference baselines were approximately AUROC of 0.60.</p><p><strong>Conclusion: </strong>Successor-release participant leakage is a preventable evaluation failure in clinical AI benchmark reuse. Before pooling, external validation, or model comparison, biomedical informatics studies should document release lineage, verify participant identity at the signal or record level, reconcile labels, deduplicate across releases, and enforce participant-disjoint evaluation.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"46-53"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-Preserving Linkage of Distributed Biological, Clinical, and Imaging Data Supporting Artificial Intelligence in Pediatric Oncology.","authors":"Dieter Hayn, Martin Baumgartner, Karl Kreiner, Emanuel Sandner, Bernhard Jammerbund, Martin Schalling, Ulrike Poetschger, Vanessa Duester, Blanca Martinez de Las Heras, Adela Cañete Nieto, Ana Jiménez-Pastor, Luis Martí-Bonmatí, Ruth Ladenstein, Günter Schreier","doi":"10.1055/a-2942-8547","DOIUrl":"10.1055/a-2942-8547","url":null,"abstract":"<p><strong>Background: </strong>Cancer remains the leading cause of disease-related mortality in children over the age of one in Europe, with over 35,000 new pediatric cases and more than 6,000 deaths annually. Due to the rarity of pediatric cancers, clinical trial protocols often substitute for formal treatment guidelines, resulting in many children being enrolled in multiple trials, with biological samples and genomic data stored in various biobanks. Data collection in pediatric oncology is challenging, with sparse data acquired over extended periods, underscoring the need for optimal utilization of all available information through linked, privacy-preserving datasets.</p><p><strong>Methods: </strong>Here, we report the development of a distributed, privacy-preserving data infrastructure for the PRIMAGE project, a European initiative aimed at supporting artificial intelligence (AI)-driven image analysis for pediatric cancer prognostics. The infrastructure leverages the European Patient Identity (EUPID) Services for Privacy-Preserving Record Linkage, enabling pseudonymized data integration across clinical, biological, and imaging sources. The system incorporates EUPID's hashing and phonetic matching protocols to pseudonymize patient identifiers and link distributed datasets, facilitating secondary data use in compliance with the General Data Protection Regulation.</p><p><strong>Results: </strong>Data from over 700 neuroblastoma patients from European trials and hospitals were linked and uploaded to the PRIMAGE platform, where AI models predict clinical outcomes.</p><p><strong>Conclusion: </strong>This infrastructure successfully facilitated AI model development, advancing pediatric oncology research, and offering a scalable framework for future European health data initiatives, such as the European Health Data Space.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"54-64"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sufficiency of Japan's Future Public Databases in Capturing Data Items for Clinical Trials.","authors":"Kei Tanaka, Mamoru Narukawa","doi":"10.1055/a-2905-6595","DOIUrl":"10.1055/a-2905-6595","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate the extent to which the data items required in ongoing prospective clinical trials are available in real-world data (RWD) infrastructures currently being prepared in Japan, with a focus on the availability of trial-level items.</p><p><strong>Methods: </strong>Data items pertaining to target diseases, inclusion or exclusion criteria, and primary endpoints were extracted from phase III clinical trials initiated in Japan (including multiregional clinical trials) in 2022, and then categorized by data items. For each category, the availability of items from the shared electronic medical records data and National Database data was assessed. Subsequently, the availability of data corresponding to the target disease, inclusion or exclusion criteria, and primary endpoints was assessed at a trial level. Additionally, the clinical laboratory values used in the inclusion/exclusion criteria and primary endpoints were evaluated.</p><p><strong>Results: </strong>Among the 165 target trials, the target disease was identifiable in 158 trials (95.8%); 1,763 of 2,774 inclusion/exclusion criteria items (63.6%), and 47 of 250 primary endpoint items (18.8%) were available. When the predefined criteria for item-level data availability were jointly applied across target diseases, inclusion/exclusion criteria, and primary endpoints, only eight trials (4.8%) met all the conditions.</p><p><strong>Conclusion: </strong>These findings indicate a substantial gap between the trial-level data requirements for recent phase III clinical trials and the data elements currently planned in Japan's emerging RWD infrastructure. As this initiative is in its early stages in Japan and further improvements are planned, future progress is expected.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"65-73"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148622353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Knowledge Graph-Driven Hypergeometric Efficacy Prediction Model for Classical Traditional Chinese Herbal Formulas.","authors":"Li Yuanbai, Liu Fangzhou, Li Yihao, Du Yu, Li Meng, Qin Qin, Yang Yang, Ma Hongming","doi":"10.1055/a-2841-4549","DOIUrl":"10.1055/a-2841-4549","url":null,"abstract":"<p><strong>Background: </strong>The multilevel semantic structure of traditional Chinese medicine (TCM) formulas makes their efficacy difficult to represent computationally.</p><p><strong>Objective: </strong>This study aimed to develop an interpretable, statistically rigorous model for quantitatively predicting the dominant efficacies of classical TCM herbal formulas.</p><p><strong>Methods: </strong>A knowledge graph encompassing five semantic entities-disease, syndrome, symptom, efficacy, and herb-was constructed to standardize and infer multilevel efficacy relationships. Based on this structure, the Hypergeometric Efficacy Prediction Model (HEPM) was established, using hypergeometric enrichment analysis to assess whether specific efficacies are significantly aggregated within a formula. A curated dataset of 174 classical formulas from authoritative TCM sources was used for model validation.</p><p><strong>Results: </strong>HEPM effectively reproduced characteristic efficacy patterns of classical prescriptions, achieving an average F1 score of 0.63 across 174 formulas. The knowledge graph structure resolved semantic inconsistency and incompleteness in traditional efficacy descriptions, enhancing the integrity and computability of efficacy information.</p><p><strong>Conclusion: </strong>HEPM provides a statistically grounded and interpretable framework for modeling efficacy formation in TCM herbal formulas. The method offers a replicable approach for efficacy prediction and supports the development of knowledge-driven intelligent TCM analysis and clinical decision support applications.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"101-109"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Panic Prediction from Digital Phenotyping: Subject-Level Cross-Validation Reveals Limited Between-Person Generalization.","authors":"Minoru Hattori, Naoko Hasunuma","doi":"10.1055/a-2895-5222","DOIUrl":"10.1055/a-2895-5222","url":null,"abstract":"<p><strong>Objectives: </strong>A publicly shared digital phenotyping dataset was recently used to train models predicting day-before-panic (DBP) events, reporting a cross-validated receiver operating characteristic area under the curve (ROC-AUC) of around 0.90. Because the stratified five-fold cross-validation (CV) allowed the same participant's days in both training and test folds, it was unclear whether this reflects between-person generalization or information leakage. We reanalyzed the public dataset to quantify this distinction.</p><p><strong>Methods: </strong>We reanalyzed the public dataset (3,969 person-days; 254 DBP events, 6.4%, across 19 of 43 participants) with the same XGBoost classifier and 71 features, comparing stratified five-fold cross-validation with leave-one-subject-out (LOSO) cross-validation. A retrospective diagnostic decomposition was applied within-subject z-scoring to dynamic features before LOSO.</p><p><strong>Results: </strong>We reproduced the original ROC-AUC at 0.895 ± 0.011. Under LOSO, pooled ROC-AUC dropped to 0.489 (bootstrap 95% CI: 0.325-0.643, including 0.50). The SHapley Additive exPlanations (SHAP) ranking under stratified CV was dominated by static traits (CTQ subscales, BRIAN, SPAQ). The diagnostic decomposition yielded LOSO ROC-AUC 0.516 (precision-recall area under the curve [PR-AUC] 0.114), suggesting a weak residual within-person signal, though this should not be interpreted as prospective performance.</p><p><strong>Conclusion: </strong>Robust between-person generalization was not confirmed under subject-held-out evaluation. The feature ranking was trait-dominated, though the data cannot definitively distinguish identity discrimination from other between-person variance sources. Digital phenotyping studies should report subject-level cross-validation alongside standard metrics, per the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction model Risk of Bias Assessment Tool (PROBAST) guidelines.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"129-133"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Methodological Development of a Digital Clinical Safety Training Program Informed by a National Framework: A New Zealand Case Study.","authors":"Rashmeen Sandhu, Brian Yow, Lara Hopley, Karen Day","doi":"10.1055/a-2895-5157","DOIUrl":"10.1055/a-2895-5157","url":null,"abstract":"<p><strong>Background: </strong>The rapid digitalization of health care services has introduced new safety risks related to patient confidentiality, the reliability of digital tools, and their integration into clinical workflows. While digital health adoption is emphasized in national and international policy, guidance on embedding digital clinical safety (DCS) principles in large organizations is limited. The Health New Zealand - Te Whatu Ora (Health NZ) Data and Digital team has developed the first DCS Framework for New Zealand, but mechanisms to support its organizational adoption have not yet been established. This article describes the methodological design of a DCS training program and identifies preimplementation considerations for integrating DCS principles into organizational practice.</p><p><strong>Methods: </strong>A qualitative exploratory study was conducted within Health NZ using purposive sampling and semistructured interviews with seven stakeholders involved in digital health, clinical informatics, and organizational learning. A structured options assessment was undertaken prior to the interviews to compare potential organizational approaches for implementing DCS capability. Stakeholder feedback on these options, together with interview findings, informed an integrated assessment of program feasibility and design.</p><p><strong>Results: </strong>Findings from the stakeholder interviews indicated that there is limited shared understanding of DCS across clinical and digital teams, strong support for DCS training that is accessible and relevant to everyday practice, and variable compliance with current mandatory training offered at Health NZ. The options assessment identified a comprehensive, modular training program as the most suitable option for embedding DCS principles within Health NZ policies and practices.</p><p><strong>Conclusion: </strong>This study presents a structured methodology informed by the DCS Framework for designing DCS training within a national health system. Findings highlight that training can serve as a key mechanism for organizational change, promoting shared understanding, engagement, and consistent adoption of safe digital practices. This approach provides transferable insights into embedding DCS practices within a complex health system to support safe, resilient, and equitable digital health care delivery.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"90-100"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}