International Journal of Medical Informatics最新文献

{"title":"Effectiveness of computerized decision support systems linked to electronic health records: An updated systematic review with meta-analysis","authors":"Annalisa Biffi ,&nbsp;Greta Castellini ,&nbsp;Gabriele del Castillo ,&nbsp;Francesca De Nard ,&nbsp;Camilla Vismara ,&nbsp;Federico Cabitza ,&nbsp;Giovanni Corrao ,&nbsp;Silvia Gianola","doi":"10.1016/j.ijmedinf.2025.106220","DOIUrl":"10.1016/j.ijmedinf.2025.106220","url":null,"abstract":"<div><h3>Background</h3><div>Computerized decision support systems (CDSSs) integrated into electronic health records are intended to support continuous use of evidence in clinical decision-making, tailored to individual patients. We aimed to update a previous systematic review on the effectiveness of CDSSs linked with patient-data via electronic health records (EHRs) published in 2014.</div></div><div><h3>Methods</h3><div>We updated the searches on MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases from 2013 up to January 2023. We included randomized controlled trials (RCTs) that evaluated as intervention CDSSs featuring rule- or algorithm-based software integrated with EHRs and evidence-based knowledge compared with usual care, CDSSs without advice, or non-evidence-based CDSSs in any professional healthcare setting. Two independent reviewers extracted relevant data from the included RCTs and assessed the certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluations approach. Meta-analyses with fixed- and random-effects models were performed for two primary outcomes: mortality and morbidity.</div></div><div><h3>Results</h3><div>We included 47 RCTs, incorporating data from 29 new RCTs in this update. Compared with controls, CDSS use may result in little to no reduction in mortality (38 trials, 127,623 patients; fixed-effects model risk ratio [RR] = 0.98; 95 % confidence interval [CI] 0.93 to 1.02; I² = 0 %; moderate certainty). The <em>meta</em>-analysis on morbidity reached nominal statistical significance: CDSS use may have trivial or small benefits with respect to morbidity (34 RCTs; 133,504 patients; fixed-effects model RR = 0.92, 95 % CI 0.90–0.97; random-effects model RR = 0.93, 95 % CI 0.87–0.99; I² = 48 %; high certainty). Our <em>meta</em>-analysis did not highlight substantial effects on mortality while tiny reductions in morbidity are possible. In specific therapeutic areas, such as cardiovascular, a small effect may be present. Nevertheless, CDSSs could improve care processes and clinician behavior, potentially influencing long-term health outcome.</div><div><strong>Registration</strong> CRD42014007177.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106220"},"PeriodicalIF":4.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of digital self-management programmes on stroke survivors: a systematic review of randomised controlled trials","authors":"Weiwei Guo ,&nbsp;Kim Lam Soh ,&nbsp;Kim Geok Soh ,&nbsp;Hasni Idayu Saidi","doi":"10.1016/j.ijmedinf.2025.106210","DOIUrl":"10.1016/j.ijmedinf.2025.106210","url":null,"abstract":"<div><h3>Purpose</h3><div>To synthesise evidence on the effectiveness of digital self-management programmes for stroke survivors’ health outcomes, self-efficacy, and quality of life.</div></div><div><h3>Methods</h3><div>Relevant English-language studies published between 2015 and 2025 were retrieved from PubMed, Embase, the Cochrane Library, Web of Science and Scopus databases. This systematic review was conducted in accordance with the PRISMA guidelines and registered with PROSPERO (CRD420251059348).<!--> <!-->Studies were included if they investigated digital self-management interventions for adult stroke survivors, with outcome measures including secondary prevention, self-efficacy, self-management ability, and quality of life. Study quality was assessed using the Cochrane Risk of Bias tool. Owing to substantial heterogeneity across digital platforms, intervention duration, and outcome measurement tools, a descriptive synthesis approach was adopted.</div></div><div><h3>Results</h3><div>A total of 12 randomised controlled trials (RCTs) involving 3,049 participants were included. Among these, all three studies assessing self-efficacy reported significant improvements in stroke survivors (<em>p</em> &lt; 0.05), two out of three studies demonstrated enhanced self-management ability (<em>p</em> &lt; 0.05), all six studies evaluating quality of life showed significant positive effects (<em>p</em> &lt; 0.05), and all six studies assessing medication adherence reported improvement. However, effects on secondary prevention behaviours such as smoking, alcohol use, physical activity, and blood pressure control were inconsistent. Few studies assessed motor function or long-term outcomes. Intervention content, delivery platforms, and intensity varied widely.</div></div><div><h3>Conclusion</h3><div>Digital self-management via technology shows positive impacts on self-efficacy, medication adherence, and quality of life in stroke survivors. The impact on motor rehabilitation remains unclear, indicating a need for further research. Digital self-management can enhance stroke survivors’ self-efficacy and self-management abilities, promoting active rehabilitation. This intervention effectively improves medication adherence and quality of life but has limited impact on behaviour changes such as smoking cessation and alcohol reduction. It is important to consider integrating digital tools with conventional care while addressing patients’ digital literacy and accessibility challenges. Further development and research are needed to evaluate the effects of digital self-management on stroke functional recovery and activity capacity.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106210"},"PeriodicalIF":4.1,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145696318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Machine learning in predicting cancer complications using longitudinal Data: A systematic review and Meta-Analysis","authors":"Abu Sarwar Zamani ,&nbsp;Abdelwahed Motwakel Eltayeb ,&nbsp;Adel Alluhayb ,&nbsp;Md.Mobin Akhtar ,&nbsp;Rashid Ayub ,&nbsp;Mohammed Abdelmonem Ahmed Abdelrahim ,&nbsp;Sara Saadeldeen Ibrahim Mohamed ,&nbsp;Naved Ahmad","doi":"10.1016/j.ijmedinf.2025.106217","DOIUrl":"10.1016/j.ijmedinf.2025.106217","url":null,"abstract":"<div><div>Cancer prognosis of complications like metastasis, recurrence, and side effects of treatments is important to enhance patient prognosis. There is great potential in the use of ML on lifetime data for improving prediction accuracy in oncology; however, there is no systematic review of the subject. This SRMA is intended to assess the accuracy of ML models based on longitudinal studies for the estimation of cancer-related complications. The articles were identified from PubMed, Google Scholar, and IEEE Xplore databases for the years 2020 to 2024. Seven of the studies reviewed in the paper analyzed ML models that employed longitudinal data for cancer complication prognosis. The risk of bias of included studies was assessed using the Cochrane Risk of Bias tool, and for diagnostic accuracy, the QUADES 2 tool was used. Information on ML techniques, prediction accuracy, and results was obtained. The pooled area under the curve (AUC) for immune-related adverse events prediction was 0.78 (95% CI: 0.73–0.83). For cancer recurrence and mortality prediction, pooled AUCs ranged from 0.70 to 0.75. Machine learning models integrating clinical, genomic, and imaging data demonstrated superior predictive accuracy across various cancer types. Models predicting quality of life deterioration during treatment showed an AUC of 0.82. ML models applying longitudinal data effectively predict cancer complications with improved accuracy when integrating multimodal data. These models offer promising tools for clinical decision-making in oncology.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"208 ","pages":"Article 106217"},"PeriodicalIF":4.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling connected care: Mapping aged care clinical concepts to snomed ct","authors":"Joshua McRae ,&nbsp;Teyl Engstrom ,&nbsp;Jodie Austin ,&nbsp;Murray Hargrave ,&nbsp;Kylynn Loi ,&nbsp;Paulose Varghese ,&nbsp;Clair Sullivan ,&nbsp;Leonard C Gray ,&nbsp;Ronald Dendere","doi":"10.1016/j.ijmedinf.2025.106211","DOIUrl":"10.1016/j.ijmedinf.2025.106211","url":null,"abstract":"<div><h3>Objective</h3><div>Health information systems for acute, primary and aged care have evolved separately. Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the dominant clinical terminology in acute and primary care, while interRAI assessment systems are widely used in aged care. This creates barriers for seamless sharing of health information when older people transition between settings. We answer the research question “Is it feasible to map aged care clinical concepts to a standardised clinical vocabulary?”, to support interoperability.</div></div><div><h3>Materials and Methods</h3><div>Using existing guidelines and open-source tools, we mapped a representative sample of aged care assessments from the interRAI suite of systems to SNOMED CT. The sample consisted of 50 mappable elements covering physical health, cognitive and functional status domains. Each mapping was characterised as ‘equivalent’, ‘broader’, ‘inexact’, or ‘narrower’ and the mappings were validated with clinical experts through discussion to reach consensus.</div></div><div><h3>Results</h3><div>All interRAI elements had a feasible mapping to SNOMED CT. 68% were an equivalent match, 26% were broader, 4% were inexact and 2% were narrower. SNOMED CT qualifier values were used in 46% of maps to replicate the granularity in the interRAI assessments. In most maps (86%), one-to-one mappings were used.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the feasibility of mapping interRAI elements to SNOMED CT, however not all maps were equivalent. Future work should attempt mapping an entire interRAI assessment system. This will advance efforts towards interoperability among aged care and other clinical settings, which will improve consumer experience, safety and continuity of care for older people.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106211"},"PeriodicalIF":4.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TransformerCARE: A novel speech analysis pipeline using transformer-based models and audio augmentation techniques for cognitive impairment detection","authors":"Hossein Azadmaleki ,&nbsp;Ali Zolnour ,&nbsp;Sina Rashidi ,&nbsp;James M. Noble ,&nbsp;Julia Hirschberg ,&nbsp;Elyas Esmaeili ,&nbsp;Tahoura Morovati ,&nbsp;Maryam Zolnoori","doi":"10.1016/j.ijmedinf.2025.106208","DOIUrl":"10.1016/j.ijmedinf.2025.106208","url":null,"abstract":"<div><h3>Objective</h3><div>Early diagnosis of cognitive impairment, including Alzheimer’s and other dementias, is critical for effective treatment and slowing disease progression. However, over 50% of cases remain undiagnosed until advanced stages due to limitations in current methods. Recognizing speech impairments as early markers of cognitive decline, this study evaluated the utility of speech analysis as a technique for early detection. We introduce TransformerCARE, a speech processing pipeline utilizing advanced speech transformer models.</div></div><div><h3>Methods</h3><div>TransformerCARE incorporated a series of key steps, including preprocessing, speech segmentation, transformer fine-tuning, segment aggregation, performance evaluation, and data augmentation. In the fine-tuning step, we evaluated the performance of four state-of-the-art speech transformer models: Wav2vec 2.0, HuBERT, WavLM, and DistilHuBERT. For data augmentation, we adopted multiple techniques, with particular emphasis on frequency masking due to its ability to preserve subtle acoustic cues associated with cognitive impairment. We measured the performance of TransformerCARE on the ADReSSo Challenge dataset from DementiaBank, comprising 237 subjects (122 cognitively impaired and 115 cognitively normal).</div></div><div><h3>Results</h3><div>TransformerCARE demonstrated its highest performance with HuBERT, achieving an AUC of 81.80 (F1-score = 79.31) using an aggregation technique that averaged embeddings of 14-second speech segments. Augmenting the training data with frequency masking improved performance by 5 %, resulting in an AUC of 86.11 (F1-score = 84.63). We also demonstrated that incorporating clinicians’ speech during patient interactions can improve the performance of the pipeline. Our error analysis revealed significant differences between the acoustic patterns of correctly identified negative cases (true negatives) and those incorrectly identified as positive (false positives), as well as between correctly identified positive cases (true positives) and those incorrectly identified as negative (false negatives). This indicates specific deviations in speech characteristics among inaccurately diagnosed subjects.</div></div><div><h3>Conclusion</h3><div>In summary, TransformerCARE demonstrates strong potential for integration into clinical workflows as a screening tool for cognitive impairment, aiding in the timely and appropriate care of affected patients.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106208"},"PeriodicalIF":4.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145702832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time and temporality in machine learning methods to improve cancer clinical decision support: A literature review","authors":"Yiyu Wang ,&nbsp;Anastasia Griva ,&nbsp;Umair Ul Hassan","doi":"10.1016/j.ijmedinf.2025.106219","DOIUrl":"10.1016/j.ijmedinf.2025.106219","url":null,"abstract":"<div><h3>Objective</h3><div>This systematic literature review explores how temporal and time-related dimensions are incorporated into <em>Machine Learning</em> (ML) models used in <em>Clinical Decision Support Systems</em> (CDSS) for cancer. The study examines current applications, identifies research trends and limitations, and proposes future directions for enhancing temporal modeling in ML-based cancer decision support.</div></div><div><h3>Methods</h3><div>Following the PRISMA guidelines, a systematic search was conducted in the Web of Science database using combinations of keywords related to machine learning, clinical decision support, time, and cancer. After applying inclusion and exclusion criteria, 83 peer-reviewed studies published between 2014 and 2023 were analyzed. Each study was examined to determine how temporal aspects were integrated into ML models and categorized using the Ancona time framework to capture conceptions of time, actors relating to time, and mapping activities to time.</div></div><div><h3>Results</h3><div>The findings show increasing research activity since 2015, with rapid growth from 2020 onwards. Most studies focused on survival analysis, time-series modeling, and time-to-event prediction, emphasizing their value in prognosis and treatment planning. However, temporal constructs related to annotation efficiency, biological timing, and longitudinal data remain underexplored. Many approaches still rely on static datasets, lack external validation, and provide limited interpretability. The mapping to the Ancona framework revealed fragmented consideration of time across studies, with limited attention to synchronization, temporal orientation, and patient or clinician experiences of time.</div></div><div><h3>Conclusion</h3><div>The review highlights both progress and persistent limitations in applying temporal dimensions to ML-based cancer CDSS. Future research should strengthen longitudinal modeling, improve temporal data integration, and consider the clinical and human aspects of time to enhance decision support accuracy and relevance.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106219"},"PeriodicalIF":4.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disability pension during emerging adulthood: Insights from the young-HUNT study on psychological distress, chronic pain, and policy reform","authors":"Martin A. Gorosito ,&nbsp;Bjørnar Berg ,&nbsp;Anis Yazidi ,&nbsp;Åsmund Hermansen ,&nbsp;Britt Elin Øiestad ,&nbsp;Margreth Grotle ,&nbsp;Kåre Rønn Richardsen ,&nbsp;Hårek Haugerud","doi":"10.1016/j.ijmedinf.2025.106216","DOIUrl":"10.1016/j.ijmedinf.2025.106216","url":null,"abstract":"<div><div><strong>Introduction:</strong> Disability pension (DP) among young adults has steadily increased in Norway, with chronic pain and psychological distress among the main causes. This study examined the association between co-occurring psychological distress and chronic pain in adolescence and the risk of DP during emerging adulthood, before and after the Norwegian Labour and Welfare Administration (NAV) reform. Machine learning survival models were also developed to predict early DP and identify consistent risk factors across cohorts.</div><div><strong>Methods:</strong> Data from two general population surveys in 1995–1997 (Young-HUNT1) and 2006–2008 (Young-HUNT3) were linked to public registry data for DP, with a mean follow-up of 12 years. These represent populations pre- and post-NAV reform, respectively. Crude and adjusted Cox proportional hazards models tested associations between chronic pain, psychological distress and their co-occurrence with early DP. Machine learning survival models were developed using common predictors across cohorts and validated temporally. SurvSHAP(t) and hazard ratios were used for explainable risk factor analysis.</div><div><strong>Results:</strong> Co-occurring psychological distress and chronic pain were associated with increased early DP risk in Young-HUNT3 (HR = 2.25, [95 % CI 1.62 to 3.14]) but not in Young-HUNT1. Tree-based machine learning models showed strong predictive performance and generalizability over time, with ExtraSurvivalTrees achieving a validation c-index of 0.75 [95 % CI 0.74–0.76]. Consistent risk factors included low social integration, limited physical activity, and low parental education in both cohorts, while additional factors post-reform included low self-esteem, poor self-rated health, and negative school experiences.</div><div><strong>Conclusion:</strong> Adolescents with co-occurring psychological distress and chronic pain face an elevated risk of early DP. Early identification and support are crucial, as risk factors extend beyond medical conditions. Findings also suggest that NAV policy changes may have influenced DP receipt rates among young adults.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106216"},"PeriodicalIF":4.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical capacities at the heart of learning health systems: Conceptual framework based on a developmental literature review","authors":"Yan Bertrand ,&nbsp;Stéphanie Lachance ,&nbsp;Aude Motulsky","doi":"10.1016/j.ijmedinf.2025.106185","DOIUrl":"10.1016/j.ijmedinf.2025.106185","url":null,"abstract":"<div><h3>Introduction</h3><div>Practical models for learning health systems implementation often lack a clear understanding of the organizational capacities required to sustain continuous data-driven improvement cycles. Among these, analytical capacities are widely recognized but insufficiently conceptualized. This study aims to develop a comprehensive framework of analytical capacities in healthcare organizations.</div></div><div><h3>Methodology</h3><div>A developmental literature review was conducted to identify empirical and conceptual articles related to data management, analytics, and use in healthcare organizations. 697 articles were screened and twelve studies met the inclusion criteria. Data extraction was performed independently by two researchers using a structured grid that included a framework for the initial conceptual classification of relevant data. A thematic analysis was then conducted on the classified data to identify and group underlying processes into distinct analytical capacities. The resulting descriptive model was refined through expert consultation and used to build the conceptual framework.</div></div><div><h3>Results</h3><div>Thirteen analytical capacities were identified, and then grouped into five categories according to their nature: data creation, data circulation, data preparation, data analysis, and data appropriation. A conceptual framework was developed to illustrate the relationships between these capacities and their role in a cyclical process of data-driven improvement.</div></div><div><h3>Discussion and conclusion</h3><div>The proposed framework characterizes the different analytical capacities and illustrates their interdependence, offering a practical tool for assessing and planning analytical development in healthcare organizations. It highlights the central role of data mobilization and supports the operationalization of learning health systems by making explicit the capacities that underpin organizational learning. It also lays the groundwork for developing maturity models specific to each analytical capacity, as well as for empirically validating the framework in real-world settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106185"},"PeriodicalIF":4.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethics in Danish healthcare AI policy: A document analysis.","authors":"Victor Vadmand Jensen, Marianne Johansson Jørgensen, Rikke Hagensby Jensen, Jeppe Lange, Jan Wolff, Mette Terp Høybye","doi":"10.1016/j.ijmedinf.2025.106065","DOIUrl":"10.1016/j.ijmedinf.2025.106065","url":null,"abstract":"<p><strong>Introduction: </strong>Nations are increasingly turning towards artificial intelligence (AI) systems to support healthcare settings. While nations must then contend with ethical considerations surrounding healthcare AI, they do so in a variety of ways, emphasizing different ethical considerations in different ways. However, there is still limited knowledge on how Scandinavian healthcare AI policy emphasizes ethics. In this paper, we investigate ethics in Danish healthcare AI policy to highlight underlying policy preferences.</p><p><strong>Methods: </strong>We present a document analysis of Danish policy documents relating to AI. We view policy documents' contents as expectations that signal and frame what is perceived as a desirable future with healthcare AI. From 210 policy documents, we extracted data of text snippets related to categories of ethical principles and pipeline stages, as well as articulated reasons for considering ethics. We analyzed the proportions of ethical principles and pipeline stages quantitatively and reasons for considering ethics inductively.</p><p><strong>Results: </strong>The most frequently cited ethical principle was prevention of harm (n = 177), while the most commonly referenced pipeline stage was implementation, evaluation, and oversight (n = 189). Both ethical principles and pipeline stages significantly deviated from equal proportions (p<0.001). Additionally, five primary reasons for addressing ethics emerged in the documents: fit of AI with existing healthcare structures, the potential consequences of AI, its marketability, associated uncertainties, and the perceived inevitability of its adoption. These findings indicate that Danish healthcare AI policy predominantly frames ethical considerations based on the potential consequences of AI deployment.</p><p><strong>Conclusions: </strong>Our study suggests the need for steering Danish, and more broadly Scandinavian, healthcare AI policy toward other views of ethics that integrate non-potentiality.</p>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"204 ","pages":"106065"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indication-based prescribing and prescribing with indications, effects on documentation, medicines use, and clinical outcomes: a systematic review","authors":"Lorna Pairman ,&nbsp;Paul Chin ,&nbsp;Matthew Doogue","doi":"10.1016/j.ijmedinf.2025.106209","DOIUrl":"10.1016/j.ijmedinf.2025.106209","url":null,"abstract":"<div><h3>Aim</h3><div>To describe the effect of indication-based prescribing and prescribing with indications on electronic medication record documentation, appropriate medicines use, and clinical outcomes.</div></div><div><h3>Methods</h3><div>Databases Medline, Embase, CINAHL, and Scopus were systematically searched using two concepts: ‘indication’ and ‘electronic prescribing’. Inclusion criteria included interventional studies introducing indication-based prescribing, or prescribing with indications, using an electronic medication management system. A narrative synthesis was conducted for the following <em>a priori</em> outcomes: indication provision, indication accuracy, prescription appropriateness, appropriate dose or duration of medicine use, deprescribing, total medicine use, and patient outcomes.</div></div><div><h3>Results</h3><div>From 1,908 abstracts, 24 studies met inclusion criteria. Of the 24 included studies only one study examined prescriptions for all medicines, 16 were focused on prescribing antimicrobial medicines, and four were restricted to blood products. There was substantial heterogeneity in the interventions implemented and the ordering systems used. An improvement was found in indication documentation in three studies, appropriateness of the prescription for the documented indication in four studies, rate of deprescribing in two studies, and a reduction in overuse of antimicrobial medicines and blood products in ten studies. Accuracy of indications was inconsistently defined and measured.</div></div><div><h3>Conclusion</h3><div>Interventions in electronic medication management systems are being increasingly used to improve indication documentation and promote appropriate medicines use. The generalisability of studies is limited by heterogeneity in intervention and system design. Work is needed to 1) develop agreed international standards to guide system vendors and consistency in practice, and 2) improve standardisation of medicine use terminology in literature to guide reporting.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106209"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}