International Journal of Medical Informatics最新文献

{"title":"Impact of AI recommendation correctness on diagnostic accuracy in clinical decision-making","authors":"Florian Kücking ,&nbsp;Dorothee A. Busch ,&nbsp;Mareike Przysucha ,&nbsp;Jan-Oliver Kutza ,&nbsp;Niels Hannemann ,&nbsp;Jens Hüsers ,&nbsp;Birgit Babitsch ,&nbsp;Ursula Hübner","doi":"10.1016/j.ijmedinf.2025.106223","DOIUrl":"10.1016/j.ijmedinf.2025.106223","url":null,"abstract":"<div><h3>Background</h3><div>Clinical decision-making is shaped by healthcare provider-related factors such as experience, qualification and cognitive skills. AI-based Clinical Decision Support Systems (CDSS) promise to enhance diagnostic accuracy but may also introduce risks, particularly through automation bias. The relative impact of correct and incorrect AI recommendations compared to human factors remains poorly understood.</div></div><div><h3>Methods</h3><div>A simulated diagnostic intervention study was conducted with 223 physicians and nurses, who generated 1,338 decisions when assessing wound maceration from images combined with AI recommendations. Participants first completed a baseline assessment of diagnostic performance without AI support, followed by a second phase including AI recommendations (correct or incorrect, based on a CNN). Diagnostic decisions were analysed using a generalised linear mixed model (GLMM) to examine the influence of AI recommendation correctness and healthcare provider-related factors (diagnostic performance, qualification, experience, trust in AI, gender, profession, age, healthcare sector) on decision accuracy.</div></div><div><h3>Results</h3><div>AI recommendations had a strong and bidirectional influence on diagnostic accuracy. Participants were ten times more likely to make correct decisions when receiving a correct AI recommendation (OR = 10.0, p &lt; 0.001), but their accuracy decreased reciprocally when the AI recommendation was incorrect. Among provider-related factors, high baseline diagnostic performance (OR = 2.44, p = 0.019), pertinent formal qualifications (OR = 1.40, p = 0.049), longer work experience (OR = 1.89, p = 0.018), and female gender (OR = 1.55, p = 0.008) were associated with higher diagnostic accuracy. Trust in AI, age, profession, and healthcare sector showed no significant effects in the multivariate model. The overall effect of introducing AI was equivocal compared to baseline, however, there was a differential effect whether the recommendation was correct or wrong.</div></div><div><h3>Conclusions</h3><div>AI recommendations can exert a stronger influence on diagnostic decisions than healthcare provider-related factors. While AI support improved accuracy when correct, it reduced accuracy when incorrect, indicating overreliance on the system and posing a substantial safety risk. These findings highlight the dual nature of AI in clinical decision support and underscore the imperative for systems with consistently high quality in clinical practice. Equally important, clinicians must receive training and support to critically assess AI recommendations when making clinical decisions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106223"},"PeriodicalIF":4.1,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736658","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":"Generative artificial intelligence as a source of advice on resuscitation and first aid for laypeople: A scoping review","authors":"Alexei A. Birkun","doi":"10.1016/j.ijmedinf.2025.106224","DOIUrl":"10.1016/j.ijmedinf.2025.106224","url":null,"abstract":"<div><h3>Introduction</h3><div>The performance of cutting-edge generative artificial intelligence (GenAI) in guiding laypeople on how to give help in health emergencies is attracting growing attention. This study aimed to map and summarise original research evidence on the quality of GenAI-synthesised advice on resuscitation and first aid.</div></div><div><h3>Methods</h3><div>The review encompassed journal publications that reported original quantitative data on the quality (accuracy, correctness, completeness, appropriateness) of GenAI-synthesised advice on how laypeople should perform cardiopulmonary resuscitation or provide first aid. Relevant papers were identified through PubMed, Scopus, and Google Scholar. Studies were included if they were published in English as an article, short report, letter, or note during the period 2017–2025. The review was conducted following the recommendations of the PRISMA extension for Scoping Reviews.</div></div><div><h3>Results</h3><div>Among the 19 eligible studies, 17 evaluated the performance of text-generating GenAI tools, one tested user-to-GenAI voice interaction and another one investigated text-to-video generation capabilities. The studies exhibited substantial heterogeneity in research design, methods, and reporting. Most of them (89.5 %) presented evidence of flaws in the generation of advice on resuscitation or first aid, including a failure to synthesise requested content (reported by 15.8 % of the studies), the creation of incomplete instructions (57.9 %), inaccurate instructions (57.9 %), or superfluous guidance (36.8 %), irrelevant or potentially harmful. The prevalence of misinformation varied from study to study, at times encompassing the whole sample of evaluated GenAI responses. Some authors did not accentuate the issue of misinformation despite the reported data indicating quality defects.</div></div><div><h3>Conclusions</h3><div>Current evidence indicates risks associated with the unsupervised generation of resuscitation and first aid guidance by publicly available GenAI, as the synthesised content often contains misinformation that may mislead users and induce harmful actions. There is a growing need for international collaboration to develop coordinated strategies to limit GenAI-driven misinformation and mitigate potential health risks.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106224"},"PeriodicalIF":4.1,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737403","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":"Exploring hospital staff experiences and requirements for safer and more equitable EHR-integrated medication management system: A qualitative study","authors":"Sreyon Murthi ,&nbsp;Shane Scahill ,&nbsp;Nazanin Ghahreman-Falconer ,&nbsp;Nataly Martini","doi":"10.1016/j.ijmedinf.2025.106222","DOIUrl":"10.1016/j.ijmedinf.2025.106222","url":null,"abstract":"<div><h3>Background</h3><div>Medication-related harm remains a global challenge, causing preventable illness, mortality, and cost. Electronic health records (EHRs) and electronic medication management systems (eMMS) aim to improve safety through standardisation and decision support, yet persistent usability issues, fragmented workflows, and limited consideration of equity continue to compromise outcomes, particularly for Indigenous, older, and disabled populations.</div></div><div><h3>Aim</h3><div>To explore how doctors, nurses, pharmacists, digital health professionals, and organisational leaders experience EHR-integrated medication systems with particular attention to medication safety, workflow, and inequities.</div></div><div><h3>Methods</h3><div>An exploratory descriptive qualitative study was conducted at a large tertiary hospital in New Zealand (January – February 2025). Data were analysed using Braun and Clarke’s reflexive thematic analysis, guided by User-Centred Design, the Sociotechnical Model, the Digital Health Equity Framework, and the Māori wellbeing model Te Whare Tapa Whā. Rigour was supported through reflexive journaling, supervisory review, and COREQ adherence.</div></div><div><h3>Results</h3><div>Thirteen semi-structured interviews and two focus groups were held with 22 participants. Three overlapping themes were identified. <em>System design and organisational factors influencing medication safety</em> showed how complex interfaces, rigid automation, and unreliable infrastructure increased cognitive load and required vigilance. <em>Limited recognition of cultural and social needs in eMMS</em>, revealed that missing fields for language, disability, and family context constrained culturally safe communication and equitable care. <em>Design priorities for safer, more inclusive systems</em> captured participants’ vision of adaptive decision support, integrated data views, and embedded equity features such as interpreter prompts, disability and family engagement fields, and co-design to ensure systems evolve with clinical and cultural practice.</div></div><div><h3>Conclusions</h3><div>Improving medication safety requires more than digital transformation of medication processes. Integrating user-centred design, sociotechnical awareness, and equity principles can create intelligent, context-aware systems that support, rather than replace, clinical judgement, empathy, and human connection in patient care.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106222"},"PeriodicalIF":4.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736657","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":"Implementation of digital health interventions in thyroid cancer care: A scoping review","authors":"Mingxia Zhu,&nbsp;Xinlu Wang,&nbsp;Lei Mei","doi":"10.1016/j.ijmedinf.2025.106221","DOIUrl":"10.1016/j.ijmedinf.2025.106221","url":null,"abstract":"<div><h3>Background</h3><div>Thyroid cancer is one of the fastest-growing malignancies worldwide, with an increasing number of long-term survivors requiring ongoing care. Digital health interventions offer scalable and patient-centered approaches to support this population, yet it remains unclear how these interventions have been designed, delivered, evaluated, and adopted in practice. This scoping review aims to synthesize current evidence on the characteristics, effectiveness, and uptake of digital health interventions in thyroid cancer care.</div></div><div><h3>Methods</h3><div>A systematic search was performed in PubMed, Web of Science, Embase, CINAHL, CENTRAL, PsycINFO, China National Knowledge Infrastructure (CNKI), Wanfang, and the Chinese Biomedical Literature Database (CBM) from inception to April 6, 2025. Reference lists of included articles were also screened manually. Information on study characteristics, participant details, intervention types and settings, outcomes, and reported barriers and facilitators was extracted.</div></div><div><h3>Results</h3><div>A total of 23 studies were included, most of which were conducted since 2018 and focused on postoperative patients. Interventions primarily targeted follow-up care, postoperative rehabilitation, and psychological support, using technologies such as smartphone apps, AI-driven systems, and telehealth. Preliminary evidence suggests improvement in quality of life, psychological well-being, and treatment-related adherence. However, few studies evaluated implementation outcomes or addressed barriers like digital literacy and privacy concerns. Theoretical frameworks and stakeholder co-design were rarely used.</div></div><div><h3>Conclusions</h3><div>Digital health interventions in thyroid cancer care show promise but remain underutilized in clinical practice. Future research should enhance uptake and implementation by expanding target populations, integrating emerging technologies, and adopting more rigorous and grounded approaches. Addressing these gaps may guide continued innovation and support broader integration of digital health interventions in this population.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106221"},"PeriodicalIF":4.1,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736656","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":"Virtual care in residential aged care and primary care settings: a systematic literature review using the SEIPS framework","authors":"Meredith A.B. Makeham ,&nbsp;Tamasha Jayawardena ,&nbsp;Samiha Elkheir ,&nbsp;Ann Carrigan ,&nbsp;Soumya ,&nbsp;Heather Russell ,&nbsp;Mirela Prgomet ,&nbsp;Vaibhav Tyagi ,&nbsp;Amy Von Huben ,&nbsp;Melissa Baysari ,&nbsp;Adeola Bamgboje-Ayodele","doi":"10.1016/j.ijmedinf.2025.106218","DOIUrl":"10.1016/j.ijmedinf.2025.106218","url":null,"abstract":"<div><h3>Background</h3><div>The global need to improve access to primary care in residential aged care homes (RACHs) has driven interest in virtual care models. Despite rapid telehealth adoption, little is known about the sociotechnical factors influencing use in aged care settings, particularly from the perspectives of primary care providers, aged care staff, and residents. This review applied the Systems Engineering Initiative for Patient Safety (SEIPS) framework to synthesise evidence on barriers, enablers, processes, and outcomes of virtual care delivery in RACHs and primary care.</div></div><div><h3>Methods</h3><div>We conducted a systematic review in accordance with PRISMA guidelines and registered with PROSPERO (CRD42024562423). Databases searched included MEDLINE, Embase, CINAHL, and Scopus (January 2016-March 2025). Eligible studies reported qualitative, quantitative, or mixed-methods findings on virtual care involving RACHs and primary care. Data were extracted using a SEIPS-informed template and synthesised deductively across sociotechnical domains.</div></div><div><h3>Findings</h3><div>Thirteen studies met the inclusion criteria. Common barriers included limited digital literacy, sensory and cognitive impairments, poor audio-visual quality, lack of staff training, and workflow disruption. System-level challenges included poor technology interoperability, inadequate digital infrastructure, and insufficient organisational and policy support. Enablers included strong clinician–resident relationships, access to remote monitoring tools, and peer support. Reported outcomes were mixed: improved access, communication, and reduced emergency transfers were noted, alongside concerns about increased workload, reduced relational care, and diagnostic limitations. Studies reporting resident perspectives are lacking.</div></div><div><h3>Interpretation</h3><div>Virtual care has the potential to improve aged care access and outcomes, but effective implementation requires more than technology alone. Hybrid models integrating virtual with in-person care require supportive policies, funding models, and organisational workflows. Addressing interoperability gaps, infrastructure needs, and increasing co-design with residents are essential to create virtual care models that are sustainable, person-centred, and scalable in primary care and aged care contexts.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"207 ","pages":"Article 106218"},"PeriodicalIF":4.1,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145783589","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":"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}