Pub Date : 2026-01-17DOI: 10.1016/j.ijmedinf.2026.106288
Tahmineh Aldaghi , Robert Bem , Jan Muzik
Aim
Individuals with diabetes require continuous self-management. Diabetes monitoring systems generate structured reports that help individuals and healthcare providers interpret data and optimize treatment strategies. To design and validate an Integrated Diabetes Report (IDR) that improves the clarity, usability, and clinical relevance of diabetes data visualizations.
Method
A review of 13 diabetes monitoring systems revealed five main report categories: overlay, logbook, device-specific, daily, and overview reports. While the overview report was the most frequently used, it lacked comprehensive visualization and essential clinical metrics. To address these gaps, a multidisciplinary panel of four experts collaborated to design a more integrated reporting framework.
Results
Across systems, glucose statistics were included in all reports, followed by insulin data (in 12 systems), carbohydrate intake (in 6 systems), hypo-hyperglycemic indices (in 2 systems), sleep indices (in 2 systems), and medication details (in 1 system). Key gaps included minimal data on physical activity, limited documentation of carbohydrates, and the absence of consolidated insulin visualization. The IDR introduces a complications section, an integrated graph combining AGP with basal and bolus insulin, and an advanced insulin profile comparing seven calculated indices.
Conclusion
The IDR improves clinical interpretation, supports treatment decisions, and enhances risk assessment for diabetes management.
{"title":"Enhancing diabetes monitoring systems’ reports: A novel integrated diabetes report (IDR)","authors":"Tahmineh Aldaghi , Robert Bem , Jan Muzik","doi":"10.1016/j.ijmedinf.2026.106288","DOIUrl":"10.1016/j.ijmedinf.2026.106288","url":null,"abstract":"<div><h3>Aim</h3><div>Individuals with diabetes require continuous self-management. Diabetes monitoring systems generate structured reports that help individuals and healthcare providers interpret data and optimize treatment strategies. To design and validate an Integrated Diabetes Report (IDR) that improves the clarity, usability, and clinical relevance of diabetes data visualizations.</div></div><div><h3>Method</h3><div>A review of 13 diabetes monitoring systems revealed five main report categories: overlay, logbook, device-specific, daily, and overview reports. While the overview report was the most frequently used, it lacked comprehensive visualization and essential clinical metrics. To address these gaps, a multidisciplinary panel of four experts collaborated to design a more integrated reporting framework.</div></div><div><h3>Results</h3><div>Across systems, glucose statistics were included in all reports, followed by insulin data (in 12 systems), carbohydrate intake (in 6 systems), hypo-hyperglycemic indices (in 2 systems), sleep indices (in 2 systems), and medication details (in 1 system). Key gaps included minimal data on physical activity, limited documentation of carbohydrates, and the absence of consolidated insulin visualization. The IDR introduces a complications section, an integrated graph combining AGP with basal and bolus insulin, and an advanced insulin profile comparing seven calculated indices.</div></div><div><h3>Conclusion</h3><div>The IDR improves clinical interpretation, supports treatment decisions, and enhances risk assessment for diabetes management.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106288"},"PeriodicalIF":4.1,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013318","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}
Pub Date : 2026-01-17DOI: 10.1016/j.ijmedinf.2026.106292
Jin Ye
This comment relates to Kücking et al.’s (2026) study on the bidirectional effects of artificial intelligence recommendations and healthcare provider related factors on the accuracy of wound impregnation diagnosis. While acknowledging the valuable contributions of this research, including distinguishing between correct/incorrect artificial intelligence outputs, rigorous simulation design, and emphasis on clinical safety, we have raised key questions to enhance the interpretation of results and real-world translation. The main focuses include the moderating role of artificial intelligence system accuracy in automation bias, external effectiveness in real clinical environments, potential mechanisms for gender differences in diagnostic performance, the impact of visual cue design on decision-making, and the potential of explainable artificial intelligence (XAI) in risk mitigation. This review aims to promote further research and facilitate the safe and effective integration of artificial intelligence based clinical decision support systems (CDSS) into clinical practice.
{"title":"Beyond binary diagnosis: Key questions on AI accuracy, real-world applicability, and safety in clinical decision support","authors":"Jin Ye","doi":"10.1016/j.ijmedinf.2026.106292","DOIUrl":"10.1016/j.ijmedinf.2026.106292","url":null,"abstract":"<div><div>This comment relates to Kücking et al.’s (2026) study on the bidirectional effects of artificial intelligence recommendations and healthcare provider related factors on the accuracy of wound impregnation diagnosis. While acknowledging the valuable contributions of this research, including distinguishing between correct/incorrect artificial intelligence outputs, rigorous simulation design, and emphasis on clinical safety, we have raised key questions to enhance the interpretation of results and real-world translation. The main focuses include the moderating role of artificial intelligence system accuracy in automation bias, external effectiveness in real clinical environments, potential mechanisms for gender differences in diagnostic performance, the impact of visual cue design on decision-making, and the potential of explainable artificial intelligence (XAI) in risk mitigation. This review aims to promote further research and facilitate the safe and effective integration of artificial intelligence based clinical decision support systems (CDSS) into clinical practice.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106292"},"PeriodicalIF":4.1,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013323","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}
Pub Date : 2026-01-17DOI: 10.1016/j.ijmedinf.2026.106296
Tristan Ruhwedel , Julian M.M. Rogasch , Paul Martin Dahlke , Seyd Shnayien , Christian Furth , Christoph Wetz , Holger Amthauer , Imke Schatka , Nick Lasse Beetz
Introduction
Worldwide radiologists are facing a high administrative workload. ICD-10 coding is mandatory for reimbursement in many health systems and a frequent source of billing errors. Large language models have shown promise in supporting coding related tasks, but previous studies with earlier ChatGPT versions reported mixed results and evidence specific to radiology reports remains scarce. We therefore aimed to investigate whether ChatGPT-5 can be consulted when assigning ICD-10 codes to radiology reports and whether this leads to a measurable time advantage.
Methods
2,738 fictious radiology reports across multiple modalities were derived from the PARROT database. Additionally, 100 fictitious PET/CT reports were created. Each report was assigned a single, most relevant ICD-10 code using ChatGPT-5. For PARROT, ChatGPT-derived codes were compared with predefined database reference labels. For PET/CT, ChatGPT-derived codes were compared with codes assigned by an independent manual coder. Exact and character-level concordance were assessed. In cases of discordance, a blinded adjudicator selected the most accurate ICD-10 code. Coding efficiency was evaluated for PET/CT reports by measuring coding time per report.
Results
For PARROT, exact-code concordance was 1,590/2,738 (58.1 %). In a random subset of 200 mismatches, blinded adjudication preferred the ChatGPT derived code in 123 and the reference label in 77 cases (p = 0.0015). Coding non-English reports resulted in significantly lower concordance (first character: p = 0.002; second/third characters: p < 0.001; last characters: p = 0.012) and longer coding times than English reports (p = 0.002). Regarding PET/CT reports, median coding time was 8 s with ChatGPT and 135 s without. The median time saved was 127 s per report.
Conclusion
Applied to daily clinical care, higher code correctness might reduce billing errors, while saved time could be reallocated to patient care. Radiologists should collaborate with developers to create versions of LLMs that operate within data-secure environments.
{"title":"Less time Coding, more time Caring: Performance evaluation of ChatGPT-5 for ICD-10 coding of radiology reports","authors":"Tristan Ruhwedel , Julian M.M. Rogasch , Paul Martin Dahlke , Seyd Shnayien , Christian Furth , Christoph Wetz , Holger Amthauer , Imke Schatka , Nick Lasse Beetz","doi":"10.1016/j.ijmedinf.2026.106296","DOIUrl":"10.1016/j.ijmedinf.2026.106296","url":null,"abstract":"<div><h3>Introduction</h3><div>Worldwide radiologists are facing a high administrative workload. ICD-10 coding is mandatory for reimbursement in many health systems and a frequent source of billing errors. Large language models have shown promise in supporting coding related tasks, but previous studies with earlier ChatGPT versions reported mixed results and evidence specific to radiology reports remains scarce. We therefore aimed to investigate whether ChatGPT-5 can be consulted when assigning ICD-10 codes to radiology reports and whether this leads to a measurable time advantage.</div></div><div><h3>Methods</h3><div>2,738 fictious radiology reports across multiple modalities were derived from the PARROT database. Additionally, 100 fictitious PET/CT reports were created. Each report was assigned a single, most relevant ICD-10 code using ChatGPT-5. For PARROT, ChatGPT-derived codes were compared with predefined database reference labels. For PET/CT, ChatGPT-derived codes were compared with codes assigned by an independent manual coder. Exact and character-level concordance were assessed. In cases of discordance, a blinded adjudicator selected the most accurate ICD-10 code. Coding efficiency was evaluated for PET/CT reports by measuring coding time per report.</div></div><div><h3>Results</h3><div>For PARROT, exact-code concordance was 1,590/2,738 (58.1 %). In a random subset of 200 mismatches, blinded adjudication preferred the ChatGPT derived code in 123 and the reference label in 77 cases (p = 0.0015). Coding non-English reports resulted in significantly lower concordance (first character: p = 0.002; second/third characters: p < 0.001; last characters: p = 0.012) and longer coding times than English reports (p = 0.002). Regarding PET/CT reports, median coding time was 8 s with ChatGPT and 135 s without. The median time saved was 127 s per report.</div></div><div><h3>Conclusion</h3><div>Applied to daily clinical care, higher code correctness might reduce billing errors, while saved time could be reallocated to patient care. Radiologists should collaborate with developers to create versions of LLMs that operate within data-secure environments.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106296"},"PeriodicalIF":4.1,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026173","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}
Pub Date : 2026-01-15DOI: 10.1016/j.ijmedinf.2026.106289
Phue Thet Khaing, Masaharu Nakayama
Context
Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit reach. Biometric markers captured using digital technologies may enable earlier and more objective detections.
Purpose
To map biometric modalities used for PTSD detection in digital health, identify underused markers, characterise machine learning (ML)/artificial intelligence (AI) approaches, and assess sex-related analyses.
Methods
Guided by PRISMA-ScR, a protocol on the Open Science Framework was pre-registered and searches in PubMed, IEEE Xplore, and Google Scholar (2015–2025) were conducted. The full search string was: (“post-traumatic stress disorder” OR “PTSD”) AND (“biometric data” OR “biosensor” OR “wearable technology”) AND (“detection” OR “screening” OR “diagnosis” OR “monitoring”) AND (“digital health” OR “mobile health” OR “AI-based” OR “machine learning”). Peer-reviewed human studies using biometric data with digital tools and/or ML/AI for PTSD detection were eligible. Of 3,312 records, 89 underwent full-text review, and 18 studies met the inclusion criteria.
Analysis
Data were categorised by biometric modality, digital platform (wearable devices, mobile applications, ML/AI systems), study population, and performance metrics (area under the curve, sensitivity, specificity). Findings were grouped thematically (physiological, neuroimaging, behavioural, genetic, multimodal) and synthesised narratively to identify trends, gaps, and the application of sex-stratified modelling.
Results
Most studies focused on physiological (e.g., heart rate, sleep) and neuroimaging (functional magnetic resonance imaging, electroencephalography) signals; behavioural and genetic modalities were underexplored. Data were frequently captured via wearables and mobile platforms, with ML commonly applied. Performance reporting was uneven, sex-stratified analyses were rare, and several promising modalities (e.g., eye-tracking, electrodermal activity) remain underused.
Conclusion
Digital biometric approaches can detect PTSD; however, progress has been slowed by heterogeneous study designs, inconsistent reporting, and limited attention to sex differences. Establishing common reporting standards, evaluating multimodal models in real-world settings, and developing algorithms incorporating sex for more equitable screening are warranted.
{"title":"Biometric Data in Post-Traumatic Stress Disorder Detection: A Scoping Review of Digital Health Applications","authors":"Phue Thet Khaing, Masaharu Nakayama","doi":"10.1016/j.ijmedinf.2026.106289","DOIUrl":"10.1016/j.ijmedinf.2026.106289","url":null,"abstract":"<div><h3>Context</h3><div>Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit reach. Biometric markers captured using digital technologies may enable earlier and more objective detections.</div></div><div><h3>Purpose</h3><div>To map biometric modalities used for PTSD detection in digital health, identify underused markers, characterise machine learning (ML)/artificial intelligence (AI) approaches, and assess sex-related analyses.</div></div><div><h3>Methods</h3><div>Guided by PRISMA-ScR, a protocol on the Open Science Framework was pre-registered and searches in PubMed, IEEE Xplore, and Google Scholar (2015–2025) were conducted. The full search string was: (“post-traumatic stress disorder” OR “PTSD”) AND (“biometric data” OR “biosensor” OR “wearable technology”) AND (“detection” OR “screening” OR “diagnosis” OR “monitoring”) AND (“digital health” OR “mobile health” OR “AI-based” OR “machine learning”). Peer-reviewed human studies using biometric data with digital tools and/or ML/AI for PTSD detection were eligible. Of 3,312 records, 89 underwent full-text review, and 18 studies met the inclusion criteria.</div></div><div><h3>Analysis</h3><div>Data were categorised by biometric modality, digital platform (wearable devices, mobile applications, ML/AI systems), study population, and performance metrics (area under the curve, sensitivity, specificity). Findings were grouped thematically (physiological, neuroimaging, behavioural, genetic, multimodal) and synthesised narratively to identify trends, gaps, and the application of sex-stratified modelling.</div></div><div><h3>Results</h3><div>Most studies focused on physiological (e.g., heart rate, sleep) and neuroimaging (functional magnetic resonance imaging, electroencephalography) signals; behavioural and genetic modalities were underexplored. Data were frequently captured via wearables and mobile platforms, with ML commonly applied. Performance reporting was uneven, sex-stratified analyses were rare, and several promising modalities (e.g., eye-tracking, electrodermal activity) remain underused.</div></div><div><h3>Conclusion</h3><div>Digital biometric approaches can detect PTSD; however, progress has been slowed by heterogeneous study designs, inconsistent reporting, and limited attention to sex differences. Establishing common reporting standards, evaluating multimodal models in real-world settings, and developing algorithms incorporating sex for more equitable screening are warranted.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106289"},"PeriodicalIF":4.1,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115094","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}
Pub Date : 2026-01-14DOI: 10.1016/j.ijmedinf.2026.106291
Zhihao Lei
{"title":"“Calibration or contamination?” Reassessing the evaluation of large language models for clinical mortality prediction","authors":"Zhihao Lei","doi":"10.1016/j.ijmedinf.2026.106291","DOIUrl":"10.1016/j.ijmedinf.2026.106291","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106291"},"PeriodicalIF":4.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979480","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}
Pub Date : 2026-01-12DOI: 10.1016/j.ijmedinf.2026.106277
Manuri De Silva , Alice Voskoboynik , Sailavan Ramesh , Janice Campbell , Saravanan Satkumaran , Daryl R. Cheng
Objective
Communicable diseases, especially seasonal respiratory illnesses, contribute significantly to paediatric hospital presentations and admissions. Existing surveillance systems often require retrospective manual data collation and focus on either demographic or clinical data, not both. The Communicable Diseases Platform (CDP) is a dynamic data platform that aggregates both data types for all communicable disease presentations to The Royal Children’s Hospital Melbourne (RCH).
Methods
In the pilot phase, the CDP extracted de-identified aggregated data from hospital electronic medical records for patients with positive respiratory swabs. A dashboard displayed positivity rate and cumulative hospital admissions trends from 2016 to 2025, further filterable by pathogen, age, presentation type and interventions.
Discussion
The CDP improves understanding of clinical profiles, disease burden and seasonal patterns, supporting better outbreak control, patient flow prediction and clinical surveillance. Future developments include immunisation data integration and machine learning algorithm evaluation for real-time vaccine effectiveness estimations and communicable disease predictive modelling.
{"title":"Communicable diseases platform (CDP): Real-Time clinical analytics for infections","authors":"Manuri De Silva , Alice Voskoboynik , Sailavan Ramesh , Janice Campbell , Saravanan Satkumaran , Daryl R. Cheng","doi":"10.1016/j.ijmedinf.2026.106277","DOIUrl":"10.1016/j.ijmedinf.2026.106277","url":null,"abstract":"<div><h3>Objective</h3><div>Communicable diseases, especially seasonal respiratory illnesses, contribute significantly to paediatric hospital presentations and admissions. Existing surveillance systems often require retrospective manual data collation and focus on either demographic or clinical data, not both. The Communicable Diseases Platform (CDP) is a dynamic data platform that aggregates both data types for all communicable disease presentations to The Royal Children’s Hospital Melbourne (RCH).</div></div><div><h3>Methods</h3><div>In the pilot phase, the CDP extracted de-identified aggregated data from hospital electronic medical records for patients with positive respiratory swabs. A dashboard displayed positivity rate and cumulative hospital admissions trends from 2016 to 2025, further filterable by pathogen, age, presentation type and interventions.</div></div><div><h3>Discussion</h3><div>The CDP improves understanding of clinical profiles, disease burden and seasonal patterns, supporting better outbreak control, patient flow prediction and clinical surveillance. Future developments include immunisation data integration and machine learning algorithm evaluation for real-time vaccine effectiveness estimations and communicable disease predictive modelling.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106277"},"PeriodicalIF":4.1,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979478","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}
Pub Date : 2026-01-11DOI: 10.1016/j.ijmedinf.2026.106275
Wenyong Wang , Mahnaz Samadbeik , Gaurav Puri , Donald S.A. McLeod , Elton Lobo , Tuan Duong , Titus Kirwa , Clair Sullivan
Background
Electronic Medical Records (EMRs) aim to improve efficiency, safety, and quality of care. However, the impact of EMR implementation, particularly in outpatient diabetes care, remains underexplored. This study explored clinicians’ perspectives on EMR use in diabetes outpatient care.
Methods
This qualitative study, conducted in line with COREQ guidelines, involved four focus groups with 22 clinicians (doctors, nurses, and allied health) at a metropolitan diabetes service in Queensland, Australia. Data were analysed using deductive content analysis, guided by the Quintuple Aim and Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology frameworks.
Results
Clinicians reported mixed outcomes across the Quintuple Aim domains, shaped by technology adoption constructs. Facilitators such as improved efficiency, access to patient information, and prescribing safety reflected perceived usefulness and positive attitudes, contributing to favourable outcomes across multiple Quintuple Aim. Barriers such as navigation complexity, technical issues, alert fatigue, and overwhelming training led to negative outcomes in EMR use. Tensions around documentation practices and patient expectations of system use, resulted in mixed outcomes. Overall, clinicians viewed EMRs as essential, but sustained adoption required improved usability, tailored training, and better system integration.
Conclusion
This study concludes that while the EMRs improved safety, efficiency, and access to information, their design and implementation also introduced burdens that negatively affected clinician experience. EMRs significantly shape the healthcare workforce, influencing workflow, wellbeing, and professional engagement. In outpatient diabetes care, specific workflow challenges such as glycaemic data integration highlight that existing EMR designs may not fully support the complexity of chronic disease management. To maximise benefits, EMR initiatives should be approached as quality improvement activities, with role-specific training, reliable infrastructure, and clinician involvement in system optimisation. Future research should address usability challenges, enhance integration, and ensure that both clinician and patient perspectives guide digital health transformation.
{"title":"Clinicians’ perspectives on electronic medical records use in diabetes outpatient Care: A qualitative study","authors":"Wenyong Wang , Mahnaz Samadbeik , Gaurav Puri , Donald S.A. McLeod , Elton Lobo , Tuan Duong , Titus Kirwa , Clair Sullivan","doi":"10.1016/j.ijmedinf.2026.106275","DOIUrl":"10.1016/j.ijmedinf.2026.106275","url":null,"abstract":"<div><h3>Background</h3><div>Electronic Medical Records (EMRs) aim to improve efficiency, safety, and quality of care. However, the impact of EMR implementation, particularly in outpatient diabetes care, remains underexplored. This study explored clinicians’ perspectives on EMR use in diabetes outpatient care.</div></div><div><h3>Methods</h3><div>This qualitative study, conducted in line with COREQ guidelines, involved four focus groups with 22 clinicians (doctors, nurses, and allied health) at a metropolitan diabetes service in Queensland, Australia. Data were analysed using deductive content analysis, guided by the Quintuple Aim and Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology frameworks.</div></div><div><h3>Results</h3><div>Clinicians reported mixed outcomes across the Quintuple Aim domains, shaped by technology adoption constructs. Facilitators such as improved efficiency, access to patient information, and prescribing safety reflected perceived usefulness and positive attitudes, contributing to favourable outcomes across multiple Quintuple Aim. Barriers such as navigation complexity, technical issues, alert fatigue, and overwhelming training led to negative outcomes in EMR use. Tensions around documentation practices and patient expectations of system use, resulted in mixed outcomes<strong>.</strong> Overall, clinicians viewed EMRs as essential, but sustained adoption required improved usability, tailored training, and better system integration.</div></div><div><h3>Conclusion</h3><div>This study concludes that while the EMRs improved safety, efficiency, and access to information, their design and implementation also introduced burdens that negatively affected clinician experience. EMRs significantly shape the healthcare workforce, influencing workflow, wellbeing, and professional engagement. In outpatient diabetes care, specific workflow challenges such as glycaemic data integration highlight that existing EMR designs may not fully support the complexity of chronic disease management. To maximise benefits, EMR initiatives should be approached as quality improvement activities, with role-specific training, reliable infrastructure, and clinician involvement in system optimisation. Future research should address usability challenges, enhance integration, and ensure that both clinician and patient perspectives guide digital health transformation.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106275"},"PeriodicalIF":4.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960768","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}
Pub Date : 2026-01-11DOI: 10.1016/j.ijmedinf.2026.106271
Zhihong Han , Baixin Li , Jie Liu
Background
Aortic dissection (AD) is a critical cardiovascular disorder with substantial risks of short-term mortality. Some researchers have endeavored to utilize machine learning (ML) approaches to develop predictive models for the risk of mortality in AD. However, systematic evidence about the accuracy of these models remains scarce, which poses challenges to the development and enhancement of risk assessment tools. Therefore, this study seeks to systematically review the reliability of ML in forecasting the risk of mortality in AD.
Methods
A search was implemented through PubMed, Cochrane, Embase, and Web of Science up to September 11, 2025. The prediction model risk of bias (RoB) assessment tool (PROBAST) was leveraged to estimate the RoB of the included studies. Subgroup analyses were implemented based upon types of AD and time of death.
Results
In total, 35 studies were included, covering 19,838 patients with AD. The results showed that, within the training datasets, ML models demonstrated a sensitivity (SEN) of 0.75 (95% CI: 0.72–0.78) and specificity (SPE) of 0.77 (95% CI: 0.74–0.80) for predicting mortality in AD. Within the validation set, which mainly focused on TAAD, the SEN was 0.79 (95% CI: 0.74–0.84) and the SPE was 0.78 (95% CI: 0.68–0.85). For in-hospital mortality, the SEN was 0.78 (95% CI: 0.72–0.83) and the SPE was 0.77 (95% CI: 0.65–0.86); for out-of-hospital mortality, the SEN and SPE were 0.81–0.84 and 0.74–0.86.
Conclusion
ML models demonstrate remarkable accuracy in forecasting the risk of mortality in AD and show superior performance relative to existing scoring systems to some extent. Future research should incorporate more multi-center, multi-ethnic, and geographically varied cases to develop a more broadly applicable risk prediction tool and offer insights for the tailored prevention strategies.
{"title":"Predictive value of machine learning for mortality risk in aortic dissection: a systematic review and meta-analysis","authors":"Zhihong Han , Baixin Li , Jie Liu","doi":"10.1016/j.ijmedinf.2026.106271","DOIUrl":"10.1016/j.ijmedinf.2026.106271","url":null,"abstract":"<div><h3>Background</h3><div>Aortic dissection (AD) is a critical cardiovascular disorder with substantial risks of short-term mortality. Some researchers have endeavored to utilize machine learning (ML) approaches to develop predictive models for the risk of mortality in AD. However, systematic evidence about the accuracy of these models remains scarce, which poses challenges to the development and enhancement of risk assessment tools. Therefore, this study seeks to systematically review the reliability of ML in forecasting the risk of mortality in AD.</div></div><div><h3>Methods</h3><div>A search was implemented through PubMed, Cochrane, Embase, and Web of Science up to September 11, 2025. The prediction model risk of bias (RoB) assessment tool (PROBAST) was leveraged to estimate the RoB of the included studies. Subgroup analyses were implemented based upon types of AD and time of death.</div></div><div><h3>Results</h3><div>In total, 35 studies were included, covering 19,838 patients with AD. The results showed that, within the training datasets, ML models demonstrated a sensitivity (SEN) of 0.75 (95% CI: 0.72–0.78) and specificity (SPE) of 0.77 (95% CI: 0.74–0.80) for predicting mortality in AD. Within the validation set, which mainly focused on TAAD, the SEN was 0.79 (95% CI: 0.74–0.84) and the SPE was 0.78 (95% CI: 0.68–0.85). For in-hospital mortality, the SEN was 0.78 (95% CI: 0.72–0.83) and the SPE was 0.77 (95% CI: 0.65–0.86); for out-of-hospital mortality, the SEN and SPE were 0.81–0.84 and 0.74–0.86.</div></div><div><h3>Conclusion</h3><div>ML models demonstrate remarkable accuracy in forecasting the risk of mortality in AD and show superior performance relative to existing scoring systems to some extent. Future research should incorporate more multi-center, multi-ethnic, and geographically varied cases to develop a more broadly applicable risk prediction tool and offer insights for the tailored prevention strategies.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106271"},"PeriodicalIF":4.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979460","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}
Pub Date : 2026-01-10DOI: 10.1016/j.ijmedinf.2026.106276
Xizhi Wu , Madeline S. Kreider , Philip E. Empey , Chenyu Li , Yanshan Wang
Objective
Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information.
Materials and methods
We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest [RF], Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error analysis prompting). A 5-fold cross validation were conducted to validate each model.
Results
Error analysis prompting achieved optimal precision, recall, and F1 scores for treatment (F1 = 1.000) and toxicities extraction (F1 = 0.965), whereas zero-shot perform moderately (treatment F1 = 0.889, toxicities extraction F1 = 0.854) Rule-based reached F1 = 1.000 for treatment and F1 = 0.904 for toxicities extraction. LR and SVM ranked second and fourth for toxicities extraction (LR F1 = 0.914, SVM F1 = 0.903). Deep learning and RF underperformed, with performance of BERT reached F1 = 0.792 for treatment and F1 = 0.837 for toxicities extraction.,ClinicalBERT reached F1 = 0.797 for treatment and F1 = 0.884 for toxicities extraction). RF reached F1 = 0.745 for treatment and F1 = 0.853 for toxicities extraction.
Discussion
LMM-based error analysis outperformed all others, followed by machine learning methods. Machine learning and deep learning methods were limited by small training data and showed limited generalizability, particularly for rare categories.
Conclusion
LLM-based error analysis most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.
{"title":"Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing","authors":"Xizhi Wu , Madeline S. Kreider , Philip E. Empey , Chenyu Li , Yanshan Wang","doi":"10.1016/j.ijmedinf.2026.106276","DOIUrl":"10.1016/j.ijmedinf.2026.106276","url":null,"abstract":"<div><h3>Objective</h3><div>Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information.</div></div><div><h3>Materials and methods</h3><div>We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest [RF], Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error analysis prompting). A 5-fold cross validation were conducted to validate each model.</div></div><div><h3>Results</h3><div>Error analysis prompting achieved optimal precision, recall, and F1 scores for treatment (F1 = 1.000) and toxicities extraction (F1 = 0.965), whereas zero-shot perform moderately (treatment F1 = 0.889, toxicities extraction F1 = 0.854) Rule-based reached F1 = 1.000 for treatment and F1 = 0.904 for toxicities extraction. LR and SVM ranked second and fourth for toxicities extraction (LR F1 = 0.914, SVM F1 = 0.903). Deep learning and RF underperformed, with performance of BERT reached F1 = 0.792 for treatment and F1 = 0.837 for toxicities extraction.,ClinicalBERT reached F1 = 0.797 for treatment and F1 = 0.884 for toxicities extraction). RF reached F1 = 0.745 for treatment and F1 = 0.853 for toxicities extraction.</div></div><div><h3>Discussion</h3><div>LMM-based error analysis outperformed all others, followed by machine learning methods. Machine learning and deep learning methods were limited by small training data and showed limited generalizability, particularly for rare categories.</div></div><div><h3>Conclusion</h3><div>LLM-based error analysis most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106276"},"PeriodicalIF":4.1,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979481","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}
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