International Journal of Medical Informatics最新文献

{"title":"Quality review and content analysis of liver complications mobile apps in Iran: A statistical and machine learning approach","authors":"Farzaneh Kermani ,&nbsp;Mahdi Mahmoodi ,&nbsp;Mahmood Reza Nasiri ,&nbsp;Azam Orooji","doi":"10.1016/j.ijmedinf.2025.105842","DOIUrl":"10.1016/j.ijmedinf.2025.105842","url":null,"abstract":"<div><h3>Background</h3><div>Liver disease accounts for 4 % of global mortality. The advent of mobile technology has introduced a novel domain in liver disease management. Identifying effective mobile apps with pertinent information on liver diseases is essential. This study seeks to evaluate liver disease-related mobile applications using the Mobile Application Rating Scale (MARS) quality assessment tool.</div></div><div><h3>Method</h3><div>This research employs a cross-sectional descriptive and analytical methodology focusing on liver disease-related mobile applications. We evaluated all Persian and English mobile applications available on the <em>Google Play</em>, <em>Cafe Bazaar</em>, and <em>Myket</em> Stores dedicated to liver diseases until 2023. After eliminating duplicates, evaluators extracted technical specifications and features of apps. The MARS was employed to assess the quality of the mobile applications. Both statistical and machine learning methods were employed for analysis.</div></div><div><h3>Results</h3><div>A total of 2,044 mobile applications were identified, with 49 selected for final analysis. The apps focused on liver-related issues included general liver disease (n = 20, 40.82 %), hepatitis (n = 9, 18.37 %), and fatty liver disease (n = 8, 16.33 %). In terms of functionality, the majority of apps (n = 20, 40.82 %) served as calculators, with 15 specifically for calculation. Among these, three integrated educational elements, and two also supported diet and fitness alongside calculator functions. Additionally, 20 apps aimed to provide educational and informative content. The average quality score was 3.17 (SD = 0.20), with scores ranging from 2.33 to 4.45. Generally, the mean score of Engagement, Functionality, Aesthetics and Information were 4.20 (SD = 0.67), 4.00 (SD = 0.67), 4.00 (SD = 0.92), and 4.00 (SD = 0.67), respectively. The highest Subjective quality score was 4.75.</div></div><div><h3>Conclusions</h3><div>Liver disease-related mobile applications serve users in educational, diet and lifestyle, calculation, risk assessment, and management domains, focusing mainly on general liver diseases and hepatitis. However, the results revealed that the apps lack sufficient and reliable information.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105842"},"PeriodicalIF":3.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437664","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":"Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review","authors":"Ruba Sulaiman ,&nbsp;Md.Ahasan Atick Faisal ,&nbsp;Maram Hasan ,&nbsp;Muhammad E.H. Chowdhury ,&nbsp;Faycal Bensaali ,&nbsp;Abdulrahman Alnabti ,&nbsp;Huseyin C. Yalcin","doi":"10.1016/j.ijmedinf.2025.105840","DOIUrl":"10.1016/j.ijmedinf.2025.105840","url":null,"abstract":"<div><h3>Background</h3><div>Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of these complications depends on pre-existing clinical conditions and patient specific complex anatomical features. Accurate prediction of TAVI outcomes will assist in the precise risk assessment for patients undergoing TAVI. Throughout the past decade, different machine learning (ML) approaches have been utilized to predict outcomes of TAVI. This systematic review aims to assess the application of ML in TAVI for the purpose of outcome prediction<strong>.</strong></div></div><div><h3>Methods</h3><div>Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was adapted for searching the PubMed and Scopus databases on ML use in TAVI outcomes prediction. Once the studies that meet the inclusion criteria were identified, data from these studies were retrieved and were further examined. 17 parameters relevant to TAVI outcomes were carefully identified for assessing the quality of the included studies.</div></div><div><h3>Results</h3><div>Following the search of the mentioned databases, 78 studies were initially retrieved, and 17 of these studies were included for further assessment. Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. The most employed ML algorithms were random forest, logistics regression, and gradient boosting. Among the studied parameters, serum creatinine, age, BMI, hemoglobin, and aortic valve mean gradient were identified as key predictors for TAVI outcomes. These predictors were found to be well aligned with established associations in current literature.</div></div><div><h3>Conclusion</h3><div>ML presents a promising opportunity for improving the success and safety of TAVI and enhancing patient-centered care. While currently retrospective studies with low generalizability and heterogeneity form the basis of ML TAVI research, future prospective investigations with highly heterogeneous patient TAVI cohorts will be critically important for firmly establishing the applicability of ML in predicting TAVI outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105840"},"PeriodicalIF":3.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430293","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":"AI-driven triage in emergency departments: A review of benefits, challenges, and future directions","authors":"Adebayo Da’Costa ,&nbsp;Jennifer Teke ,&nbsp;Joseph E. Origbo ,&nbsp;Ayokunle Osonuga ,&nbsp;Eghosasere Egbon ,&nbsp;David B. Olawade","doi":"10.1016/j.ijmedinf.2025.105838","DOIUrl":"10.1016/j.ijmedinf.2025.105838","url":null,"abstract":"<div><h3>Background</h3><div>Emergency Departments (EDs) are critical in providing immediate care, often under pressure from overcrowding, resource constraints, and variability in patient prioritization. Traditional triage systems, while structured, rely on subjective assessments, which can lack consistency during peak hours or mass casualty events. AI-driven triage systems present a promising solution, automating patient prioritization by analyzing real-time data, such as vital signs, medical history, and presenting symptoms. This narrative review examines the key components, benefits, limitations, and future directions of AI-driven triage systems in EDs.</div></div><div><h3>Method</h3><div>This narrative review analyzed peer-reviewed articles published between 2015 and 2024, identified through searches in PubMed, Scopus, IEEE Xplore, and Google Scholar. Findings were synthesized to provide a comprehensive overview of their potential and limitations.</div></div><div><h3>Results</h3><div>The review identifies substantial benefits of AI-driven triage, including improved patient prioritization, reduced wait times, and optimized resource allocation. However, challenges such as data quality issues, algorithmic bias, clinician trust, and ethical concerns are significant barriers to widespread adoption. Future directions emphasize the need for algorithm refinement, integration with wearable technology, clinician education, and ethical framework development to address these challenges and ensure equitable implementation.</div></div><div><h3>Conclusion</h3><div>AI-driven triage systems have the potential to transform ED operations by enhancing efficiency, improving patient outcomes, and supporting healthcare professionals in high-pressure environments. As healthcare demands continue to grow, these systems represent a vital innovation for advancing emergency care and addressing longstanding challenges in triage.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105838"},"PeriodicalIF":3.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FibrAIm – The machine learning approach to identify the early stage of liver fibrosis and steatosis","authors":"Barbara Ginter-Matuszewska ,&nbsp;Agnieszka Adamek ,&nbsp;Maciej Majchrzak ,&nbsp;Blazej Rozplochowski ,&nbsp;Agata Zientarska ,&nbsp;Arleta Kowala-Piaskowska ,&nbsp;Piotr Lukasiak","doi":"10.1016/j.ijmedinf.2025.105837","DOIUrl":"10.1016/j.ijmedinf.2025.105837","url":null,"abstract":"<div><h3>Background</h3><div>Early recognition of steatosis (fatty liver) and fibrosis in liver health is crucial for effectively managing and preventing the possibility of liver dysfunction. Detecting steatosis helps identify individuals at risk of liver-related diseases, such as inflammation (Non-Alcoholic SteatoHepatitis, NASH) and fibrosis. Fibrosis involves the formation of scar tissue in the liver due to chronic inflammation and injury. Early recognition of fibrosis helps categorize patients based on their risk of progression to advanced liver disease. Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) leads to many outcomes, including Metabolic dysfunction-Associated Steatohepatitis (MASH), fibrosis, and cirrhosis. We aim to show that routine clinical tests supported by machine learning offer sufficient information to predict these endpoints.</div></div><div><h3>Methods</h3><div>The research focused on applying various operational research methods such as Linear Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Multi-Layer Perceptron, and Naive Bayes.</div></div><div><h3>Results</h3><div>The proposed method - FibrAIm - allows the identification of patients at risk of complications related to the conditions analyzed based on inconclusive test results. It can also identify the risk of fibrosis in those whose results appear correct.</div></div><div><h3>Conclusions</h3><div>Given the results obtained during the trials, FibrAIm could become a valuable tool for diagnosing patients at risk of liver early steatosis and fibrosis by identifying cases based on standardized screening tests.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105837"},"PeriodicalIF":3.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting and validating the risk of interstitial lung disease in systemic lupus erythematosus","authors":"Aoyang Guo ,&nbsp;Yanran Chen ,&nbsp;Hongyang Liu ,&nbsp;Shujun Gao ,&nbsp;Xinyi Huang ,&nbsp;Dongzhou Liu ,&nbsp;Qianqian Zhao ,&nbsp;Xiaoping Hong","doi":"10.1016/j.ijmedinf.2025.105839","DOIUrl":"10.1016/j.ijmedinf.2025.105839","url":null,"abstract":"<div><h3>Objective</h3><div>Our study aimed to<!--> <!-->construct a web-based calculator to predict high risk patients of interstitial lung disease (ILD) in systemic lupus erythematosus (SLE).</div></div><div><h3>Methods</h3><div>This retrospective study comprised training and test cohorts, including 581 and 86 patients, respectively. Univariate, least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme Gradient Boosting (XGBoost), and logistic regression (LR) analyses were performed. A Venn diagram was used to investigate critical features. Receiver operating characteristic (ROC) analysis and decision curve analysis were used to evaluate the model’s performance. Risk stratification was performed using the best ROC cut-off value. The web-based calculator was established using Streamlit software.</div></div><div><h3>Results</h3><div>Characteristics such as Raynaud’s phenomenon, pulmonary artery systolic pressure, serositis, anti-U1RNP antibodies, anti-Ro52 antibodies, C-reactive protein, age, and disease course were associated with SLE complicated by ILD (SLE-ILD). LR-Venn, RF-Venn, XGBoost-Venn, LASSO-logic, RF, and XGBoost models were constructed. In training cohort, the XGBoost model demonstrated the highest area under the ROC curve (AUC, 0.890; cut-off value, 0.197; sensitivity, 0.793; specificity, 0.836) and provided<!--> <!-->a net<!--> <!-->benefit<!--> <!-->in decision curve analysis (odds ratio [OR] for SLE-ILD [high- vs. low-risk], 19.6). The model was validated in the test cohort (AUC, 0.866; sensitivity, 0.722; specificity, 0.897; OR, 22.7). Furthermore, an XGBoost model-based web calculator was developed.</div></div><div><h3>Conclusion</h3><div>Our web calculator (<span><span>https://st</span><svg><path></path></svg></span><span><span>-xgboost-app-kcv9qm.streamlit.app/</span><svg><path></path></svg></span>) greatly improved risk prediction for SLE-ILD and was implemented effectively.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105839"},"PeriodicalIF":3.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting cancer survival at different stages: Insights from fair and explainable machine learning approaches","authors":"Tejasvi Sanjay Kamble ,&nbsp;Hongtao Wang ,&nbsp;Nicole Myers ,&nbsp;Nickolas Littlefield ,&nbsp;Leah Reid ,&nbsp;Cynthia S. McCarthy ,&nbsp;Young Ji Lee ,&nbsp;Hongfang Liu ,&nbsp;Liron Pantanowitz ,&nbsp;Soheyla Amirian ,&nbsp;Hooman H. Rashidi ,&nbsp;Ahmad P. Tafti","doi":"10.1016/j.ijmedinf.2025.105822","DOIUrl":"10.1016/j.ijmedinf.2025.105822","url":null,"abstract":"<div><h3>Objectives</h3><div>While prior machine learning (ML) models for cancer survivability prediction often treated all cancer stages uniformly, cancer survivability prediction should involve understanding how different stages impact the outcomes. Additionally, the success of ML-powered cancer survival prediction models depends a lot on being fair and easy to understand, especially for different stages of cancer. This study addresses cancer survivability prediction using fair and explainable ML methods.</div></div><div><h3>Methods</h3><div>Focusing on bladder, breast, and prostate cancers using SEER Program data, we developed and validated fair and explainable ML strategies to train separate models for each stage. These computational strategies also advance the fairness and explainability of the ML models.</div></div><div><h3>Results</h3><div>The current work highlights the important role of ML fairness and explainability in stage-specific cancer survivability prediction, capturing and interpreting the associated factors influencing cancer survivability.</div></div><div><h3>Conclusions</h3><div>This contribution advocates for integrating fairness and explainability in these ML models to ensure equitable, fair, interpretable, and transparent predictions, ultimately enhancing patient care and shared decision-making in cancer treatment.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105822"},"PeriodicalIF":3.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430276","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":"Utilization, challenges, and training needs of digital health technologies: Perspectives from healthcare professionals","authors":"Ruby Khan ,&nbsp;Sumbal Khan ,&nbsp;Hailah M. Almohaimeed ,&nbsp;Amany I. Almars ,&nbsp;Bakht Pari","doi":"10.1016/j.ijmedinf.2025.105833","DOIUrl":"10.1016/j.ijmedinf.2025.105833","url":null,"abstract":"<div><div><strong>Introduction:</strong> Digital health technology (DHTs), such as electronic health records (EHRs), mobile health apps, and remote monitoring systems, is revolutionizing contemporary healthcare by improving diagnosis, patient care, and operational efficiency. Notwithstanding these developments, infrastructure, technical assistance, and personnel training remain obstacles to the successful deployment of DHTs.</div><div><strong>Methods:</strong> 500 medical experts participated in a survey to evaluate the use, advantages, and challenges of DHTs. The frequency of DHT use, the perceived advantages, and the challenges—such as technical difficulties and a lack of training—were the main topics of the data gathered.</div><div><strong>Results:</strong> The most popular technology was mobile health apps (44.4%), followed by EHR systems and diagnostic tools (33.3%). Benefits reported included decreased administrative burden (50%) and increased diagnostic accuracy (46.2%). However, there significant obstacles were found, though: 63% of respondents said they had only received limited training, and 51.9% mentioned software bugs and network problems. Despite these obstacles, 63% of those surveyed reported increases in the effectiveness of healthcare delivery.</div><div><strong>Discussion:</strong> Our study finds a gap between the infrastructure needed for DHTs to be implemented successfully and their quick adoption. This study challenges the notion that adopting technology alone increases productivity by highlighting the importance of thorough technical assistance and staff training. These issues need to be resolved if DHTs are to be fully utilized for improved healthcare delivery and operational effectiveness.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105833"},"PeriodicalIF":3.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403335","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 fading structural prominence of explanations in clinical studies","authors":"Daniele Roberto Giacobbe,&nbsp;Matteo Bassetti","doi":"10.1016/j.ijmedinf.2025.105835","DOIUrl":"10.1016/j.ijmedinf.2025.105835","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"197 ","pages":"Article 105835"},"PeriodicalIF":3.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402525","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 the importance of clinical and sociodemographic factors on self-rated health in midlife: A cross-sectional study using machine learning","authors":"Hisrael Passarelli-Araujo","doi":"10.1016/j.ijmedinf.2025.105834","DOIUrl":"10.1016/j.ijmedinf.2025.105834","url":null,"abstract":"<div><h3>Background</h3><div>Self-rated health (SRH) is influenced by various factors, including clinical and sociodemographic characteristics. However, in the context of Brazil, we still lack a clear understanding of the relative importance of these factors and how they differ between men and women in midlife. Given the significance of gender equity in health, it is crucial to explore these differences to meet the specific needs of each group.</div></div><div><h3>Objective</h3><div>This study examines the importance of clinical and sociodemographic factors of SRH among middle-aged Brazilian adults and analyzes how they vary between men and women.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis was conducted using data from the 2019 National Health Survey (PNS) with a representative sample of 31,926 middle-aged adults (40–59 years) living in private households on Brazilian territory. Five machine learning models—Naive Bayes, SVM, Logistic Regression, Random Forests, and XGBoost—were employed to analyze the data.</div></div><div><h3>Results</h3><div>The analysis revealed gender-specific patterns in SRH predictors. For men, education was the most critical factor, followed by diagnoses of physical and mental illnesses. For women, SRH was primarily influenced by chronic disease diagnoses, low education, and health insurance coverage. Alcohol consumption was a stronger predictor of poor SRH for men than women, likely due to cultural norms that promote higher alcohol use among men.</div></div><div><h3>Conclusion</h3><div>This study provides insights into midlife health disparities, emphasizing gender-specific factors influencing SRH. Machine learning demonstrated its value in uncovering nuanced patterns in health data, offering a powerful tool for public health research and policy.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105834"},"PeriodicalIF":3.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388314","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":"Serious Games for constipation management for people with intellectual disabilities: A scoping review and narrative synthesis","authors":"Serena Daniel ,&nbsp;Ruth Bishop ,&nbsp;Ellie Killner ,&nbsp;Alison Whight ,&nbsp;Sarah Lennard ,&nbsp;Stephen Howard ,&nbsp;Richard Laugharne ,&nbsp;Rohit Shankar","doi":"10.1016/j.ijmedinf.2025.105832","DOIUrl":"10.1016/j.ijmedinf.2025.105832","url":null,"abstract":"<div><h3>Introduction</h3><div>People with intellectual disability (PwID) are 2% of the UK population. Constipation and bowel movement (BM) problems (diarrhoea/faecal incontinence etc.) affects over a third of PwID and is a serious cause of morbidity and mortality. Pw ID rely heavily on outside support (family/professional carers/healthcare professionals), many of whom are ignorant to bowel related harms. There is significant stigma to discuss BM particularly constipation.</div><div>Serious Games (SG) are increasingly used for education of health needs. This review examines if game-based technology can assist improving knowledge and reducing stigma of BM problems particularly constipation.</div></div><div><h3>Objective</h3><div>To identify and gain evidence of SGs aimed at improving knowledge of BM management particularly constipation.</div></div><div><h3>Methods</h3><div>A systematic search of publications between 2010 and 2024 was conducted following the PRISMA ScR statement for scoping reviews. The search inclusion/exclusion criteria were designed and overseen by an information specialist. PUBMED, EMBASE and PsychINFO databases were searched. Extracted variables included SG title, co-production and expert involvement, target outcome, evaluation methodology, effectiveness, sustainability and game platform. Results were narratively synthesised.</div></div><div><h3>Results</h3><div>Of 2966 papers retrieved, three were selected for inclusion, none RCTs. All three included SGs aimed to teach BM management or recognition to healthcare workers/ professionals. Two studies evaluated game efficacy. No SGs were assessed after initial trials, none were implemented in clinical practice. Only one game successfully improved participant knowledge. All game creators consulted experts during game design, but none consulted patients. None discussed reducing stigma amongst their audience.</div></div><div><h3>Conclusion</h3><div>Only one of three SGs identified improved BM knowledge in healthcare workers/professionals and was not specific to PwID. There is potential to co-produce with PwID and their carers a SG to support BM problems particularly constipation to reduce stigma, improve outcomes and be a templar for other similarly vulnerable groups like those with dementia.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105832"},"PeriodicalIF":3.7,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}