BMJ Health & Care Informatics最新文献

{"title":"Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study","authors":"Chih-Wei Sung, Joshua Ho, Cheng-Yi Fan, Ching-Yu Chen, Chi-Hsin Chen, Shao-Yung Lin, Jia-How Chang, Jiun-Wei Chen, Edward Pei-Chuan Huang","doi":"10.1136/bmjhci-2023-100859","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100859","url":null,"abstract":"Background High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach. Methods This 3-year retrospective cohort study assessed adult patients between January 2019 and December 2021 from National Taiwan University Hospital Hsin-Chu Branch with high-risk ED revisit, defined as hospital or intensive care unit admission after ED return within 72 hours. A total of 150 features were preliminarily screened, and 79 were used in the prediction model. Deep learning, random forest, extreme gradient boosting (XGBoost) and stacked ensemble algorithm were used. The stacked ensemble model combined multiple ML models and performed model stacking as a meta-level algorithm. Confusion matrix, accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance. Results Analysis was performed for 6282 eligible adult patients: 5025 (80.0%) in the training set and 1257 (20.0%) in the testing set. High-risk ED revisit occurred for 971 (19.3%) of training set patients vs 252 (20.1%) in the testing set. Leading predictors of high-risk ED revisit were age, systolic blood pressure and heart rate. The stacked ensemble model showed more favourable prediction performance (AUROC 0.82) than the other models: deep learning (0.69), random forest (0.78) and XGBoost (0.79). Also, the stacked ensemble model achieved favourable accuracy and specificity. Conclusion The stacked ensemble algorithm exhibited better prediction performance in which the predictions were generated from different ML algorithms to optimally maximise the final set of results. Patients with older age and abnormal systolic blood pressure and heart rate at the index ED visit were vulnerable to high-risk ED revisit. Further studies should be conducted to externally validate the model. Data are available on reasonable request.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140636847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"‘If you build it, they will come…to the wrong door: evaluating patient and caregiver-initiated ethics consultations via a patient portal’","authors":"Liz Blackler, Amy E Scharf, Konstantina Matsoukas, Michelle Colletti, Louis P Voigt","doi":"10.1136/bmjhci-2023-100988","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100988","url":null,"abstract":"Objectives Memorial Sloan Kettering Cancer Center (MSK) sought to empower patients and caregivers to be more proactive in requesting ethics consultations. Methods Functionality was developed on MSK’s electronic patient portal that allowed patients and/or caregivers to request ethics consultations. The Ethics Consultation Service (ECS) responded to all requests, which were documented and analysed. Results Of the 74 requests made through the portal, only one fell under the purview of the ECS. The others were primarily requests for assistance with coordinating clinical care, hospital resources or frustrations with the hospital or clinical team. Discussion To better empower patients and caregivers to engage Ethics, healthcare organisations and ECSs must first provide them with accessible, understandable and iterative educational resources. Conclusion After 19.5 months, the ‘Request Ethics Consultation’ functionality on the patient portal was suspended. Developing resources on the role of Ethics for our patients and caregivers remains a priority. All data relevant to the study are included in the article or uploaded as supplementary information.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Codesign of health technology interventions to support best-practice perioperative care and surgical waitlist management.","authors":"Sarah Joy Aitken, Sophie James, Amy Lawrence, Anthony Glover, Henry Pleass, Janani Thillianadesan, Sue Monaro, Kerry Hitos, Vasi Naganathan","doi":"10.1136/bmjhci-2023-100928","DOIUrl":"10.1136/bmjhci-2023-100928","url":null,"abstract":"<p><strong>Objectives: </strong>This project aimed to determine where health technology can support best-practice perioperative care for patients waiting for surgery.</p><p><strong>Methods: </strong>An exploratory codesign process used personas and journey mapping in three interprofessional workshops to identify key challenges in perioperative care across four health districts in Sydney, Australia. Through participatory methodology, the research inquiry directly involved perioperative clinicians. In three facilitated workshops, clinician and patient participants codesigned potential digital interventions to support perioperative pathways. Workshop output was coded and thematically analysed, using design principles.</p><p><strong>Results: </strong>Codesign workshops, involving 51 participants, were conducted October to November 2022. Participants designed seven patient personas, with consumer representatives confirming acceptability and diversity. Interprofessional team members and consumers mapped key clinical moments, feelings and barriers for each persona during a hypothetical perioperative journey. Six key themes were identified: 'preventative care', 'personalised care', 'integrated communication', 'shared decision-making', 'care transitions' and 'partnership'. Twenty potential solutions were proposed, with top priorities a digital dashboard and virtual care coordination.</p><p><strong>Discussion: </strong>Our findings emphasise the importance of interprofessional collaboration, patient and family engagement and supporting health technology infrastructure. Through user-based codesign, participants identified potential opportunities where health technology could improve system efficiencies and enhance care quality for patients waiting for surgical procedures. The codesign approach embedded users in the development of locally-driven, contextually oriented policies to address current perioperative service challenges, such as prolonged waiting times and care fragmentation.</p><p><strong>Conclusion: </strong>Health technology innovation provides opportunities to improve perioperative care and integrate clinical information. Future research will prototype priority solutions for further implementation and evaluation.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Invitation to join the Healthcare AI Language Group: HeALgroup.AI Initiative.","authors":"Sebastian Manuel Staubli, Basel Jobeir, Michael Spiro, Dimitri Aristotle Raptis","doi":"10.1136/bmjhci-2023-100884","DOIUrl":"10.1136/bmjhci-2023-100884","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bibliometric analysis of the 3-year trends (2018-2021) in literature on artificial intelligence in ophthalmology and vision sciences.","authors":"Hayley Monson, Jeffrey Demaine, Adrianna Perryman, Tina Felfeli","doi":"10.1136/bmjhci-2023-100780","DOIUrl":"10.1136/bmjhci-2023-100780","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this analysis is to present a current view of the field of ophthalmology and vision research and artificial intelligence (AI) from topical and geographical perspectives. This will clarify the direction of the field in the future and aid clinicians in adapting to new technological developments.</p><p><strong>Methods: </strong>A comprehensive search of four different databases was conducted. Statistical and bibliometric analysis were done to characterise the literature. Softwares used included the R Studio bibliometrix package, and VOSviewer.</p><p><strong>Results: </strong>A total of 3939 articles were included in the final bibliometric analysis. Diabetic retinopathy (391, 6% of the top 100 keywords) was the most frequently occurring indexed keyword by a large margin. The highest impact literature was produced by the least populated countries and in those countries who collaborate internationally. This was confirmed via a hypothesis test where no correlation was found between gross number of published articles and average number of citations (p value=0.866, r=0.038), while graphing ratio of international collaboration against average citations produced a positive correlation (r=0.283). Majority of publications were found to be concentrated in journals specialising in vision and computer science, with this category of journals having the highest number of publications per journal (18.00 publications/journal), though they represented a small proportion of the total journals (<1%).</p><p><strong>Conclusion: </strong>This study provides a unique characterisation of the literature at the intersection of AI and ophthalmology and presents correlations between article impact and geography, in addition to summarising popular research topics.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10910687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review","authors":"Daraje kaba Gurmessa, Worku Jimma","doi":"10.1136/bmjhci-2023-100954","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100954","url":null,"abstract":"Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms. Methods In this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms ‘breast cancer’, ‘explainable’, ‘interpretable’, ‘machine learning’, ‘artificial intelligence’ and ‘XAI’. Rayyan online platform detected duplicates, inclusion and exclusion of papers. Results This study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans’ confidence in using the XAI system—additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer. Conclusion XAI is not conceded to increase users’ and doctors’ trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking. PROSPERO registration number CRD42023458665. Data are available upon reasonable request.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139669953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM","authors":"Christine Mary Hallinan, Roger Ward, Graeme K Hart, Clair Sullivan, Nicole Pratt, Ashley P Ng, Daniel Capurro, Anton Van Der Vegt, Siaw-Teng Liaw, Oliver Daly, Blanca Gallego Luxan, David Bunker, Douglas Boyle","doi":"10.1136/bmjhci-2023-100953","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100953","url":null,"abstract":"Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers. Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site. Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting. Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data. Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings. Data sharing not applicable as no datasets generated.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139922526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of large language models on advocating the management of meningitis: a comparative qualitative stud","authors":"Urs Fisch, Paulina Kliem, Pascale Grzonka, Raoul Sutter","doi":"10.1136/bmjhci-2023-100978","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100978","url":null,"abstract":"Objectives We aimed to examine the adherence of large language models (LLMs) to bacterial meningitis guidelines using a hypothetical medical case, highlighting their utility and limitations in healthcare. Methods A simulated clinical scenario of a patient with bacterial meningitis secondary to mastoiditis was presented in three independent sessions to seven publicly accessible LLMs (Bard, Bing, Claude-2, GTP-3.5, GTP-4, Llama, PaLM). Responses were evaluated for adherence to good clinical practice and two international meningitis guidelines. Results A central nervous system infection was identified in 90% of LLM sessions. All recommended imaging, while 81% suggested lumbar puncture. Blood cultures and specific mastoiditis work-up were proposed in only 62% and 38% sessions, respectively. Only 38% of sessions provided the correct empirical antibiotic treatment, while antiviral treatment and dexamethasone were advised in 33% and 24%, respectively. Misleading statements were generated in 52%. No significant correlation was found between LLMs’ text length and performance (r=0.29, p=0.20). Among all LLMs, GTP-4 demonstrated the best performance. Discussion Latest LLMs provide valuable advice on differential diagnosis and diagnostic procedures but significantly vary in treatment-specific information for bacterial meningitis when introduced to a realistic clinical scenario. Misleading statements were common, with performance differences attributed to each LLM’s unique algorithm rather than output length. Conclusions Users must be aware of such limitations and performance variability when considering LLMs as a support tool for medical decision-making. Further research is needed to refine these models' comprehension of complex medical scenarios and their ability to provide reliable information. Data are available upon reasonable request.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapidly scalable and low-cost public health surveillance reporting system for COVID-19.","authors":"Vivek Jason Jayaraj, Chiu-Wan Ng, Victor Chee-Wai Hoe, Diane Woei-Quan Chong, Sanjay Rampal","doi":"10.1136/bmjhci-2023-100759","DOIUrl":"10.1136/bmjhci-2023-100759","url":null,"abstract":"<p><strong>Objective: </strong>Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting.</p><p><strong>Methods: </strong>A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources.</p><p><strong>Results: </strong>Key COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5% user conversion rate, evidencing its value and adaptability.</p><p><strong>Conclusion: </strong>This cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11077347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139490782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulating AI for health.","authors":"Ian Oppermann","doi":"10.1136/bmjhci-2023-100931","DOIUrl":"10.1136/bmjhci-2023-100931","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138884377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}