BMJ Health & Care Informatics最新文献

{"title":"Comparative performance of logistic regression, multilayer perceptron and decision tree models for predicting surgical pressure injuries: a retrospective cohort study.","authors":"Chia-Yen Li, Chi-Ming Chu, Chao-Wen Chen, Hung-Yen Ke, Peng-Ching Hsiao, Hsueh-Hsing Pan","doi":"10.1136/bmjhci-2025-101532","DOIUrl":"10.1136/bmjhci-2025-101532","url":null,"abstract":"<p><strong>Objectives: </strong>Surgical pressure injuries (SPIs) are a significant patient safety risk due to prolonged immobility and tissue hypoperfusion under general anaesthesia. Existing risk assessment tools lack real-time predictive capabilities. This study developed and validated a machine-learning model for SPI prediction and clinical integration.</p><p><strong>Method: </strong>This retrospective cohort study analysed electronic health records from 931 surgical inpatients under general anaesthesia between January 2016 and December 2021. SPI cases were identified using ICD-10 codes with 1:1 matching by medical specialty. Data preprocessing included imputation, normalisation and outlier removal. Logistic regression (LR), multilayer perceptron (MLP) and decision tree (DT) models were developed and validated via cross-validation. Model performance was assessed using area under the curve (AUC), accuracy, precision, recall and F1 score.</p><p><strong>Results: </strong>Significant SPI predictors included the Charlson Comorbidity Index (p<0.001), number of medication types (p=0.001) and body mass index (p<0.001). The MLP outperformed LR (AUC=0.707) and DT (AUC=0.717), achieving the highest AUC (0.836), accuracy (0.773), precision (0.812), recall (0.688) and F1 score (0.745).</p><p><strong>Discussion: </strong>The MLP model effectively identified key SPI risk factors, outperforming LR and DT by capturing non-linear relationships. Its integration into clinical workflows may enhance perioperative risk management through early detection and targeted interventions.</p><p><strong>Conclusion: </strong>Machine learning integration can improve early SPI detection and personalised prevention. The MLP model demonstrated the highest potential for real-time SPI risk stratification. Future research should validate this model across diverse surgical populations and develop scalable strategies for clinical implementation.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084962","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":"Implementation of patient safety monitoring systems in hospitals: a systematic review.","authors":"Ghasem Alizadeh-Dizaj, Shahla Damanabi, Mohammad Esmaeil Hejazi, Samira Raoofi, Leila R Kalankesh","doi":"10.1136/bmjhci-2024-101392","DOIUrl":"10.1136/bmjhci-2024-101392","url":null,"abstract":"<p><strong>Background: </strong>The significance of patient safety has been acknowledged in healthcare systems, prompting the need for effective patient safety monitoring systems (PSMSs). These systems' endeavour is to manage patient safety data and improve overall safety within healthcare organisations. This study aims to characterise the implementation of and outputs of such systems across hospital settings.</p><p><strong>Methods: </strong>A systematic literature review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review included a comprehensive search of databases such as PubMed, EMBASE, Scopus, Web of Science and Google Scholar for studies published in English up to 30 July 2024. The focus was on monitoring systems that manage patient safety in medical care, with inclusion criteria that required studies to examine the application of PSMSs and report their implementation outputs.</p><p><strong>Results: </strong>The literature search yielded 23 relevant studies published between 2009 and 2023. PSMSs were used in various clinical contexts, including emergency departments, radiology wards, intensive care units and operating rooms, addressing various issues such as medication safety, healthcare-associated infections, blood transfusion errors, surgical site infections, laboratory and radiology adverse events. The findings indicated positive outputs from the implementation of PSMSs. Furthermore, these systems provide valuable information and timely alerts and contribute to a culture of safety in healthcare facilities.</p><p><strong>Conclusions: </strong>PSMSs can be used for enhancing safety practices, reducing adverse events and promoting a culture of patient safety. Further research and continued implementation of PSMSs are essential to further augment patient safety standards in healthcare settings.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084998","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":"Automated sepsis prediction from unstructured electronic health records using natural language processing: a retrospective cohort study.","authors":"Lipi Mishra, Sowmya Muchukunte Ramaswamy, Broderick Ivan McCallum-Hee, Keaton Wright, Riley Croxford, Sunil Belur Nagaraj, Matthew Anstey","doi":"10.1136/bmjhci-2024-101354","DOIUrl":"10.1136/bmjhci-2024-101354","url":null,"abstract":"<p><strong>Objective: </strong>Artificial intelligence (AI) holds promise for predicting sepsis. However, challenges remain in integrating AI, natural language processing (NLP) and free text data to enhance sepsis diagnosis at emergency department (ED) triage. This study aimed to evaluate the effectiveness of AI in improving sepsis diagnosis.</p><p><strong>Methods: </strong>This retrospective cohort study analysed data from 134 266 patients admitted to the ED and subsequently hospitalised between 1 January 2016 and 31 December 2021. The data set comprised 10 variables and free-text triage comments, which underwent tokenisation and processing using a bag-of-words model. We evaluated four traditional NLP classifier models, including logistic regression, LightGBM, random forest and neural network. We also evaluated the performance of the BERT classifier. We used area under precision-recall curve (AUPRC) and area under the curve (AUC) as performance metrics.</p><p><strong>Results: </strong>Random forest exhibited superior predictive performance with an AUPRC of 0.789 (95% CI: 0.7668 to 0.8018) and an AUC of 0.80 (95% CI: 0.7842 to 0.8173). Using raw text, the BERT model achieved an AUPRC of 0.7542 (95% CI: 0.7418 to 0.7741) and AUC of 0.7735 (95% CI: 0.7628 to 0.8017) for sepsis prediction. Key variables included ED treatment time, patient age, arrival-to-treatment time, Australasian Triage Scale and visit type.</p><p><strong>Discussion: </strong>This study demonstrates AI, particularly random forest and BERT classifiers, for early sepsis detection in EDs using free-text patient concerns.</p><p><strong>Conclusion: </strong>Incorporating free text into machine learning improved diagnosis and identified missed cases, enhancing sepsis prediction in the ED with an AI-powered clinical decision support system. Large, prospective studies are needed to validate these findings.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069089","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":"New openEHR technology and clinical collaboration in vital steps toward improved patient care and true interoperability: Scotland's first digital ReSPECT emergency care plan.","authors":"Susannah Mclean, Paul Miller, Alistair Ewing, Juliet Anne Spiller, Lynsey Fielden","doi":"10.1136/bmjhci-2025-101435","DOIUrl":"10.1136/bmjhci-2025-101435","url":null,"abstract":"<p><strong>Objective: </strong>To deploy a digital application of the Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) across health boards (HBs).</p><p><strong>Methods: </strong>Clinicians, patients and other regional stakeholders collaborated with the Scottish National Technology Service (NTS) defining requirements. Development was agile with user feedback.</p><p><strong>Results: </strong>The ReSPECT web application developed on Scotland's National Digital Platform used an openEHR Clinical Data Repository. Plans can be viewed and edited across settings. Deployed in 2020, by July 2025, 8 of 14 HBs were onboarded and >5500 patients had digital ReSPECT plans.</p><p><strong>Discussion: </strong>openEHR structures clinical data in a modular way, enabling other applications to use the same data layer. Close collaboration between technicians and users fulfilled the application's requirements and solved problems together.</p><p><strong>Conclusions: </strong>Collaboration on the digital ReSPECT accelerated deployment, enabling more people's wishes and clinical recommendations to be captured and shared across care settings and transitions. openEHR technology enables new data uses.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145039124","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":"Data as medicine's backbone: redefining its value to foster innovation in the data economy.","authors":"Michael Byczkowski","doi":"10.1136/bmjhci-2025-101513","DOIUrl":"10.1136/bmjhci-2025-101513","url":null,"abstract":"<p><p>Data are the engine of modern medicine, yet its economic trade-off remains unequally distributed: hospitals and research institutions shoulder the effort of collection, while life science companies reap the financial rewards. This imbalance raises pressing questions about fairness, rights and sustainability.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145039197","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":"Wearable device-measured circadian rest-activity rhythm with mortality risk in patients with cancer.","authors":"Xionge Mei, Nana Zheng, Biao Li, Yue Liu, Lulu Yang, Tong Luo, Ngan Yin Chan, Joey Wy Chan, Yaping Liu, Xiao Tan, Christian Benedict, Yun Kwok Wing, Jihui Zhang, Hongliang Feng","doi":"10.1136/bmjhci-2025-101553","DOIUrl":"10.1136/bmjhci-2025-101553","url":null,"abstract":"<p><strong>Objectives: </strong>The objectives were to examine the associations between accelerometer-measured circadian rest-activity rhythm (CRAR), the most prominent circadian rhythm in humans and the risk of mortality from all-cause, cancer and cardiovascular disease (CVD) in patients with cancer.</p><p><strong>Methods: </strong>7456 cancer participants from the UK Biobank were included. All participants wore accelerometers from 2013 to 2015 and were followed up until 24 January 2024, with a median follow-up of 9.00 years. The multidimensional parameters of the CRAR were calculated using the 7-day accelerometer data collected under free-living conditions. Cox proportional hazard models were used to evaluate the associations between CRAR and all-cause, cancer and CVD mortality.</p><p><strong>Results: </strong>Among 7456 cancer patients (mean age: 65.7±6.87 years; 58.85% women) aged 44-79 years, 934 (12.5%) deaths occurred over 9.00 years (64 525 person-years). CRAR disruptions, including low amplitude, low mesor and high fragmentation, were significantly associated with an increased risk of all-cause mortality (adjusted HR range, 1.30-2.00), cancer (adjusted HR range, 1.46-1.83) and CVD mortality (adjusted HR range, 1.73-2.66) in patients with cancer.</p><p><strong>Discussion: </strong>These associations were robust across various cancer types. In addition, CRAR disruptions, particularly low amplitude, exceeded multiple traditional risk factors such as poor sleep, smoking, alcohol consumption, obesity and unhealthy diet in predicting mortality.</p><p><strong>Conclusion: </strong>CRAR parameters may serve as novel and robust predictors of mortality in patients with cancer.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12421596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032799","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":"Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol.","authors":"Amos Otieno Olwendo, Gideon Kikuvi, Simon Karanja","doi":"10.1136/bmjhci-2024-101419","DOIUrl":"https://doi.org/10.1136/bmjhci-2024-101419","url":null,"abstract":"<p><strong>Introduction: </strong>This study seeks to determine incidence, comorbidities and drivers for new HIV infections to develop, test and validate a risk prediction model for screening for new cases of HIV.</p><p><strong>Methods and analysis: </strong>The study has two components: a cross-sectional study to develop the prediction model using the HIV dataset from the Kenya AIDS and STI Control Programme and a 15-month prospective study for the validation of the model. Inferential analysis will be conducted using algorithms that perform best in disease prediction: Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron. Model sensitivity and specificity will be examined using the receiver operating characteristic curve, and performance will be evaluated using metrics: accuracy, precision, recall and F1 score.</p><p><strong>Ethics and dissemination: </strong>The study obtained ethical approval (JKU/ISERC/02321/1421) from the Jomo Kenyatta University of Agriculture and Technology Ethical and Research Board and a research licence (NACOSTI/P/24/414749) from the National Commission for Science, Technology and Innovation.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144942208","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":"Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.","authors":"Ying Wang, Farah Magrabi","doi":"10.1136/bmjhci-2024-101146","DOIUrl":"10.1136/bmjhci-2024-101146","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the transferability of BERT (Bidirectional Encoder Representations from Transformers) to patient safety, we use it to classify incident reports characterised by limited data and encompassing multiple imbalanced classes.</p><p><strong>Methods: </strong>BERT was applied to classify 10 incident types and 4 severity levels by (1) fine-tuning and (2) extracting word embeddings for feature representation. Training datasets were collected from a state-wide incident reporting system in Australia (<i>n_type/severity=2860/1160</i>). Transferability was evaluated using three datasets: a balanced dataset (<i>type/severity: n_benchmark=286/116</i>); a real-world imbalanced dataset (<i>n_original=444/4837, rare types/severity<=1%</i>); and an independent hospital-level reporting system (<i>n_independent=6000/5950, imbalanced</i>). Model performance was evaluated by F-score, precision and recall, then compared with convolutional neural networks (CNNs) using BERT embeddings and local embeddings from incident reports.</p><p><strong>Results: </strong>Fine-tuned BERT outperformed small CNNs trained with BERT embedding and static word embeddings developed from scratch. The default parameters of BERT were found to be the most optimal configuration. For incident type, fine-tuned BERT achieved high F-scores above 89% across all test datasets (<i>CNNs=81%</i>). It effectively generalised to real-world settings, including rare incident types (eg, clinical handover with 11.1% and 30.3% improvement). For ambiguous medium and low severity levels, the F-score improvements ranged from 3.6% to 19.7% across all test datasets.</p><p><strong>Discussion: </strong>Fine-tuned BERT led to improved performance, particularly in identifying rare classes and generalising effectively to unseen data, compared with small CNNs.</p><p><strong>Conclusion: </strong>Fine-tuned BERT may be useful for classification tasks in patient safety where data privacy, scarcity and imbalance are common challenges.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144882072","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":"Predicting patient deterioration with physiological data using AI: systematic review protocol.","authors":"Lynsey Threlfall, Cen Cong, Victoria Riccalton, Edward Meinert, Chris Plummer","doi":"10.1136/bmjhci-2024-101417","DOIUrl":"10.1136/bmjhci-2024-101417","url":null,"abstract":"<p><strong>Introduction: </strong>The second iteration of the National Early Warning Score has been adopted widely within the UK and internationally. It uses routinely collected physiological measurements to standardise the assessment and response to acute illness. Its use is associated with reduced mortality but has limited positive and negative predictive accuracy. There is a growing body of research demonstrating the effectiveness of artificial intelligence (AI) in predicting clinical deterioration, but there is limited evidence to show which aspect of AI is best suited to this task. This systematic review aims to establish which AI or machine learning algorithm is best suited to analysing physiological data sets to predict patient deterioration in a hospital setting.</p><p><strong>Methods and analysis: </strong>A systematic review will be conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) and the PICOS (Population, Intervention, Comparator, Outcome and Study) frameworks. Eight databases (PubMed, Embase, CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore and ACM Digital Library) will be used to search for studies published from 2007 to the present that meet the inclusion criteria. Two reviewers will screen the studies identified and extract data independently, with any discrepancies resolved by discussion. The review is expected to be completed by January 2026, and the results will be presented in publication by June 2026.</p><p><strong>Ethics and dissemination: </strong>Ethical approval is not required as data will be obtained from published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12336570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788245","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":"Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper.","authors":"Sam Freeman, Isuru Ranapanada, Md Ali Hossain, Kogul Srikandabala, Md Anisur Anisur Rahman, Damminda Alahakoon, Hamed Akhlaghi","doi":"10.1136/bmjhci-2024-101285","DOIUrl":"10.1136/bmjhci-2024-101285","url":null,"abstract":"<p><strong>Introduction: </strong>To address timely care in emergency departments, artificial neural networks (ANNs) with natural language processing will be applied to triage notes to predict patient disposition. This study will develop a predictive model that predicts disposition and type of admission.</p><p><strong>Methods and analysis: </strong>This will include data preprocessing and quality enhancement, masked language modelling, ANN-based fusion network for prediction. Generative artificial intelligence, along with a medical dictionary, will be employed to augment and contextually reconstruct triage notes to disambiguate and improve linguistic quality. Text features will be extracted, and cluster analysis will be performed on the extracted topics and text features to identify distinct patterns.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12314934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764499","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}