Healthcare Technology Letters最新文献

{"title":"E-Variance: An Application to Assure Clinical Data Integrity and Improve Patient Safety and Workflows in Electronic Medical Records","authors":"Shafiq Rahman,&nbsp;Konstantin Milman,&nbsp;Taek-Soo Lee","doi":"10.1049/htl2.70069","DOIUrl":"10.1049/htl2.70069","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Ensuring critical data integrity practices are often missing from the confidence of decision-making in the recorded clinical data. Thus, we developed an application to report clinical data errors to provide effective healthcare IT services and maintain clinical data integrity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>The RadOnc E-Variance web-based application was designed and developed to report data inconsistencies and variances noted during clinical operations. The data inconsistency issues can be reported in 3 broad categories: Patient-related, Non-Patient related, and other suggestions/open-ended, allowing uploading of images or document files. The contents presented on the web are dynamically created in the application and are stored in a database. In the back-end, data analytics and detailed assessments are available on the dashboard. Users reporting variances remain anonymous to ensure reporting freely and frequently.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Once implemented clinically, we have found significant improvements with fewer errors, ensured patient treatment safety, and improved workflows in our state-wide clinics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The RadOnc E-Variance application effectively increases patient safety by ensuring data integrity and minimises risks in the EMR applications at multifaceted enterprise levels of hospital environments by reducing the inconsistencies in data recording and errors recorded in electronic health records.</p>\u0000 </section>\u0000 </div>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217323","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148082784","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":"A Hybrid Ensemble Approach for Early-Stage Diabetes Detection","authors":"Rachana Katuwal KC,&nbsp;Su Yang","doi":"10.1049/htl2.70060","DOIUrl":"10.1049/htl2.70060","url":null,"abstract":"<p>Diabetes has become a critical global health concern, particularly in regions where access to diagnostic facilities is limited. In this work, we propose a hybrid framework that combines extreme gradient boosting (XGBoost) and deep neural networks (DNNs) for early-stage diabetes detection, using soft voting to generate the final ensemble predictions. The proposed framework was evaluated on two datasets: the widely used Diabetes UCI dataset and a newly collected dataset from Nepal. The ensemble method achieved 99% accuracy (ACC) with an area under the curve (AUC) of 1.00 on the Diabetes UCI dataset, and 91% ACC with a 0.96 AUC on the Nepal diabetes dataset, demonstrating its strong generalisability across distinct populations. Compared to individual models, the hybrid approach offered increased stability and a lower rate of false negatives, which is particularly important in clinical contexts. These findings highlight the potential of hybrid machine learning–deep learning models as cost-effective, scalable and generalisable decision-support tools for diabetes risk assessment.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12905728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203167","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148078717","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":"Toward Efficient Identification of Retinal Diseases: A Lightweight Convolutional Neural Network-Based Approach Using Optical Coherence Tomography","authors":"Utsab Saha,&nbsp;Puja Saha,&nbsp;MD Jahin Alam,&nbsp;Maruf Ahmed","doi":"10.1049/htl2.70059","DOIUrl":"10.1049/htl2.70059","url":null,"abstract":"<p>Retinal diseases are a major cause of both temporary and permanent vision loss, making early detection and treatment essential to prevent irreversible damage, as the retina's ability to transmit light signals to the brain can be compromised. While deep learning has shown strong potential in diagnosing such conditions using optical coherence tomography (OCT) images, many existing models are highly complex and computationally intensive, making them impractical for deployment on edge devices in clinical settings. To address this issue, we propose a diagnostic framework based on a lightweight deep-learning architecture specifically designed for efficient retinal disease detection. Our model integrates two key components: a lite convolution block, utilizing depthwise separable convolutions for computational efficiency, and a global-local fusion block, which captures both fine-grained local and contextual global features. A squeeze-and-excitation mechanism further refines channel-wise feature importance, all while keeping the parameter count to only 0.27 million. We evaluate our model on three benchmark datasets-OCT 2017, OCT C8 and OCTDL- achieving accuracies of 99.70%, 95.00% and 97.26%, respectively. Our approach demonstrates strong and stable performance validated by confusion matrix and ROC analysis, while Grad-CAM visualizations enhance interpretability, collectively aiming to offer a practical and efficient solution for real-time retinal disease diagnosis.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12896433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203200","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148074510","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071884","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":"A Comprehensive Review of Artificial Intelligence Methods in Bone Age Assessment","authors":"Mohsen Borjalizadeh,&nbsp;Farshid Babapour Mofrad,&nbsp;Midya Yousefzamani","doi":"10.1049/htl2.70056","DOIUrl":"10.1049/htl2.70056","url":null,"abstract":"<p>Bone age reflects individual skeletal maturity and is an important factor in the follow-up and monitoring of growth and development in children. Determination of bone age by paediatricians has remained one of the most typical indications that require the use of radiology, and the historical method by which radiologists determine bone age is from a bone age atlas. However, there are still some challenges. The limited case diversity related to race, geography, and age distribution; small sample sizes; and the lack of expert validation by multiple radiologists limit the generalisability of current models. Many underlying comorbidities or health histories are often overlooked in developed models. The models of the future, which provide greater accuracy and clinical usefulness, must encompass more diverse datasets, more thorough health histories, expert validation, and fast but reliable artificial intelligence (AI) models. As an educational review, this study analysed a variety of AI-based approaches that have emerged in the past several years for paediatric bone age assessment (most using hand and wrist radiographs and often coupled with radiology reports). Of these, models such as RCNN, which we evaluated with mean absolute error, showed great potential. There are great future clinical applications and advancements that can progressively transform bone age assessment and evaluation from AI. Notably, we did identify the gaps and opportunities for potentially improving the future clinical approach of bone age assessment and evaluation.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12892095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182874","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071885","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071887","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}