Healthcare Technology Letters最新文献

{"title":"A Systematic Review of Artificial Intelligence-Based Clinical Decision Support Systems in Prostate Cancer Management","authors":"Senobar Naderian, Farzin Soleimanzadeh, Leila Nikniaz, Sarvin Sanaie, Fatemeh Sadeghi-Ghyassi, Taha Samad-Soltani","doi":"10.1049/htl2.70026","DOIUrl":"10.1049/htl2.70026","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Prostate cancer (PCa) affects over 1.4 million individuals annually and remains one of the leading causes of cancer-related death globally. Artificial intelligence-based clinical decision support systems (AI-CDSS) are increasingly used to improve diagnostic and treatment decisions in PCa management.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This systematic review aims to evaluate the development, implementation, and limitations of AI-based CDSS applied to the diagnosing, staging, treatment, or management of PCa.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The review protocol was registered with PROSPERO (ID = CRD42024498335) and adhered to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Using the (population/intervention/comparison/outcome) PICO framework, research questions were formulated to examine system development, AI models applied, adherence to CDSS standards, and integration into clinical decision-making phases. A systematic search of PubMed, Scopus, Web of Science and Embase retrieved 1137 articles; after removing 173 duplicates, 964 records were screened. A subsequent update search in June 2024 identified no additional papers. Two reviewers independently screened titles and abstracts, full texts, and resolved disagreements through discussion with a third reviewer. Studies were included if they developed and evaluated AI-based CDSS for PCa with reported performance metrics. Studies were excluded if they did not involve AI-based CDSS, lacked evaluation data, or did not address clinical decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Ten studies met the inclusion criteria. Cohorts ranged from 10 to 1.93 million records. Reported tasks included diagnosis, risk/recurrence prediction, survival estimation, and radiotherapy/ brachytherapy plan optimization. Common models were gradient boosting, neural networks, and deep reinforcement learning (DRL). Performance was variable but modern ML generally outperformed traditional baselines for survival and risk prediction (e.g., AUROC ≈ 0.84 for three-year recurrence; C-index up to 0.92 for five-year progression-free survival (PFS)). A workflow CDSS without predictive modeling reduced decision-making time by 60%. Key limitations included small or heterogeneous samples, limited generalizability, inconsistent or incomplete data inputs, scarce external validation/calibration, and interpretability concerns. Core CDSS components were inconsistently implemented.</p>\u0000 </section>\u0000 \u0000 ","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12625777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145557956","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":"Robust Human Gait Speed Recognition Under Non-Ideal Conditions for Suspension-Assisted Walking Systems","authors":"Yongfei Feng,&nbsp;Chunxiao Sang,&nbsp;Lei Tian,&nbsp;Zhengcai Wang,&nbsp;Fangyan Dong,&nbsp;Fei Wang,&nbsp;Hongbo Wang","doi":"10.1049/htl2.70029","DOIUrl":"10.1049/htl2.70029","url":null,"abstract":"<p>Accurate gait speed estimation is vital for mobility assessment, rehabilitation monitoring, and intelligent assistive systems. However, existing methods often struggle under non-ideal conditions such as abrupt speed changes, asymmetric steps, or environmental disturbances. This study proposes a reliable framework for estimating gait speed, which integrates inertial measurement unit (IMU) signals with a hybrid deep learning model based on convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks. Six IMUs were placed on bilateral thighs, calves, and ankles to capture comprehensive lower-limb motion data. After noise filtering, gait cycle segmentation, and Z-score normalisation, features were extracted across time, frequency, and time-frequency domains using Fast Fourier and Continuous Wavelet Transforms (FFT and CWT). A two-stage feature selection strategy combining Random Forest (RF) and Principal Component Analysis (PCA) reduced dimensionality while preserving discriminative power. The CNN-BiLSTM model was trained and tested on data from eight healthy subjects under variable-speed treadmill walking. Results showed superior performance, with an average <i>R²</i> of 95.2% and a root mean square error (RMSE) of 0.0301 m/s, outperforming convolutional neural network (CNN), long short-term memory (LSTM), GRU, and backpropagation (BP) models. Sensor ablation confirmed the value of multi-site integration, and real-time tests validated online applicability. This work offers a reliable, high-precision solution for gait speed estimation under non-ideal conditions, with potential for wearable gait analysis and intelligent rehabilitation.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12624757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145557910","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":"Isolated Effects of Bone, Spring and Surgical Parameters on the Surgical Outcome in Spring-Assisted Sagittal Synostosis Correction","authors":"Jenson Jacob,&nbsp;Selim Bozkurt","doi":"10.1049/htl2.70028","DOIUrl":"10.1049/htl2.70028","url":null,"abstract":"<p>Sagittal synostosis can be corrected using spring-assisted cranioplasty which, involves removing bony parts from the skull and inserting compressed springs to expand the skull. However, surgical outcomes in spring-assisted cranioplasty may remain suboptimal due to the interacting effects of skull, and spring parameters and surgical settings. Therefore, understanding the effects of skull and spring parameters, and surgical settings, on surgical outcomes is crucial to improving spring-assisted cranioplasty. The aim of this study is to evaluate the isolated effects of calvarial bone and spring parameters and surgical settings on the surgical outcome in the correction of sagittal synostosis with spring-assisted cranioplasty. A 3D parametric skull model with sagittal synostosis was used to simulate spring-assisted cranioplasty. Computational simulations were used to simulate the effects of spring, surgical, and skull parameters. Results showed that relatively low elastic modulus and thickness in the calvarial bones had remarkable effects on surgical outcomes in sagittal synostosis after spring-assisted cranioplasty. However, after spring-assisted cranioplasty, the post-operative cranial index increased a little for higher elastic modulus and thickness in the calvarial bones. Therefore, evaluating the mechanical and geometric properties of the skull during surgical planning may ensure successful surgical outcomes in sagittal synostosis treated with spring-assisted cranioplasty.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12610409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145514692","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":"Poo Manager: Co-Designing a Serious Computer Game to Improve Constipation Management Awareness in Carers of People With Intellectual Disabilities","authors":"Serena Daniel,&nbsp;Ruth Bishop,&nbsp;Stephen Howard,&nbsp;Sarah Lennard,&nbsp;Samantha Pye,&nbsp;Richard Laugharne,&nbsp;Christopher James,&nbsp;Rohit Shankar","doi":"10.1049/htl2.70024","DOIUrl":"10.1049/htl2.70024","url":null,"abstract":"<p>Approximately 2% of the population are people with intellectual disability (PwID). PwID are prone to multimorbidity, polypharmacy and premature mortality. Constipation is a prevalent and life-threatening issue for PwID fostered by iatrogenic harm, ignorance and stigma in family, and health and care settings. Despite its clinical significance, educational tools targeting caregivers’ knowledge of constipation prevention and management in PwID are scarce. Serious games (SGs) offer a novel and engaging platform to educate carers and reduce health stigma. <i>Poo manager</i> was collaboratively designed by a team of clinicians, academics, game developers and people with lived experience. The game involves navigating a care home, identifying constipation risk factors and selecting appropriate management strategies through evidence-based gameplay. A co-production workshop involving PwID, their carers and clinicians provided feedback on gameplay. Evaluation was conducted using validated measures to examine intervention acceptability, appropriateness and adoption feasibility. Over 60% found the SG enjoyable and believed it could improve constipation management knowledge. Scores demonstrated high acceptability, appropriateness and feasibility, though some concerns around game usability were noted. <i>Poo manager</i> is a promising SG intervention with strong potential to enhance constipation care for PwID. Further development and formal effectiveness evaluation are warranted to ensure impact on clinical outcomes.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12583887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145453623","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":"Deep Learning-Based Analysis of Mammographic Images for Breast Cancer Detection Using Transfer Learning","authors":"Fajar Walayat,&nbsp;Allah Ditta,&nbsp;Zafar Iqbal Karmani,&nbsp;Bilal Shoaib,&nbsp;Tehseen Mazhar,&nbsp;Muhammad Adnan Khan,&nbsp;Rashid A. Saeed","doi":"10.1049/htl2.70019","DOIUrl":"https://doi.org/10.1049/htl2.70019","url":null,"abstract":"<p>Breast cancer ranks as one of the most perilous diseases, being the second most prevalent disease among women globally. This condition involves a rapid and uncontrolled proliferation of breast cells. Prominent symptoms of breast cancer include alterations in breast size and shape, discomfort, the presence of lumps, and nipple discharge. Primarily affecting women aged 50–60 years and older, this malignancy exhibits a substantial occurrence rate within the elderly demographic, accounting for over 80% of cases. Unfortunately, breast cancer claims the lives of numerous women annually. However, early detection and diagnosis offer a chance for a positive outcome, potentially reducing the fatality rate by 20% and enhancing patient survival rates. Past research endeavours have centred on training diverse models to detect breast cancer in its nascent stages. The objective of this research is to detect breast cancer at an early stage and facilitate medical fertility. In this work, we proposed an innovative transfer learning methodology for identifying breast cancer through mammograms. This approach not only aids medical professionals in automating diagnosis but also addresses potential human errors during analysis. By employing transfer learning, our technique empowers doctors to detect cancer in its incipient stages, substantially enhancing diagnostic accuracy. The convolutional neural network (CNN) architectures explored were Inception-v3, ResNet-50, VGG-16, SqueezeNet, and AlexNet. The dataset was collected from the local hospital of Lahore, comprising 900 mammogram images. After segmenting the mammogram images and eliminating noise, we conducted a comprehensive assessment of the CNN architectures. Remarkably, the AlexNet architecture outperformed its counterparts, achieving an outstanding accuracy of 96.7%. This achievement underscores the potency of our transfer learning model combined with distinct solver types.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366549","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 Detection of Bone Fractures in Muscle X-Ray Images Using Multiband-Frequency Aware Deep Representation Learning","authors":"Rishab Kumar Pattnaik,&nbsp;Rajesh Kumar Tripathy,&nbsp;Haipeng Liu","doi":"10.1049/htl2.70021","DOIUrl":"10.1049/htl2.70021","url":null,"abstract":"<p>The automated detection of bone fractures in muscle X-ray (MXR) images using artificial intelligence is vital for successful treatment and better patient outcomes. This paper proposes the multiband-frequency aware deep representation learning network (MFADRLN)-based automated approach for detecting bone fractures in MXR images. The discrete wavelet transform-based multiresolution analysis is utilised to evaluate subband images from the MXR image. Then, the deep representation learning (DRL) is applied to each subband of the MXR image, followed by feature concatenation, a dense layer, and a sigmoid layer for detecting bone fractures. The DRL branch for each subband mainly consists of the pre-trained or frozen EfficientNetV2B2-based block, a flattened layer, two successive dense-batch normalisation (BN)-dropout layer blocks, followed by the sigmoid layer for extracting multi-band frequency-aware features from MXR images. The MFADRLN model's importance is to capture the frequency-specific and spatial information of the MXR image and obtain an improved feature representation for efficient detection of bone fractures. The publicly available musculoskeletal X-ray image databases are used to evaluate the performance of the proposed MFADRLN-based approach. The results reveal that the MFADRLN has obtained the accuracy and F1-score values of 92.22% and 0.841, respectively, for detecting bone fractures. The proposed approach has demonstrated superior performance compared to the existing transfer learning techniques (ResNet50, EfficientNetV2B2, DenseNet201, MobileNetV2, InceptionV3, and XceptionNet), Vision transformer and swin transformer models to detect bone fractures in MXR images from the same database. The classification performance of the MFADRLN is compared with existing deep-learning techniques for detecting bone fractures in MXR images.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12516016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145293973","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":"Differentiation of Low and High-Grade Meningiomas Using Transfer Learning on MR Images","authors":"Oktay Fasihi Shirehjini,&nbsp;Farshid Babapour Mofrad,&nbsp;Mohammadreza Shahmohammadi,&nbsp;Fatemeh Karami","doi":"10.1049/htl2.70022","DOIUrl":"https://doi.org/10.1049/htl2.70022","url":null,"abstract":"<p>Despite the advantages of magnetic resonance (MR) images in diagnosing intracranial tumours such as meningiomas, non-invasive grading remains challenging. In this respect, the potential of transfer learning and data augmentation methods was explored to develop computer-aided diagnosis (CAD) systems for automatically differentiating between low and high-grade meningiomas on MR images. Four MR modalities were enrolled, including T1-weighted, T2-weighted, fluid-attenuated inversion recovery and T1-weighted contrast enhanced and the effectiveness of different data augmentation and deep learning approaches in determining the optimum AI-based solution for meningioma grading was investigated using a multi-stage framework, which included image preprocessing, data augmentation by either conventional geometric transformations or fancy principal component analysis (PCA) and the use of ImageNet pre-trained, well-known convolutional neural networks (CNNs) for training models with the two widely utilised optimisers of adaptive moment estimation (Adam) and stochastic gradient descent (SGD). Finally, performance was evaluated utilising standard metrics to select the best-obtained model with optimal approaches. Results indicated that trained visual geometry group (VGG)-19 with generated images by fancy PCA performed best with an accuracy of 97.80% and 98.90% in classifying unseen samples using SGD and Adam optimisers, respectively. This procedure also yielded acceptably reproducible results in a second study, ensuring the efficiency of each model using a small-scale dataset. Developing CAD systems using pre-trained CNNs and fancy PCA is a promising approach for classifying meningiomas on MR images into low and high-grade categories, which can serve as an alternative to invasive methods and provide valuable assistance for accurate diagnosis before treatment.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272627","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":"Trustworthy AI in Telehealth: Navigating Challenges, Ethical Considerations, and Future Opportunities for Equitable Healthcare Delivery","authors":"Seyyed Ali Zendehbad,&nbsp;Jamal Ghasemi,&nbsp;Farid Samsami Khodadad","doi":"10.1049/htl2.70020","DOIUrl":"10.1049/htl2.70020","url":null,"abstract":"<p>Trustworthy artificial intelligence (TAI) will transform telehealth by providing safe, transparent, and ethically compliant systems that enhance clinician decision-making and patient relationships. This systematic review examines how TAI and large language models (LLMs), including large language model meta ai (LLaMA), can be integrated into telehealth systems, their role in optimizing e-consultation workflows, and their capacity to support personalized care through data collected by wearable biosensors and biological microelectromechanical systems (BioMEMS). These devices monitor physiological and behavioral data, such as heart rate, blood pressure, and emotional state. TAI enables effective diagnostics and targeted treatment by combining various information sources, including biosensor readings, patient history, and cognitive data. Firmware integrity plays a crucial role in ensuring security, reliability, and continuous data encryption. This review analyses 135 papers (October 2020-March 2025) from databases like IEEE Xplore, PubMed, and Scopus to demonstrate TAI's potential to enhance resource use and patient engagement. However, widespread adoption depends on overcoming technical challenges, improving firmware reliability, strengthening data security, and addressing ethical concerns. This review offers valuable guidance for engineers, system architects, and healthcare providers to create a sensitive and effective telehealth ecosystem.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214025","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":"A Model of the First Trimester Evaluation of Foetal Movements and Their Outcomes via Explainable Artificial Intelligence: A Multicentric Study","authors":"Manohar Pavanya,&nbsp;Krishnaraj Chadaga,&nbsp;Vennila J,&nbsp;Akhila Vasudeva,&nbsp;Bhamini Krishna Rao,&nbsp;Shashikala K. Bhat","doi":"10.1049/htl2.70014","DOIUrl":"10.1049/htl2.70014","url":null,"abstract":"<p>Foetal outcomes with reduced foetal movements in the later pregnancy are widely reported. We intend to quantify early foetal movements (FMs) through a checklist and their foetal outcomes via explainable artificial intelligence. It is a prospective observational study of 356 foetuses in the first trimester, and we were able to screen only 230 foetuses for early foetal growth restriction (FGR). Of which 26 were FGR and 204 were normal and were identified from the dataset using non-probability convenience sampling techniques. JASP 0.18.3, Jamovi 2.3.21, and Google Collaboratory were used to construct the predictive model. Ultrasound scores of more than 8 had favourable indicators of a normal foetus. CatBoost had the highest accuracy and recall of 87; the highest precision of 79 was given by random forest (RF), decision tree (DT), K-nearest neighbour (KNN), and CatBoost; and the F1 score of 83 was given by CatBoost. The lowest Hamming loss of 0.13 was obtained via CatBoost. The highest Jaccard score of 0.87 was by CatBoost. The stacked model has an accuracy of 89, a precision of 79, and a recall of 83. Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME), QLattice, and Anchor also provided good explanations. The created model can serve as a warning tool to obstetricians to make timely medical decisions.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081924","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":"An AI-Enabled, Patient-Centred Digital Platform for Integrated Chronic Heart Failure Management: Architecture, Validation and Clinical Insights","authors":"George Petridis,&nbsp;Apostolia Karabatea,&nbsp;Constantinos Bakogiannis,&nbsp;Emmanouil S. Rigas,&nbsp;Haridimos Kondylakis,&nbsp;Alexander Berler,&nbsp;Konstantinos Kangelidis,&nbsp;Nikolaos Malamas,&nbsp;Angelina Kouroubali,&nbsp;Dimitrios Katehakis,&nbsp;Vassilios Vassilikos,&nbsp;Panagiotis D. Bamidis,&nbsp;Antonios Billis","doi":"10.1049/htl2.70015","DOIUrl":"10.1049/htl2.70015","url":null,"abstract":"<p>Healthcare systems across Europe and globally are increasingly challenged by the need to deliver high-quality, coordinated care for complex patient populations, such as those living with chronic heart failure (CHF). Many national healthcare policies consider the adoption and implementation of patient-centred and interoperable information communication technologies-enabled solutions offered in a single digital platform as a key facilitator towards the transition to integrated and coordinated care. Aiming to support CHF patients and to assist their management, in this paper, we present CareCardia, a modular digital solution designed to support the comprehensive management of CHF. CareCardia offers an interoperable ecosystem that connects healthcare professionals, informal caregivers and patients along a unified CHF care pathway spanning across diagnosis, acute care and jointly managed long-term care. Specifically, CareCardia integrates state-of-the-art, clinical evidence-based technologies such as a clinical decision support system and an exergaming platform that will follow patients through the CHF journey. This paper outlines the system architecture and core functionalities of CareCardia prototype. We also present early findings from the initial exploration of the tool, discussing its anticipated impact on CHF and its potential to foster patient empowerment across the continuum of care.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998838","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}