Healthcare analytics (New York, N.Y.)最新文献

A focal loss and sequential analytics approach for liver disease classification and detection 肝脏疾病分类和检测的局灶丢失和顺序分析方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-10-04 DOI: 10.1016/j.health.2025.100424

Musa Mustapha , Oluwadamilare Harazeem Abdulganiyu , Isah Ndakara Abubakar , Kaloma Usman Majikumna , Garba Suleiman , Mehdi Ech-chariy , Mekila Mbayam Olivier

{"title":"A focal loss and sequential analytics approach for liver disease classification and detection","authors":"Musa Mustapha , Oluwadamilare Harazeem Abdulganiyu , Isah Ndakara Abubakar , Kaloma Usman Majikumna , Garba Suleiman , Mehdi Ech-chariy , Mekila Mbayam Olivier","doi":"10.1016/j.health.2025.100424","DOIUrl":"10.1016/j.health.2025.100424","url":null,"abstract":"<div><div>Liver disease poses a significant global health challenge requiring accurate and timely diagnosis. This research develops a novel deep learning model, named AFLID-Liver, to improve the classification of liver diseases from medical data. The AFLID-Liver model integrates three key techniques: an Attention Mechanism to focus on the most relevant data features, Long Short-Term Memory (LSTM) networks to process potential sequential information, and Focal Loss to effectively handle imbalances between different disease classes in the dataset. This combination enhances the model's ability to learn complex patterns and make robust predictions. We evaluated AFLID-Liver using a dataset of various patient records, including biomarkers and demographics. Our proposed model achieved superior performance, with 99.9 % accuracy, 99.9 % precision, and a 99.9 % F-score, significantly outperforming a baseline Gated Recurrent Unit (GRU) model (99.7 % accuracy, 97.9 % F-score) and existing state-of-the-art approaches. These results demonstrate AFLID-Liver's potential for highly accurate liver disease detection. To validate the generalizability of the proposed model, we performed cross validation using an external dataset which also yielded a good performance depicting the potential of the proposed model. The novelty lies in the synergistic integration of these techniques, offering a robust approach for clinical decision support and improved patient outcomes. Future research will aim to enhance the computational efficiency, paving the way for its adoption in real-time clinical applications.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100424"},"PeriodicalIF":0.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264752","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}

引用次数: 0

A constrained optimization approach for ultrasound shear wave speed estimation with time-lateral plane cleaning in medical imaging 医学成像中带时间横向平面清洗的超声剪切波速估计约束优化方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-27 DOI: 10.1016/j.health.2025.100423

MD Jahin Alam, Md. Kamrul Hasan

{"title":"A constrained optimization approach for ultrasound shear wave speed estimation with time-lateral plane cleaning in medical imaging","authors":"MD Jahin Alam, Md. Kamrul Hasan","doi":"10.1016/j.health.2025.100423","DOIUrl":"10.1016/j.health.2025.100423","url":null,"abstract":"<div><div>Ultrasound shear wave elastography (SWE) is a noninvasive tissue stiffness measurement technique for medical diagnosis. In SWE, an acoustic radiation force creates shear waves (SW) throughout a medium where the shear wave speed (SWS) is related to the medium stiffness. Traditional SWS estimation techniques are not noise-resilient in handling jitter and reflection artifacts. This paper proposes new techniques to estimate SWS in both time and frequency domains. These new methods utilize loss functions which are: (1) optimized by lateral signal shift between known locations, and (2) constrained by neighborhood displacement group shift determined from the time-lateral plane-denoised SW propagation. The proposed constrained optimization is formed by coupling neighboring particles’ losses with a Gaussian kernel, giving an optimum arrival time for the center particle to enforce local stiffness homogeneity and enable noise resilience. The explicit denoising scheme involves isolating SW profiles from time-lateral planes, creating parameterized masks. Additionally, lateral interpolation is performed to enhance reconstruction resolution and thereby improve the reliability of optimization. The proposed scheme is evaluated on a simulation (US-SWS-Digital-Phantoms) and three experimental phantom datasets: (i) Mayo Clinic CIRS049 model, (ii) RSNA-QIBA-US-SWS, (iii) Private data. The constrained optimization performance is compared with three time-of-flight (ToF) and two frequency-domain methods. The evaluations produced visually and quantitatively superior and noise-robust reconstructions compared to classical methods. Due to the quality and minimal error of SWS map formation, the proposed technique can find its application in tissue health inspection and cancer diagnosis.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100423"},"PeriodicalIF":0.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219004","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}

引用次数: 0

An integrated deep learning approach for enhancing brain tumor diagnosis 一种增强脑肿瘤诊断的集成深度学习方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-25 DOI: 10.1016/j.health.2025.100421

Rabeya Bashri Sumona , John Pritom Biswas , Ahmed Shafkat , Md Mahbubur Rahman , Md Omor Faruk , Yaqoob Majeed

{"title":"An integrated deep learning approach for enhancing brain tumor diagnosis","authors":"Rabeya Bashri Sumona , John Pritom Biswas , Ahmed Shafkat , Md Mahbubur Rahman , Md Omor Faruk , Yaqoob Majeed","doi":"10.1016/j.health.2025.100421","DOIUrl":"10.1016/j.health.2025.100421","url":null,"abstract":"<div><div>The diagnosis of a brain tumor poses a significant challenge due to the varied manifestations of tumors and their impact on patient health. Traditional Magnetic Resonance Imaging (MRI) based methods are time-consuming, expensive, and highly reliant on radiologists’ expertise. Automated and reliable classification techniques are crucial to enhancing diagnostic accuracy, improving patient outcomes, and ensuring timely detection. This study introduces RDXNet, a hybrid deep learning model that integrates ResNet50, DenseNet121, and Xception to improve the classification of multiclass brain tumors. We utilized three publicly available datasets which are Br35H :: Brain Tumor Detection 2020, Figshare Brain Tumor Dataset, and Radiopaedia MRI Scans, combining 7,023 MRI images in four categories: glioma, meningioma, no tumor, and pituitary tumor. After evaluating individual models, we integrated them into RDXNet using feature fusion and transfer learning. Our model achieves an accuracy of 94%, exceeding the performance of individual models and mitigating overfitting. To validate robustness, K-Fold Cross-Validation was conducted across multiple data splits. Additionally, Grad-CAM-based visualizations were employed to enhance interpretability, enabling clinicians to understand the model’s decision-making. Using hybrid deep learning techniques, RDXNet significantly improves classification performance and reliability. This study demonstrates the potential of Artificial Intelligence (AI)-driven computer-aided diagnosis (CAD) systems to support radiologists, enabling faster and more accurate brain tumor identification, ultimately improving patient outcomes. Our proposed hybrid model, RDXNet outperforms individual architectures in multiclass brain tumor classification, achieving state-of-the-art performance and contributing towards faster, more reliable automated diagnosis.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100421"},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219005","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}

引用次数: 0

An analytics-driven review of U-Net for medical image segmentation U-Net用于医学图像分割的分析驱动综述

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-20 DOI: 10.1016/j.health.2025.100416

Fnu Neha , Deepshikha Bhati , Deepak Kumar Shukla , Sonavi Makarand Dalvi , Nikolaos Mantzou , Safa Shubbar

{"title":"An analytics-driven review of U-Net for medical image segmentation","authors":"Fnu Neha , Deepshikha Bhati , Deepak Kumar Shukla , Sonavi Makarand Dalvi , Nikolaos Mantzou , Safa Shubbar","doi":"10.1016/j.health.2025.100416","DOIUrl":"10.1016/j.health.2025.100416","url":null,"abstract":"<div><div>Medical imaging (MI) plays a vital role in healthcare by providing detailed insights into anatomical structures and pathological conditions, supporting accurate diagnosis and treatment planning. Noninvasive modalities, such as X-ray, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US), produce high-resolution images of internal organs and tissues. The effective interpretation of these images relies on the precise segmentation of the regions of interest (ROI), including organs and lesions. Traditional methods based on manual feature extraction are time-consuming, inconsistent, and not scalable. This review explores recent advances in artificial intelligence (AI)-driven segmentation, focusing on Convolutional Neural Network (CNN) architectures, particularly the U-Net family and its variants—U-Net++, and U-Net 3+. These models enable automated, pixel-wise classification across modalities and have improved segmentation accuracy and efficiency. The review outlines the evolution of U-Net architectures, their clinical integration, and offers a modality-wise comparison. It also addresses challenges such as data heterogeneity, limited generalizability, and model interpretability, proposing solutions including attention mechanisms and Transformer-based designs. Emphasizing clinical applicability, this work bridges the gap between algorithmic development and real-world implementation.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100416"},"PeriodicalIF":0.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121178","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}

引用次数: 0

EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis EAGLE-Net：一种用于检测上消化道内镜解剖标志的分层神经网络，用于临床诊断

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-20 DOI: 10.1016/j.health.2025.100420

Thi Thu Huong Nguyen , Van Duy Truong , Xuan Huy Manh , Thanh Tung Nguyen , Hang Viet Dao , Hai Vu

{"title":"EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis","authors":"Thi Thu Huong Nguyen , Van Duy Truong , Xuan Huy Manh , Thanh Tung Nguyen , Hang Viet Dao , Hai Vu","doi":"10.1016/j.health.2025.100420","DOIUrl":"10.1016/j.health.2025.100420","url":null,"abstract":"<div><div>This study proposes a hierarchical network architecture, named EAGLE-Net, for identifying anatomical landmarks in the upper gastrointestinal (GI) tract endoscopic videos. Unlike conventional techniques, which label anatomical landmarks for static endoscopic images, the proposed method aims to classify landmarks from videos of the upper GI tract. Video streams often suffer from many noises and contaminated objects, which requires a new approach to tackle this issue. The proposed technique utilizes hierarchical network architecture, which consists of two stages: endoscopic image quality assessment and anatomical landmark classification. In the first stage, high-quality frames are preserved from GI tract videos. These frames are then used to identify a specific location among ten anatomical landmarks. The proposed method increases the coherence between the hierarchical data levels. It integrates an attention module to strengthen feature connections and utilizes a new hierarchical cross-entropy loss function to optimize model performance. The experimental results demonstrated that the proposed system achieves a high accuracy of 93% on average in both classification stages. In clinical experiments, anatomical landmarks are automatically denoted to help physicians monitor the endoscopy process. In addition, the proposed method demonstrates a potential solution for the deployment of a computer-aided diagnostic application for the detection and treatment of upper GI tract lesions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100420"},"PeriodicalIF":0.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157325","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}

引用次数: 0

A deep learning framework for 3D brain tumor segmentation and survival prediction 三维脑肿瘤分割和生存预测的深度学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-17 DOI: 10.1016/j.health.2025.100418

Ashfak Yeafi, Monira Islam, Md. Salah Uddin Yusuf

{"title":"A deep learning framework for 3D brain tumor segmentation and survival prediction","authors":"Ashfak Yeafi, Monira Islam, Md. Salah Uddin Yusuf","doi":"10.1016/j.health.2025.100418","DOIUrl":"10.1016/j.health.2025.100418","url":null,"abstract":"<div><div>Accurate and efficient segmentation of brain tumors is crucial for early diagnosis, personalized treatment planning, and improved survival rates. Brain tumors exhibit complex spatial and morphological variations, making automated segmentation a challenging task. This study introduces a dynamic segmentation network (DSNet), a novel 3D brain tumor segmentation framework that integrates adversarial learning, dynamic convolutional neural network (DCNN), and attention mechanisms to enhance precision and robustness. DSNet processes 3D magnetic resonance imaging (MRI) volumes, including T1-weighted (T1), T1-weighted with contrast enhancement (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) modalities, capturing rich spatial and contextual features. Leveraging adversarial training, DSNet refines boundary definitions, while dynamic filters adapt to tumor-specific heterogeneities, ensuring accurate segmentation across diverse cases. The attention mechanism further emphasizes tumor-relevant regions, enhancing feature extraction and boundary delineation. The model was trained and validated on the BraTS 2020 dataset, achieving dice similarity coefficients of 0.959, 0.975, and 0.947 for whole tumors (WT), tumor cores (TC), and enhancing tumor (ET) regions, respectively. Its generalizability was further confirmed through evaluations on the BraTS 2019 and BraTS 2018 datasets. Additionally, volumetric features derived from segmented images were used to predict patients’ overall survival rates via a Random Forest (RF) classifier. To enhance accessibility, we integrated the segmentation and prediction processes into a user-friendly web application. DSNet outperforms state-of-the-art methods, providing a robust and accurate solution for 3D brain tumor segmentation with strong clinical potential.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100418"},"PeriodicalIF":0.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095141","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}

引用次数: 0

An analytical review of biosensor-based chronic pain quantification in healthcare 医疗保健中基于生物传感器的慢性疼痛量化分析综述

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-15 DOI: 10.1016/j.health.2025.100419

Aarthi Kannan , Daniel West , Dinesh Kumbhare , Wei-Ting Ting , Md. Younus Ali , Hameem I. Kawsar , Gurmit Singh , Harsha Shanthanna , Eleni Hapidou , Matiar M.R. Howlader

{"title":"An analytical review of biosensor-based chronic pain quantification in healthcare","authors":"Aarthi Kannan , Daniel West , Dinesh Kumbhare , Wei-Ting Ting , Md. Younus Ali , Hameem I. Kawsar , Gurmit Singh , Harsha Shanthanna , Eleni Hapidou , Matiar M.R. Howlader","doi":"10.1016/j.health.2025.100419","DOIUrl":"10.1016/j.health.2025.100419","url":null,"abstract":"<div><div>Current clinical methods for chronic pain assessment lack objective, quantitative measures, creating a critical gap in diagnostic accuracy. This review investigates the relationship between chronic pain and key biomarkers detectable in body fluids, such as glutamate, interleukin-6, nitric oxide, and quinolinic acid. We first discuss the biological mechanisms underlying chronic pain and evaluate the relevance of these biomarkers. The review then focuses on recent advancements in non-enzymatic electrochemical biosensors used to monitor these biomarkers. For each sensor, we summarize performance metrics including sensitivity, detection limits, and linear range, while highlighting the analytical methodologies used to establish correlations between biomarker levels and pain intensity. Our findings demonstrate that quantitative analysis of biomarker fluctuations can enhance chronic pain monitoring. The integration of sensor-based biomarker analytics with clinical workflows may offer a path toward personalized treatment plans and improved decision-making in healthcare supply chains. This review emphasizes the need for continued development of high-precision biosensors as analytical tools for translating physiological signals into clinically actionable pain metrics.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100419"},"PeriodicalIF":0.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095140","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}

引用次数: 0

A penalized regression and machine learning approach for quality-of-life prediction in psoriasis patients 银屑病患者生活质量预测的惩罚回归和机器学习方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-13 DOI: 10.1016/j.health.2025.100417

Teerawat Simmachan , Napatsawan Lerdpraserdpakorn , Jarupa Deesrisuk , Chanadda Sriwipat , Subij Shakya , Pichit Boonkrong

{"title":"A penalized regression and machine learning approach for quality-of-life prediction in psoriasis patients","authors":"Teerawat Simmachan , Napatsawan Lerdpraserdpakorn , Jarupa Deesrisuk , Chanadda Sriwipat , Subij Shakya , Pichit Boonkrong","doi":"10.1016/j.health.2025.100417","DOIUrl":"10.1016/j.health.2025.100417","url":null,"abstract":"<div><div>Psoriasis is a chronic inflammatory skin disease that significantly affects patients’ quality of life (QoL), as measured by the Dermatology Life Quality Index (DLQI). This study employs penalized regression and machine learning (ML) techniques to develop predictive models for DLQI in psoriasis patients. Using a dataset of 149 Thai patients, 16 models including multiple linear regression (MLR), five penalized regression models, five Random Forest (RF) models, and five Support Vector Regression (SVR) models were trained. Feature selection was performed using ridge, LASSO, adaptive LASSO, elastic net, and adaptive elastic net to optimize predictive accuracy and interpretability. Results indicate that RF-L1L2, a Random Forest model trained on elastic net-selected features, achieved the best performance with the lowest Root Mean Square Error (RMSE) of 5.6344, and lowest Mean Absolute Pencentage Error (MAPE) of 35.5404, outperforming traditional regression models. Bland–Altman analysis further confirmed the superiority of RF models in reducing systematic bias and improving predictive agreement. However, our findings should be interpreted with caution due to the limitations of small-sample size modeling. Key features included four psychological stress factors, age, Psoriasis Area and Severity Index (PASI), comorbidities and gender, reinforcing the interplay between physical and mental health. SHapley Additive exPlanations (SHAP) was employed in model explainability. Integrating ML models into clinical decision-making, can enhance patient stratification and personalized treatment strategies, with potential applications in AI-driven healthcare solutions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100417"},"PeriodicalIF":0.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095131","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}

引用次数: 0

A scalable methodology for optimizing hospital surgical schedules considering efficiency, flexibility, and improved patient outcomes 一种可扩展的方法，用于优化医院手术计划，考虑效率、灵活性和改善患者预后

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-03 DOI: 10.1016/j.health.2025.100413

Jiaqi Suo , Claudio Martani , Timothy B. Lescun , Cherri A. Krug

{"title":"A scalable methodology for optimizing hospital surgical schedules considering efficiency, flexibility, and improved patient outcomes","authors":"Jiaqi Suo , Claudio Martani , Timothy B. Lescun , Cherri A. Krug","doi":"10.1016/j.health.2025.100413","DOIUrl":"10.1016/j.health.2025.100413","url":null,"abstract":"<div><div>Hospitals face challenges in efficiently adapting treatment delivery to growing and changing demands. The main challenge arises from accommodating diverse patients requiring specific surgical resources and attention. Traditional scheduling methods often fail to address the dynamic nature of these environments, which are characterized by numerous uncertainties and stakeholders’ complex and changing needs. This study presents a novel methodology designed to enhance hospital operational efficiency while considering the interests of all stakeholders, including hospital administrators, medical staff (doctors, nurses, technicians), and patients. This requires a nuanced approach to effectively handle unpredictable treatment demands, resource availability, and patient requirements. The methodology systematically progresses from defining constraints and resources to modeling uncertainties generating and evaluating optimal schedules through iterative processes. This study develops and applies a 12-step method to optimize the surgery scheduling for the farm animal section of the Purdue Veterinary Hospital over a defined period. The application shows the practical benefits of the proposed approach by modeling dynamic surgical demands and exploring various scheduling possibilities within resource constraints. The results reveal that the proposed method effectively accommodates increased operational demands while managing delays, accidents, and illness costs.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100413"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047823","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}

引用次数: 0

An analytical framework for improving healthcare data management and organizational performance 用于改进医疗保健数据管理和组织绩效的分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-09-01 DOI: 10.1016/j.health.2025.100415

Yeneneh Tamirat Negash , Faradilah Hanum

{"title":"An analytical framework for improving healthcare data management and organizational performance","authors":"Yeneneh Tamirat Negash , Faradilah Hanum","doi":"10.1016/j.health.2025.100415","DOIUrl":"10.1016/j.health.2025.100415","url":null,"abstract":"<div><div>Digital healthcare relies on accurate, connected data to deliver safe and efficient patient care. Yet, fragmented management systems create data silos, limit interoperability, and delay clinical and administrative decisions. These conditions impede the promise of personalized, coordinated, and efficient care. Smart Product Service Systems (Smart PSS) integrate intelligent products, digital platforms, and value-added services, thereby providing a pathway to enhanced data management and improved patient care. Prior studies seldom identify or link the specific Smart PSS attributes that shape healthcare data management and organizational performance, particularly from a causal perspective. This study fills that gap by developing an analytical framework for improving healthcare data management and organizational performance. A literature review produced 47 candidate attributes. Thirty-three healthcare experts validated 27 attributes through the Fuzzy Delphi Method. Fuzzy Decision-Making Trial and Evaluation Laboratory then mapped the causal structure among the validated attributes and their associated aspects. Intelligent products, stakeholder collaboration, and service realization emerged as core causal aspects that influence data management and organizational performance. Smart repair, monitoring and early warning, synchronized transactions, information integration, data quality, and organizational readiness ranked as the most influential criteria for practice. By prioritizing these criteria, healthcare managers reduce data fragmentation and improve service outcomes. The study provides a hierarchical Smart PSS framework and managerial guidance for institutions advancing digital healthcare.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100415"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010070","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}

引用次数: 0