Healthcare analytics (New York, N.Y.)最新文献_第4页

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

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.

肝病是一项重大的全球健康挑战，需要准确和及时的诊断。本研究开发了一种新的深度学习模型，名为AFLID-Liver，以改进从医疗数据中对肝脏疾病的分类。AFLID-Liver模型集成了三种关键技术：专注于最相关数据特征的注意机制，处理潜在顺序信息的长短期记忆（LSTM）网络，以及有效处理数据集中不同疾病类别之间不平衡的焦点丢失。这种组合增强了模型学习复杂模式和做出可靠预测的能力。我们使用各种患者记录的数据集来评估AFLID-Liver，包括生物标志物和人口统计学。我们提出的模型取得了优异的性能，具有99.9%的准确度，99.9%的精度和99.9%的F-score，显著优于基线门控循环单元（GRU）模型（99.7%的准确度，97.9%的F-score）和现有的最先进的方法。这些结果证明了AFLID-Liver在高度精确的肝脏疾病检测方面的潜力。为了验证所提出模型的可泛化性，我们使用外部数据集进行交叉验证，该数据集也产生了良好的性能，描绘了所提出模型的潜力。新颖之处在于这些技术的协同整合，为临床决策支持和改善患者预后提供了强有力的方法。未来的研究将致力于提高计算效率，为其在实时临床应用中采用铺平道路。

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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

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.

超声剪切波弹性成像（SWE）是一种用于医学诊断的无创组织刚度测量技术。在SWE中，声辐射力在介质中产生横波（SW），其中横波速度（SWS）与介质刚度有关。传统的SWS估计技术在处理抖动和反射伪影时不具有抗噪声能力。本文提出了在时域和频域估计SWS的新技术。这些新方法利用的损失函数：(1)通过已知位置之间的横向信号位移来优化，(2)通过时间横向平面去噪的SW传播确定的邻域位移群位移来约束。所提出的约束优化是通过将相邻粒子的损失与高斯核耦合形成的，为中心粒子提供最佳到达时间，以增强局部刚度均匀性并使噪声恢复。显式去噪方案包括从时间横向平面中隔离SW剖面，创建参数化掩模。此外，通过横向插值提高重构分辨率，从而提高优化的可靠性。该方案在模拟（US-SWS-Digital-Phantoms）和三个实验幻影数据集上进行了评估：(i)梅奥诊所CIRS049模型，（ii） RSNA-QIBA-US-SWS, （iii）私人数据。对比了三种飞行时间法和两种频域法的约束优化性能。与经典方法相比，评估产生了视觉和数量上的优势和噪声鲁棒性重建。该方法具有质量好、误差小的特点，可用于组织健康检查和肿瘤诊断。

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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

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.

由于肿瘤的各种表现及其对患者健康的影响，脑肿瘤的诊断提出了一个重大挑战。传统的基于磁共振成像（MRI）的方法耗时、昂贵，并且高度依赖放射科医生的专业知识。自动化和可靠的分类技术对于提高诊断准确性、改善患者预后和确保及时检测至关重要。本研究引入RDXNet，这是一种集成了ResNet50、DenseNet121和Xception的混合深度学习模型，用于改进多类别脑肿瘤的分类。我们利用Br35H:: Brain Tumor Detection 2020、Figshare Brain Tumor Dataset和Radiopaedia MRI Scans三个公开可用的数据集，结合了胶质瘤、脑膜瘤、无肿瘤和垂体瘤四类7,023张MRI图像。在评估单个模型之后，我们使用特征融合和迁移学习将它们集成到RDXNet中。我们的模型达到了94%的准确率，超过了单个模型的性能并减轻了过拟合。为了验证稳健性，对多个数据分割进行K-Fold交叉验证。此外，采用基于grad - cam的可视化来增强可解释性，使临床医生能够理解模型的决策。使用混合深度学习技术，RDXNet显著提高了分类性能和可靠性。这项研究证明了人工智能（AI）驱动的计算机辅助诊断（CAD）系统在支持放射科医生、实现更快、更准确的脑肿瘤识别、最终改善患者预后方面的潜力。我们提出的混合模型RDXNet在多类别脑肿瘤分类中优于单个架构，实现了最先进的性能，并有助于更快，更可靠的自动化诊断。

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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

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.

医学成像（MI）通过提供解剖结构和病理状况的详细信息，支持准确的诊断和治疗计划，在医疗保健中发挥着至关重要的作用。无创模式，如x射线，磁共振成像（MRI），计算机断层扫描（CT）和超声（US），产生内部器官和组织的高分辨率图像。这些图像的有效解释依赖于对感兴趣区域（ROI）的精确分割，包括器官和病变。传统的基于人工特征提取的方法耗时长、不一致且不可扩展。本文探讨了人工智能（AI）驱动的分段技术的最新进展，重点关注卷积神经网络（CNN）架构，特别是U-Net家族及其变体——U-Net++和U-Net 3+。这些模型支持跨模态的自动、逐像素分类，并提高了分割的准确性和效率。这篇综述概述了U-Net体系结构的演变，它们的临床整合，并提供了一个模式明智的比较。它还解决了诸如数据异构、有限的通用性和模型可解释性等挑战，提出了包括注意力机制和基于转换器的设计在内的解决方案。强调临床适用性，这项工作弥合了算法开发和现实世界实现之间的差距。

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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

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.

本研究提出了一种名为EAGLE-Net的分层网络架构，用于识别上消化道内镜视频中的解剖标志。与传统的标记静态内窥镜图像解剖地标的技术不同，该方法旨在从上消化道视频中对地标进行分类。视频流经常受到许多噪声和污染物体的影响，这需要一种新的方法来解决这个问题。该方法采用分层网络结构，包括内镜图像质量评估和解剖地标分类两个阶段。在第一阶段，从胃肠道视频中保留高质量的帧。然后使用这些框架在十个解剖标志中识别特定位置。该方法提高了分层数据层之间的一致性。它集成了一个关注模块来加强特征连接，并利用新的分层交叉熵损失函数来优化模型性能。实验结果表明，该系统在两个分类阶段的平均准确率均达到93%以上。在临床实验中，解剖标志被自动标记，以帮助医生监测内镜检查过程。此外，所提出的方法为计算机辅助诊断应用程序的部署提供了一种潜在的解决方案，用于检测和治疗上消化道病变。

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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

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.

准确有效的脑肿瘤分割对于早期诊断、个性化治疗计划和提高生存率至关重要。脑肿瘤表现出复杂的空间和形态变化，使自动分割成为一项具有挑战性的任务。本研究引入了一种动态分割网络（DSNet），这是一种新的3D脑肿瘤分割框架，它集成了对抗学习、动态卷积神经网络（DCNN）和注意机制，以提高精度和鲁棒性。DSNet处理三维磁共振成像（MRI）体积，包括T1加权（T1）、T1加权对比度增强（T1ce）、T2加权（T2）和流体衰减反演恢复（FLAIR）模式，捕捉丰富的空间和背景特征。利用对抗训练，DSNet细化边界定义，而动态过滤器适应肿瘤特异性异质性，确保在不同情况下准确分割。注意机制进一步强调肿瘤相关区域，加强特征提取和边界划定。该模型在BraTS 2020数据集上进行了训练和验证，在全肿瘤（WT）、肿瘤核心（TC）和增强肿瘤（ET）区域上的骰子相似系数分别为0.959、0.975和0.947。通过对BraTS 2019和BraTS 2018数据集的评估，进一步证实了其通用性。此外，通过随机森林（RF）分类器，使用从分割图像中获得的体积特征来预测患者的总体生存率。为了提高可访问性，我们将分割和预测过程集成到一个用户友好的web应用程序中。DSNet优于最先进的方法，为具有强大临床潜力的3D脑肿瘤分割提供了强大而准确的解决方案。

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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

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.

目前的临床方法慢性疼痛评估缺乏客观，定量的措施，造成诊断准确性的关键差距。本文综述了慢性疼痛与体液中可检测的关键生物标志物，如谷氨酸、白细胞介素-6、一氧化氮和喹啉酸之间的关系。我们首先讨论了慢性疼痛的生物学机制，并评估了这些生物标志物的相关性。然后综述了用于监测这些生物标志物的非酶电化学生物传感器的最新进展。对于每个传感器，我们总结了性能指标，包括灵敏度、检测限和线性范围，同时强调了用于建立生物标志物水平和疼痛强度之间相关性的分析方法。我们的研究结果表明，生物标志物波动的定量分析可以加强慢性疼痛监测。基于传感器的生物标志物分析与临床工作流程的集成可能为个性化治疗计划和改善医疗保健供应链的决策提供途径。这篇综述强调需要继续发展高精度的生物传感器作为分析工具，将生理信号转化为临床可操作的疼痛指标。

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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

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.

银屑病是一种慢性炎症性皮肤病，通过皮肤病生活质量指数（DLQI）来衡量，银屑病显著影响患者的生活质量（QoL）。本研究采用惩罚回归和机器学习（ML）技术来开发银屑病患者DLQI的预测模型。使用149例泰国患者的数据集，训练了16个模型，包括多元线性回归（MLR）模型、5个惩罚回归模型、5个随机森林（RF）模型和5个支持向量回归（SVR）模型。采用脊线、LASSO、自适应LASSO、弹性网和自适应弹性网进行特征选择，优化预测精度和可解释性。结果表明，基于弹性网络选择特征训练的随机森林模型RF-L1L2表现最佳，其均方根误差（RMSE）最低为5.6344，平均绝对百分误差（MAPE）最低为35.5404，优于传统回归模型。Bland-Altman分析进一步证实了RF模型在减少系统偏差和提高预测一致性方面的优越性。然而，由于小样本量模型的局限性，我们的研究结果应该谨慎解释。主要特征包括年龄、银屑病面积和严重程度指数（PASI）、合并症和性别四种心理压力因素，强化了身心健康之间的相互作用。模型的可解释性采用SHapley加性解释（SHAP）。将ML模型集成到临床决策中，可以增强患者分层和个性化治疗策略，在人工智能驱动的医疗保健解决方案中具有潜在的应用前景。

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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

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.

医院在有效地适应不断增长和变化的需求方面面临着挑战。主要的挑战来自于适应不同的病人需要特定的手术资源和关注。传统的调度方法往往不能解决这些环境的动态性，这些环境具有大量的不确定性和利益相关者复杂多变的需求。本研究提出了一种新颖的方法，旨在提高医院的运营效率，同时考虑所有利益相关者的利益，包括医院管理者、医务人员（医生、护士、技术人员）和患者。这需要一种微妙的方法来有效地处理不可预测的治疗需求、资源可用性和患者需求。该方法系统地从定义约束和资源到建模不确定性，通过迭代过程生成和评估最优计划。本研究开发并应用了一种12步方法来优化普渡兽医医院农场动物科在规定时间内的手术安排。通过对动态手术需求建模和在资源约束下探索各种调度可能性，应用表明了所提出方法的实际效益。结果表明，所提出的方法在管理延误、事故和疾病成本的同时，有效地适应了不断增长的运营需求。

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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

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.

数字医疗保健依赖于准确、互联的数据来提供安全、高效的患者护理。然而，分散的管理系统造成了数据孤岛，限制了互操作性，并延迟了临床和行政决策。这些情况阻碍了个性化、协调和高效护理的实现。智能产品服务系统（Smart PSS）集成了智能产品、数字平台和增值服务，从而提供了增强数据管理和改善患者护理的途径。先前的研究很少确定或联系影响医疗数据管理和组织绩效的特定智能PSS属性，特别是从因果关系的角度来看。本研究通过开发用于改进医疗保健数据管理和组织绩效的分析框架来填补这一空白。一篇文献综述产生了47个候选属性。33位医疗专家通过模糊德尔菲法验证了27个属性。然后，模糊决策试验与评价实验室绘制了被验证属性及其相关方面之间的因果结构。智能产品、利益相关者协作和服务实现成为影响数据管理和组织绩效的核心因果方面。智能维修、监测和预警、同步交易、信息集成、数据质量和组织就绪度被列为最具影响力的实践标准。通过对这些标准进行优先排序，医疗保健管理人员可以减少数据碎片并改善服务结果。该研究为推进数字医疗的机构提供了分层智能PSS框架和管理指导。

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引用次数: 0