Pub Date : 2025-12-01Epub Date: 2025-08-11DOI: 10.1016/j.health.2025.100409
Deblina Mazumder Setu
The efficient and early detection of Autism Spectrum Disorder (ASD) is a critical objective in improving diagnosis and intervention outcomes. Various methods based on functional Magnetic Resonance Imaging (fMRI) and questionnaires have been explored, among which eye tracking is a promising approach. However, existing methods relying on eye tracking often restrict us to controlled environments, making things complicated and expensive. This study eliminates the requirement for specific parameters by concentrating just on eye movement data for ASD detection, therefore introducing a novel and user-friendly technique. Feature engineering is employed, encompassing preprocessing and extracting relevant gaze movement data. These properties are utilized in machine learning and deep learning model training with hyperparameter adjusting for optimization. Using the Saliency4ASD dataset and looking beyond its usual gaze focus, this study built a model that uses eye movement alone to identify ASD with about 81% accuracy. This safe, low-cost approach has the potential to provide simple technologies that enable early detection of ASD, hence allowing its accessibility to everyone.
{"title":"An analytics-driven model for identifying autism spectrum disorder using eye tracking","authors":"Deblina Mazumder Setu","doi":"10.1016/j.health.2025.100409","DOIUrl":"10.1016/j.health.2025.100409","url":null,"abstract":"<div><div>The efficient and early detection of Autism Spectrum Disorder (ASD) is a critical objective in improving diagnosis and intervention outcomes. Various methods based on functional Magnetic Resonance Imaging (fMRI) and questionnaires have been explored, among which eye tracking is a promising approach. However, existing methods relying on eye tracking often restrict us to controlled environments, making things complicated and expensive. This study eliminates the requirement for specific parameters by concentrating just on eye movement data for ASD detection, therefore introducing a novel and user-friendly technique. Feature engineering is employed, encompassing preprocessing and extracting relevant gaze movement data. These properties are utilized in machine learning and deep learning model training with hyperparameter adjusting for optimization. Using the Saliency4ASD dataset and looking beyond its usual gaze focus, this study built a model that uses eye movement alone to identify ASD with about 81% accuracy. This safe, low-cost approach has the potential to provide simple technologies that enable early detection of ASD, hence allowing its accessibility to everyone.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100409"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827735","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}
Pub Date : 2025-12-01Epub Date: 2025-08-04DOI: 10.1016/j.health.2025.100408
Shagufta Henna , Juan Miguel Lopez Alcaraz , Upaka Rathnayake , Mohamed Amjath
Convolutional Neural Networks (CNNs) are widely utilized for their robust feature extraction capabilities, particularly in medical classification tasks. However, their opaque decision-making process presents challenges in clinical settings, where interpretability and trust are paramount. This study investigates the explainability of a custom CNN model developed for Covid-19 and non-Covid-19 classification using dry cough spectrograms, with a focus on interpreting filter-level representations and decision pathways. To improve model transparency, we apply a suite of explainable artificial intelligence (XAI) techniques, including feature visualizations, SmoothGrad, Grad-CAM, and LIME, which explain the relevance of spectro-temporal features in the classification process. Furthermore, we conduct a comparative analysis with a pre-trained MobileNetV2 model using Guided Grad-CAM and Integrated Gradients. The results indicate that while MobileNetV2 yields some degree of visual attribution, its explanations, particularly for Covid-19 predictions are diffuse and inconsistent, limiting their interpretability. In contrast, the custom CNN model exhibits more coherent and class-specific activation patterns, offering improved localization of diagnostically relevant features.
{"title":"An interpretable deep learning framework for medical diagnosis using spectrogram analysis","authors":"Shagufta Henna , Juan Miguel Lopez Alcaraz , Upaka Rathnayake , Mohamed Amjath","doi":"10.1016/j.health.2025.100408","DOIUrl":"10.1016/j.health.2025.100408","url":null,"abstract":"<div><div>Convolutional Neural Networks (CNNs) are widely utilized for their robust feature extraction capabilities, particularly in medical classification tasks. However, their opaque decision-making process presents challenges in clinical settings, where interpretability and trust are paramount. This study investigates the explainability of a custom CNN model developed for Covid-19 and non-Covid-19 classification using dry cough spectrograms, with a focus on interpreting filter-level representations and decision pathways. To improve model transparency, we apply a suite of explainable artificial intelligence (XAI) techniques, including feature visualizations, SmoothGrad, Grad-CAM, and LIME, which explain the relevance of spectro-temporal features in the classification process. Furthermore, we conduct a comparative analysis with a pre-trained MobileNetV2 model using Guided Grad-CAM and Integrated Gradients. The results indicate that while MobileNetV2 yields some degree of visual attribution, its explanations, particularly for Covid-19 predictions are diffuse and inconsistent, limiting their interpretability. In contrast, the custom CNN model exhibits more coherent and class-specific activation patterns, offering improved localization of diagnostically relevant features.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100408"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766925","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}
Pub Date : 2025-12-01Epub Date: 2025-10-10DOI: 10.1016/j.health.2025.100422
Gazi Mohammad Imdadul Alam , Tapu Biswas , Sharia Arfin Tanim , M.F. Mridha
Diabetes is a chronic metabolic disorder that heightens the risk of complications for women and presents diagnostic challenges owing to imbalanced datasets and the need for interpretable predictive models. In this study, we propose a 1D Convolutional Neural Network (1D CNN) model that achieves an accuracy of 98.61% on German Patient Dataset, comprising 2,000 samples, and 99.35% on the Bangladeshi Patient Dataset, which includes 465 samples. Our model effectively addresses class imbalance by integrating the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE-ENN), which significantly enhances performance. Additionally, we conducted a statistical comparison with Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, demonstrating our CNN’s superior accuracy while maintaining reduced complexity and enhanced transparency through the integration of SHapley Additive exPlanations (SHAP). Our SHAP analysis revealed significant variations in feature importance between the two populations, offering culturally relevant insights into the risk factors for diabetes. The SHAP analysis not only facilitates interpretability by allowing healthcare professionals to understand the influence of individual features but also emphasizes the cultural context of diabetes risk. Overall, our findings surpass existing methodologies in terms of accuracy and complexity while underscoring the critical need for demographic diversity in predictive healthcare models, paving the way for more effective diabetes prediction strategies.
{"title":"An explainable analytics framework for predicting diabetes in women using Convolutional Neural Networks","authors":"Gazi Mohammad Imdadul Alam , Tapu Biswas , Sharia Arfin Tanim , M.F. Mridha","doi":"10.1016/j.health.2025.100422","DOIUrl":"10.1016/j.health.2025.100422","url":null,"abstract":"<div><div>Diabetes is a chronic metabolic disorder that heightens the risk of complications for women and presents diagnostic challenges owing to imbalanced datasets and the need for interpretable predictive models. In this study, we propose a 1D Convolutional Neural Network (1D CNN) model that achieves an accuracy of 98.61% on German Patient Dataset, comprising 2,000 samples, and 99.35% on the Bangladeshi Patient Dataset, which includes 465 samples. Our model effectively addresses class imbalance by integrating the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE-ENN), which significantly enhances performance. Additionally, we conducted a statistical comparison with Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, demonstrating our CNN’s superior accuracy while maintaining reduced complexity and enhanced transparency through the integration of SHapley Additive exPlanations (SHAP). Our SHAP analysis revealed significant variations in feature importance between the two populations, offering culturally relevant insights into the risk factors for diabetes. The SHAP analysis not only facilitates interpretability by allowing healthcare professionals to understand the influence of individual features but also emphasizes the cultural context of diabetes risk. Overall, our findings surpass existing methodologies in terms of accuracy and complexity while underscoring the critical need for demographic diversity in predictive healthcare models, paving the way for more effective diabetes prediction strategies.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100422"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320222","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}
This systematic review explores the advances, technologies, and applications of deep learning in spinal cord magnetic resonance imaging (MRI). The current state of deep-learning techniques used for injury detection, disease diagnosis, and treatment planning in spinal cord imaging is thoroughly examined. This review includes a systematic analysis of over 100 studies from 2018 to 2025, selected based on clinical relevance, model performance, and innovation. Through a comprehensive analysis of recent literature, this review highlights the evolution and effectiveness of various deep-learning models in enhancing the accuracy and reliability of spinal cord MRI interpretations. Significant contributions of this review include identifying the most effective and innovative deep-learning approaches, such as Convolutional Neural Networks (CNNs) for precise lesion segmentation and Generative Adversarial Networks (GANs) for data augmentation. Additionally, it synthesizes current applications, such as improved injury detection and multiple sclerosis diagnosis, and explores deep-learning’s role in treatment planning. The review also addresses the challenges and limitations faced in this domain, including data scarcity, model interpretability, and computational demands, and proposes potential solutions and directions for future research. By offering these insights, this review provides a unique perspective on integrating deep-learning models into clinical workflows and their impact on clinical outcomes and patient care.
{"title":"An in-depth review and analysis of deep learning methods and applications in spinal cord imaging","authors":"Md Sabbir Hossain , Mostafijur Rahman , Mumtahina Ahmed , Ashifur Rahman , Md Mohsin Kabir , M.F. Mridha , Jungpil Shin","doi":"10.1016/j.health.2025.100429","DOIUrl":"10.1016/j.health.2025.100429","url":null,"abstract":"<div><div>This systematic review explores the advances, technologies, and applications of deep learning in spinal cord magnetic resonance imaging (MRI). The current state of deep-learning techniques used for injury detection, disease diagnosis, and treatment planning in spinal cord imaging is thoroughly examined. This review includes a systematic analysis of over 100 studies from 2018 to 2025, selected based on clinical relevance, model performance, and innovation. Through a comprehensive analysis of recent literature, this review highlights the evolution and effectiveness of various deep-learning models in enhancing the accuracy and reliability of spinal cord MRI interpretations. Significant contributions of this review include identifying the most effective and innovative deep-learning approaches, such as Convolutional Neural Networks (CNNs) for precise lesion segmentation and Generative Adversarial Networks (GANs) for data augmentation. Additionally, it synthesizes current applications, such as improved injury detection and multiple sclerosis diagnosis, and explores deep-learning’s role in treatment planning. The review also addresses the challenges and limitations faced in this domain, including data scarcity, model interpretability, and computational demands, and proposes potential solutions and directions for future research. By offering these insights, this review provides a unique perspective on integrating deep-learning models into clinical workflows and their impact on clinical outcomes and patient care.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100429"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415989","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}
Pub Date : 2025-12-01Epub Date: 2025-11-27DOI: 10.1016/j.health.2025.100439
Roaa Soloh , Lara Abou Orm , Dana Dabdoub
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition whose early detection is crucial for improving social and cognitive outcomes. Current diagnostic tools are often costly, subjective, and inaccessible to many clinics. This work presents GazeScan, an image-based analytics framework that identifies ASD from eye-tracking behavior using only standard video input. The system non-invasively performs gaze estimation via a 16-point geometric calibration and transforms gaze trajectories into grayscale scanpath images. These images are classified using a lightweight convolutional neural network. GazeScan was evaluated on the Eye-Tracking Scan Path (ETSP) dataset with five-fold cross-validation, achieving 97.01% accuracy and an AUC of 0.98. The model’s compact architecture enables real-time inference and mobile deployment without specialized hardware. The results obtained highlight the potential of accessible, AI-enabled digital screening tools to support early ASD detection and broader behavioral healthcare delivery.
{"title":"An image-based analytics framework for early autism detection using eye movements","authors":"Roaa Soloh , Lara Abou Orm , Dana Dabdoub","doi":"10.1016/j.health.2025.100439","DOIUrl":"10.1016/j.health.2025.100439","url":null,"abstract":"<div><div>Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition whose early detection is crucial for improving social and cognitive outcomes. Current diagnostic tools are often costly, subjective, and inaccessible to many clinics. This work presents GazeScan, an image-based analytics framework that identifies ASD from eye-tracking behavior using only standard video input. The system non-invasively performs gaze estimation via a 16-point geometric calibration and transforms gaze trajectories into grayscale scanpath images. These images are classified using a lightweight convolutional neural network. GazeScan was evaluated on the Eye-Tracking Scan Path (ETSP) dataset with five-fold cross-validation, achieving 97.01% accuracy and an AUC of 0.98. The model’s compact architecture enables real-time inference and mobile deployment without specialized hardware. The results obtained highlight the potential of accessible, AI-enabled digital screening tools to support early ASD detection and broader behavioral healthcare delivery.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100439"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617927","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}
Pub Date : 2025-12-01Epub Date: 2025-10-19DOI: 10.1016/j.health.2025.100426
Félicien Hêche , Philipp Schiller , Oussama Barakat , Thibaut Desmettre , Stephan Robert-Nicoud
This study investigates the impact of 19 external factors, related to weather, road traffic conditions, air quality, and time, on the hourly occurrence of emergencies. The analysis relies on six years of dispatch records (2015–2021) from the Centre Hospitalier Universitaire Vaudois (CHUV), which oversees 18 ambulance stations across the French-speaking region of Switzerland. First, classical statistical methods, including Chi-squared test, Student’s -test, and information value, are employed to identify dependencies between the occurrence of emergencies and the considered parameters. Additionally, SHapley Additive exPlanations (SHAP) values and permutation importance are computed using eXtreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) models. Training and hyperparameter optimization were performed on data from 2015–2020, while the 2021 data were held out for evaluation and for computing model interpretation metrics. Results indicate that temporal features – particularly the hour of the day – are the dominant drivers of emergency occurrences, whereas other external factors contribute minimally once temporal effects are accounted for. Subsequently, performance comparisons with a simplified model that considers only the hour of the day suggest that more complex machine learning approaches offer limited added value in this context. Operationally, this result supports the use of simple time-dependent demand curves for EMS planning. Such models can effectively guide staffing schedules and relocations without the overhead of integrating external data or maintaining complex pipelines. By highlighting the limited utility of external predictors, this study provides practical guidance for EMS organizations seeking efficient, data-driven resource allocation methods.
{"title":"An analytical study of external factors influencing emergency occurrences in healthcare","authors":"Félicien Hêche , Philipp Schiller , Oussama Barakat , Thibaut Desmettre , Stephan Robert-Nicoud","doi":"10.1016/j.health.2025.100426","DOIUrl":"10.1016/j.health.2025.100426","url":null,"abstract":"<div><div>This study investigates the impact of 19 external factors, related to weather, road traffic conditions, air quality, and time, on the hourly occurrence of emergencies. The analysis relies on six years of dispatch records (2015–2021) from the Centre Hospitalier Universitaire Vaudois (CHUV), which oversees 18 ambulance stations across the French-speaking region of Switzerland. First, classical statistical methods, including Chi-squared test, Student’s <span><math><mi>t</mi></math></span>-test, and information value, are employed to identify dependencies between the occurrence of emergencies and the considered parameters. Additionally, SHapley Additive exPlanations (SHAP) values and permutation importance are computed using eXtreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) models. Training and hyperparameter optimization were performed on data from 2015–2020, while the 2021 data were held out for evaluation and for computing model interpretation metrics. Results indicate that temporal features – particularly the hour of the day – are the dominant drivers of emergency occurrences, whereas other external factors contribute minimally once temporal effects are accounted for. Subsequently, performance comparisons with a simplified model that considers only the hour of the day suggest that more complex machine learning approaches offer limited added value in this context. Operationally, this result supports the use of simple time-dependent demand curves for EMS planning. Such models can effectively guide staffing schedules and relocations without the overhead of integrating external data or maintaining complex pipelines. By highlighting the limited utility of external predictors, this study provides practical guidance for EMS organizations seeking efficient, data-driven resource allocation methods.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100426"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361876","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}
Dengue fever remains a major global health concern that demands rapid and accurate diagnosis to prevent severe complications and support timely patient care. Traditional approaches relying on environmental variables often lack patient-level precision, limiting their clinical applicability. This study focuses on hematological parameters as more reliable indicators for early dengue detection. A novel machine learning framework, DengueStackX-19, was developed using 1,523 clinically verified patient records from Jamalpur 250-Bedded General Hospital, Jamalpur, Bangladesh. The dataset underwent rigorous preprocessing, normalization, and imbalance handling using various resampling techniques. Comparative evaluation across five balancing methods demonstrated that DengueStackX-19 consistently achieved the highest accuracy and robustness, performing effectively both before and after outlier removal. The model achieved 93.65 % accuracy and 89.63 % F1 during 10-fold cross-validation under SMOTEENN, and further attained 96.38 % accuracy and 94.20 % F1 in dengue classification, demonstrating robust generalization and consistent high performance across evaluation phases. Sensitivity analysis further verified its stability under feature perturbations. To ensure interpretability, SHAP and LIME were applied to identify the hematological factors most influential to the model's predictions, and the resulting patterns aligned with established clinical understanding. The model was deployed as an accessible web-based diagnostic tool, allowing healthcare professionals to perform real-time dengue detection without specialized laboratory infrastructure. This study demonstrates that hematology-driven AI models can significantly enhance diagnostic accuracy, reduce decision-making time, and improve patient outcomes, particularly in resource-limited settings.
{"title":"An interpretable machine learning model for dengue detection with clinical hematological data","authors":"Izaz Ahmmed Tuhin , A.K.M.Fazlul Kobir Siam , Md Mahfuzur Rahman Shanto , Md Rajib Mia , Imran Mahmud , Apurba Ghosh","doi":"10.1016/j.health.2025.100430","DOIUrl":"10.1016/j.health.2025.100430","url":null,"abstract":"<div><div>Dengue fever remains a major global health concern that demands rapid and accurate diagnosis to prevent severe complications and support timely patient care. Traditional approaches relying on environmental variables often lack patient-level precision, limiting their clinical applicability. This study focuses on hematological parameters as more reliable indicators for early dengue detection. A novel machine learning framework, DengueStackX-19, was developed using 1,523 clinically verified patient records from Jamalpur 250-Bedded General Hospital, Jamalpur, Bangladesh. The dataset underwent rigorous preprocessing, normalization, and imbalance handling using various resampling techniques. Comparative evaluation across five balancing methods demonstrated that DengueStackX-19 consistently achieved the highest accuracy and robustness, performing effectively both before and after outlier removal. The model achieved 93.65 % accuracy and 89.63 % F1 during 10-fold cross-validation under SMOTEENN, and further attained 96.38 % accuracy and 94.20 % F1 in dengue classification, demonstrating robust generalization and consistent high performance across evaluation phases. Sensitivity analysis further verified its stability under feature perturbations. To ensure interpretability, SHAP and LIME were applied to identify the hematological factors most influential to the model's predictions, and the resulting patterns aligned with established clinical understanding. The model was deployed as an accessible web-based diagnostic tool, allowing healthcare professionals to perform real-time dengue detection without specialized laboratory infrastructure. This study demonstrates that hematology-driven AI models can significantly enhance diagnostic accuracy, reduce decision-making time, and improve patient outcomes, particularly in resource-limited settings.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100430"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465963","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}
Pub Date : 2025-12-01Epub Date: 2025-11-20DOI: 10.1016/j.health.2025.100438
Olga Bountali , Sila Cetinkaya , Michael Hahsler , Farnaz Nourbakhsh , Zhenghang Xu , Henry Quinones
This study uses advanced analytics to investigate the treatment barriers faced by unfunded patients suffering from end-stage kidney disease at Parkland Hospital. Under the Emergency Medical Treatment and Labor Act (EMTALA) federal law, these patients can receive dialysis only under emergency conditions. This practice, commonly known as “emergent dialysis,” routes patients through the Emergency Room (ER) for a screening assessment to determine whether they will be accepted for treatment. Utilizing a data set from Parkland Hospital on patient ER visits seeking emergent dialysis, we leverage descriptive analytics and statistical methods to investigate (i) the impact of this accept/reject decision process on patient outcomes and (ii) the potential influence of operational, medical, and behavioral factors, such as the ER load, patient acuity level, and accept/reject patient history on it. Our research highlights an unanticipated burden caused by a subset of occasional dialysis patients with notably infrequent visits—the aspect that should not be overlooked. It also pinpoints discrepancies across patients, e.g., counterintuitively, patients accepted for treatment experienced shorter wait times before the decision was made than those rejected. More importantly, our work reveals that operational and behavioral factors influence the decision-making process substantially, much more than medical ones. The above findings underscore the critical role of analytics in our model. Our work further employs prescriptive analytics and simulation optimization approaches to provide recommendations on how policymakers can leverage the insights above to make more effective decisions that improve care delivery for this vulnerable population.
{"title":"An investigation of treatment barriers for End-Stage Kidney Disease patients using advanced analytics","authors":"Olga Bountali , Sila Cetinkaya , Michael Hahsler , Farnaz Nourbakhsh , Zhenghang Xu , Henry Quinones","doi":"10.1016/j.health.2025.100438","DOIUrl":"10.1016/j.health.2025.100438","url":null,"abstract":"<div><div>This study uses advanced analytics to investigate the treatment barriers faced by unfunded patients suffering from end-stage kidney disease at Parkland Hospital. Under the Emergency Medical Treatment and Labor Act (EMTALA) federal law, these patients can receive dialysis only under emergency conditions. This practice, commonly known as “emergent dialysis,” routes patients through the Emergency Room (ER) for a screening assessment to determine whether they will be accepted for treatment. Utilizing a data set from Parkland Hospital on patient ER visits seeking emergent dialysis, we leverage descriptive analytics and statistical methods to investigate (i) the impact of this accept/reject decision process on patient outcomes and (ii) the potential influence of operational, medical, and behavioral factors, such as the ER load, patient acuity level, and accept/reject patient history on it. Our research highlights an unanticipated burden caused by a subset of occasional dialysis patients with notably infrequent visits—the aspect that should not be overlooked. It also pinpoints discrepancies across patients, e.g., counterintuitively, patients accepted for treatment experienced shorter wait times before the decision was made than those rejected. More importantly, our work reveals that operational and behavioral factors influence the decision-making process substantially, much more than medical ones. The above findings underscore the critical role of analytics in our model. Our work further employs prescriptive analytics and simulation optimization approaches to provide recommendations on how policymakers can leverage the insights above to make more effective decisions that improve care delivery for this vulnerable population.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100438"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617926","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}
Pub Date : 2025-12-01Epub Date: 2025-09-03DOI: 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.
{"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":"2025-12-01","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}
Pub Date : 2025-12-01Epub Date: 2025-10-17DOI: 10.1016/j.health.2025.100425
Marzieh Amiri Shahbazi , Mohammad Abdullah Al-Mamun , Todd Brothers , Imtiaz Ahmed
Identifying meaningful patient phenotypes is a cornerstone of data-driven healthcare, enabling risk stratification, resource allocation, and the design of personalized care strategies. Achieving this requires robust analytical methods that can uncover hidden structure in high-dimensional clinical data while ensuring stability and interpretability of results. In this study, we present a machine learning framework for phenotypic clustering that combines partition-based (-means) and probabilistic (latent class analysis, LCA) approaches. By comparing subgroup assignments across these complementary methods, the framework provides an internal validation of clustering assignments. Rather than relying on a single method, the framework validates subgroup assignments through cross-method agreement, strengthening confidence in the robustness of the identified phenotypes and their utility for decision support. We apply the proposed framework to patients with chronic kidney disease (CKD) stratified by prior history of acute kidney injury (AKI), illustrating its value in uncovering population-level heterogeneity. While the mechanisms linking AKI to CKD phenotypic patterns remain poorly understood historically, this study investigates CKD trajectories in patients with and without prior AKI and identifies key phenotypic patterns. The analysis revealed consistent phenotypic structures, with over 80% agreement between the two clustering approaches. Distinct phenotypic patterns emerged between the AKI and non-AKI cohorts, with cardiovascular conditions consistently dominating in both groups. These findings demonstrate how stratified clustering can uncover risk signatures that traditional CKD staging systems may overlook. By combining complementary clustering algorithms, the framework strengthens the analytic foundation of phenotyping studies. Moreover, it enables the design of phenotype specific care pathways such as cluster aware monitoring panels and tailored coordination strategies, thus underscoring the broader potential of data-driven analytics to advance personalized medicine and healthcare decision support.
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