Healthcare analytics (New York, N.Y.)最新文献_第3页

An open-source application for obtaining retrospective and prospective insights into overall hospital quality star ratings

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100371

Kenneth J. Locey, Brian D. Stein, Ryan Schipfer, Brittnie Dotson, Leslie Klemp

{"title":"An open-source application for obtaining retrospective and prospective insights into overall hospital quality star ratings","authors":"Kenneth J. Locey, Brian D. Stein, Ryan Schipfer, Brittnie Dotson, Leslie Klemp","doi":"10.1016/j.health.2024.100371","DOIUrl":"10.1016/j.health.2024.100371","url":null,"abstract":"<div><div>Overall Hospital Quality Star Ratings (overall star ratings) are designed to assist healthcare consumers by summarizing dozens of hospital quality measures. These ratings are also used by hospitals to direct quality improvements and are often used in healthcare research. However, no analytical tools have been developed to provide insights into the data, measures, and scores of the overall star rating system. To this end, we developed a novel open-source application to provide retrospective insights, prospective estimates, and research-ready data. Users can 1) examine changes in hospital performance from 2021 onward, 2) recalculate overall star ratings based on hypothetical improvements, 3) download data for all hospitals included in the overall star rating system since 2021, and 4) obtain prospective estimates based on the overall star rating methodology and its data source (Care Compare). We demonstrate 99.6% accuracy when estimating overall star ratings six months prior to public release. Estimates of whether hospitals will retain their star rating are up to 90% accurate a year before public release. We discuss the use of our application in healthcare research and the potential for similar tools to be developed for other hospital rating and ranking systems.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100371"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100372

Kuan-Chen Chen , Hsiang-An Yu , Ming-Miin Yu

{"title":"A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine","authors":"Kuan-Chen Chen , Hsiang-An Yu , Ming-Miin Yu","doi":"10.1016/j.health.2024.100372","DOIUrl":"10.1016/j.health.2024.100372","url":null,"abstract":"<div><div>This study assesses changes in resource productivity in traditional Chinese medicine (TCM) system across Taiwanese counties and cities from 2016 to 2019, stratifying the analysis by population densities. Employing a data envelopment analysis (DEA) metafrontier Malmquist productivity index model, this research relaxes Hicks' neutrality assumption of technical change, allowing for the measurement of biased technological change and technical gap ratio changes. The empirical findings reveal a decline in TCM system productivity, primarily attributed to reduced technological advancements. Notably, higher productivity changes were observed in counties and cities with lower population densities, contrasting with those having higher population densities, where productivity changes were limited. The results suggest that areas with lower population densities hold significant potential for technological enhancement, as evidenced by intergroup technology updates and technological leadership indices. Furthermore, the estimates of productivity change and technological bias underscore the inadequacy of assuming Hicks’ neutral technological change for analyzing TCM system productivity in Taiwan. These findings highlight the need for improved TCM system technology and innovation within the healthcare system to address the urban-rural gap effectively.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100372"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence and diagnostic healthcare using computer vision and medical imaging

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100352

Gaurav Dhiman, Wattana Viriyasitavat, Atulya K. Nagar, Oscar Castillo

引用次数: 0

Machine learning for smart health and distributed biomedical services

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100363

Chinmay Chakraborty, Saïd Mahmoudi, Guangjie Han, Rubén González Crespo

引用次数: 0

A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation 用于心电图分类和心房颤动诊断的带频谱相关函数的深度神经网络模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-11-23 DOI: 10.1016/j.health.2024.100370

Sara Mihandoost

{"title":"A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation","authors":"Sara Mihandoost","doi":"10.1016/j.health.2024.100370","DOIUrl":"10.1016/j.health.2024.100370","url":null,"abstract":"<div><div>Atrial Fibrillation (AF) is a common type of irregular heartbeat, and early detection can significantly improve treatment outcomes and prognoses. Single-lead Electrocardiogram (ECG) devices are under extensive scrutiny for monitoring patients' heart health worldwide. Standardized ECG signal monitoring has demonstrated a significant reduction in mortality rates associated with severe cardiovascular diseases. However, the automatic detection method for AF requires significant improvement. This study presents a novel approach that utilizes the cyclostationary analysis of ECG signals, uncovering a spectral hidden periodicity between the QRS-T (the main wave components representing electrical activity in the heart) complexes of the ECG signal through the Spectral Correlation Function (SCF). To validate the proposed method's performance, the single ECG's SCF coefficients are applied to the Convolutional Recurrent Neural Network (CRNN), which consists of convolutional and long short-term memory (LSTM) layers, on the 2017 PhysioNet challenge dataset. The obtained results demonstrate that the proposed approach efficiently represents ECG signals through SCF coefficients, leading to the accurate detection of AF with an average accuracy of 92.76% and an average F1-score of 89.1%.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100370"},"PeriodicalIF":0.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images 利用脑计算机断层扫描图像预测脑中风的集合卷积神经网络模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-10-29 DOI: 10.1016/j.health.2024.100368

Most. Jannatul Ferdous, Rifat Shahriyar

{"title":"An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images","authors":"Most. Jannatul Ferdous, Rifat Shahriyar","doi":"10.1016/j.health.2024.100368","DOIUrl":"10.1016/j.health.2024.100368","url":null,"abstract":"<div><div>A stroke is a potentially fatal brain attack that causes an interruption in the blood supply to the brain. As a result, brain cells start to die due to a lack of oxygen and nutrients. After a stroke, every minute is critical. A million or more brain cells perish every minute during a stroke. The prompt identification of a stroke can prevent lasting brain damage or even save the patient’s life. Doctors advise computed tomography (CT) images of the brain for earlier stroke detection. If doctors delay CT diagnosis or may make erroneous diagnoses, this can be life-threatening. For that reason, an automatic diagnosis of stroke from a brain CT scan image will be beneficial for stroke patients. This study moderates three pre-trained convolutional neural network (CNN) models named Inceptionv3, MobileNetv2, and Xception by updating the top layer of those models using the transfer-learning technique based on CT images of the brain. A new ensemble convolutional neural network (ENSNET) model is proposed for automatic brain stroke prediction from brain CT scan images. ENSNET is the average of two improved CNN models named InceptionV3 and Xception. We have relied on the following metrics: accuracy, precision, recall, f1-score, confusion matrix, accuracy versus epoch, loss versus epoch, and the receiver operating characteristic (ROC) curve to assess performance matrices. The accuracy of the moderated Inceptionv3 is 97.48%, the moderated MobileNetv2 is 83.29%, and the moderated Xception is 96.11%. Nonetheless, the suggested ensemble model ENSNET performs better than the other models when it comes to the diagnosis of stroke from brain CT scans, providing 98.86% accuracy, 97.71% precision, 98.46% recall, 98.08% f1-score, and 98.74% area under the ROC curve(AUC). Therefore, the proposed model ENSNET can detect strokes from computed tomography images of the brain more successfully than other models.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100368"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hierarchical Bayesian approach for identifying socioeconomic factors influencing self-rated health in Japan 用分层贝叶斯方法确定影响日本自我健康评价的社会经济因素

Healthcare analytics (New York, N.Y.) Pub Date : 2024-10-25 DOI: 10.1016/j.health.2024.100367

Makoto Nakakita , Teruo Nakatsuma

{"title":"A hierarchical Bayesian approach for identifying socioeconomic factors influencing self-rated health in Japan","authors":"Makoto Nakakita , Teruo Nakatsuma","doi":"10.1016/j.health.2024.100367","DOIUrl":"10.1016/j.health.2024.100367","url":null,"abstract":"<div><div>This study identifies socioeconomic factors that potentially influence self-rated health (SRH), an important indicator of health status, in the Japanese population. We used a panel data logit model to simultaneously estimate the effects of personal attributes, living environment, and social conditions. To achieve a stable estimation of the panel data logit model, we applied hierarchical Bayesian modeling and the Markov Chain Monte Carlo (MCMC) method to obtain its estimation. Furthermore, we used the ancillary-sufficiency interweaving strategy (ASIS) algorithm to improve the efficiency of the MCMC method for the panel data logit model. The results indicate that SRH within the Japanese population is affected by demographic and socioeconomic factors (e.g., age, marital status, educational background, and employment status) and daily habits such as frequency of drinking alcohol. We also obtained results that differed from previous studies in the research literature. Differences in the national character among countries may be reflected in these results. Since SRH is a subjective measure of health status and often differs from actual health status, it is crucial to remove the influences of the national character on SRH in evaluating the actual health status of individuals within a population. The study findings provide important insights into addressing these factors to understand SRH in the Japanese context better.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100367"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An electrocardiogram signal classification using a hybrid machine learning and deep learning approach 利用机器学习和深度学习混合方法进行心电图信号分类

Healthcare analytics (New York, N.Y.) Pub Date : 2024-10-09 DOI: 10.1016/j.health.2024.100366

Faramarz Zabihi , Fatemeh Safara , Behrouz Ahadzadeh

{"title":"An electrocardiogram signal classification using a hybrid machine learning and deep learning approach","authors":"Faramarz Zabihi , Fatemeh Safara , Behrouz Ahadzadeh","doi":"10.1016/j.health.2024.100366","DOIUrl":"10.1016/j.health.2024.100366","url":null,"abstract":"<div><div>An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100366"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An inter-hospital performance assessment model for evaluating hospitals performing hip arthroplasty 用于评估髋关节置换术医院的医院间绩效评估模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-09-24 DOI: 10.1016/j.health.2024.100365

Fabian Dehanne , Magali Pirson , Etienne Cuvelier , Frédéric Bielen , Pol Leclercq , Benoît Libert , Maximilien Gourdin

{"title":"An inter-hospital performance assessment model for evaluating hospitals performing hip arthroplasty","authors":"Fabian Dehanne , Magali Pirson , Etienne Cuvelier , Frédéric Bielen , Pol Leclercq , Benoît Libert , Maximilien Gourdin","doi":"10.1016/j.health.2024.100365","DOIUrl":"10.1016/j.health.2024.100365","url":null,"abstract":"<div><div>The value of hospital care to patients is expressed as a combination of reduced healthcare costs, fewer medical complications, and improved patient satisfaction. Few studies highlight the value hospitals provide to their patients through hip replacement surgery.</div><div>This study aims to define a methodology for inter-hospital comparison purposes that can assess the value of hip replacement management to patients by using indicators of costs, medical complications, and patient outcomes.</div><div>We identified medical complications and costs from medico-administrative data collected by three hospitals. We associated a Disability Adjusted Life Years (DALYs) impact with medical complications, readmissions (within 30 days), and hospital mortality. Costs were analysed from a social security perspective. Patient outcomes were collected through a questionnaire-based survey after hip surgery. To compare the three hospitals, we created a composite indicator by standardizing each dependent variable and combining a weighting of importance provided by patients.</div><div>This study analysed 342 hospital stays. The mean (standard deviation) number of DALYs per stay was estimated to be more than 0.0028 (0.016) for a mean (standard deviation) cost of €4,834 (€3,665). The composite indicator allowed hospitals to be ranked and areas for improvement to be identified. In our case mix, Hospital 3 is the lowest-ranked hospital, with excessively high costs and a relatively low level of satisfaction compared to the others.</div><div>The simultaneous evaluation of medical complications, patient outcomes, and costs is a prerequisite for quality improvement efforts by managers and practitioners. In our opinion, this experiment, which sought to estimate the value hospitals bring to patients, may be viewed as the first step towards value-based purchasing in Belgium.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100365"},"PeriodicalIF":0.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data envelopment analysis model for optimizing transfer time of ischemic stroke patients under endovascular thrombectomy 优化血管内血栓切除术下缺血性脑卒中患者转院时间的数据包络分析模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-09-19 DOI: 10.1016/j.health.2024.100364

Mirpouya Mirmozaffari, Noreen Kamal

{"title":"A data envelopment analysis model for optimizing transfer time of ischemic stroke patients under endovascular thrombectomy","authors":"Mirpouya Mirmozaffari, Noreen Kamal","doi":"10.1016/j.health.2024.100364","DOIUrl":"10.1016/j.health.2024.100364","url":null,"abstract":"<div><div>This study applies Data Envelopment Analysis (DEA) to optimize transfer times and futile transfers of eligible ischemic stroke patients receiving Endovascular Thrombosis (EVT) in Primary Stroke Centers (PSC) in Nova Scotia. The study aims to assess healthcare delivery in Nova Scotia over two periods. It seeks to improve stroke care for rural populations by examining nine inputs, including age and distance between PSCs and the Comprehensive Stroke Centre (CSC) that provided EVT treatment, concerning a single output variable: whether EVT is performed or not. In the first phase, 115 patients were treated as Decision-Making Units (DMUs) for ten PSCs by applying an input-oriented Variable Returns to Scale (VRS) assisted by super-efficiency analysis using the Python-based PyDEA tool. This tool is known for its unrestricted capacity to handle DMUs, inputs, and outputs. In the second phase, eight PSCs with low patient numbers were merged into four DMUs, each consisting of two PSCs. These two merged PSCs have limited patients, and the selected PSCs are also geographically close. Two PSCs have been kept separate because they had sufficient patient volume. In the first phase, VRS generated more reasonable efficiency scores for evaluation, while in the second phase, Constant Returns to Scale (CRS) outperformed VRS, yielding better results. In the initial stage of the second phase, ten PSCs were considered as six DMUs using the input-oriented CRS and VRS for 115 patients. Super-efficiency measures were applied in this stage to improve the evaluation process further. In the second part of the second phase, a comparison between the first period (2018–2019) and the second period (2020–2021) was conducted using the Malmquist Productivity Index (MPI), considering CRS and VRS to evaluate the relative efficiency and productivity change of six DMUs over time.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100364"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0