{"title":"30-Days Same-Cause Congestive Heart Failure Readmission Rate at JHAH","authors":"Fatimah AlBeesh, Jalal Al Alwan","doi":"10.4018/ijpch.313195","DOIUrl":"https://doi.org/10.4018/ijpch.313195","url":null,"abstract":"Congestive heart failure attracts quality initiatives to address its high prevalence and massive impacts. It is a major global public health problem and burden on healthcare systems, especially in developing countries, and the most common cause of hospitalization and readmission among older patients, especially 30-day readmission. This article will share achievement in reducing CHF readmission rate and address and discuss interventions to improve patient quality of life and reduce re-hospitalization.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"10 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120891927","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}
Xiaoyan Jiang, Mackenzie Brown, Hei-Ran Cheong, Zuojin Hu
{"title":"COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm","authors":"Xiaoyan Jiang, Mackenzie Brown, Hei-Ran Cheong, Zuojin Hu","doi":"10.4018/ijpch.309951","DOIUrl":"https://doi.org/10.4018/ijpch.309951","url":null,"abstract":"COVID-19 is extremely contagious and has brought serious harm to the world. Many researchers are actively involved in the study of rapid and reliable diagnostic methods for COVID-19. The study proposes a novel approach to COVID-19 diagnosis. The multiple-distance gray-level co-occurrence matrix (MDGLCM) was used to analyze chest CT images, the GA algorithm was used as an optimizer, and the feedforward neural network was used as a classifier. The results of 10 runs of 10-fold cross-validation show that the proposed method has a sensitivity of 83.38±1.40, a specificity of 81.15±2.08, a precision of 81.59±1.57, an accuracy of 82.26±0.96, an F1-score of 82.46±0.88, an MCC of 64.57±1.90, and an FMI of 82.47±0.88. The proposed MDGLCM-GA-based COVID-19 diagnosis method outperforms the other six state-of-the-art methods.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"25 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125765544","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}
{"title":"Determinants of Service Quality in Healthcare","authors":"A. Ghildiyal, J. Devrari, Atul Dhyani","doi":"10.4018/ijpch.309117","DOIUrl":"https://doi.org/10.4018/ijpch.309117","url":null,"abstract":"Indian healthcare is described as the largest sector, both in revenue and employment. The quality of service—the characteristics that shape care experience beyond technical competence—is rarely discussed in the medical literature. This study reveals the determinants that affect the perception of quality of healthcare services from the patients' and service providers' points of view. A cross-sectional method was followed to determine the perception of quality of healthcare services and relating variables including infrastructure, reliability and responsiveness, empathy, affordability, and administration. The data collected from 400 respondents, including patients and service providers, for the study were analyzed using confirmatory factor analysis. Results confirmed that healthcare service quality aspects (i.e., physical environment, staff behavior, responsiveness, affordable services, admission process) positively relate to customers' perception. Findings will help the hospital managers articulate effective strategies to ensure superior quality of healthcare services to customers.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115193686","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}
{"title":"COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO","authors":"Jiaji Wang, Logan Graham","doi":"10.4018/ijpch.309118","DOIUrl":"https://doi.org/10.4018/ijpch.309118","url":null,"abstract":"Three years have passed since the sudden outbreak of COVID-19. From that year, the governments of various countries gradually lifted the measures to prevent and control the pandemic. But the number of new infections and deaths from novel coronavirus infections has not declined. So we still need to identify and research the COVID-19 virus to minimize the damage to society. In this paper, the authors use the gray level cooccurrence matrix for feature extraction and particle swarm optimization algorithm to find the optimal solution. After that, this method is validated by using the more common K fold cross validation. Finally, the results of the experimental data are compared with the more advanced methods. Experimental data show that this method achieves the initial expectation.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134058005","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}
{"title":"An Educational Solution-Driven Discussion About Racial Public Health Disparities During the COVID-19 Pandemic","authors":"Kiana S. Zanganeh, D. Burrell","doi":"10.4018/ijpch.309950","DOIUrl":"https://doi.org/10.4018/ijpch.309950","url":null,"abstract":"One of the most troubling aspects of the coronavirus disease (COVID-19) pandemic in the US is the disproportionate harm that it has caused to historically marginalized, low income, underserved, and uninsured groups. During the emergence of the pandemic, Black, Hispanic, and Asian people have markedly higher infection rates, hospitalization, and death compared with White people. Once infected with COVID-19, persons with lower incomes, underserved, and people of color are at greater risk for hospitalization because they often have more chronic medical comorbidities. The prevalence of hypertension, diabetes, and obesity are higher among low-income, minority populations, all of which can make a COVID-19 infection much worse. In addition, racial and ethnic minority populations are often underinsured and have inferior access to healthcare, which likely results in those infected seeking care later during their illness. This paper explores educational solution-driven discussion about racial public health disparities during the COVID-19 pandemic.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122117977","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}
{"title":"A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19","authors":"Jiaji Wang","doi":"10.4018/ijpch.311444","DOIUrl":"https://doi.org/10.4018/ijpch.311444","url":null,"abstract":"In 2019, the outbreak of a new coronavirus spread rapidly around the world. The use of medical image-assisted diagnosis for suspected patients can provide a more accurate and rapid picture of the disease. The earlier the diagnosis is made and the earlier the patient is treated, the lower the likelihood of virus transmission. This paper reviews current research advances in the processing of lung CT images in combination with promising deep learning, including image segmentation, recognition, and classification, and provides a comparison in a tabular format, hoping to provide inspiration for their future development.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124804979","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}
{"title":"COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine","authors":"Xue Han, Zuojin Hu, William Wang, Dimas Lima","doi":"10.4018/ijpch.309952","DOIUrl":"https://doi.org/10.4018/ijpch.309952","url":null,"abstract":"COVID-19 has swept the world and has had great impact on us. Rapid and accurate diagnosis of COVID-19 is essential. Analysis of chest CT images is an effective means. In this paper, an automatic diagnosis algorithm based on chest CT images is proposed. It extracts image features by stationary wavelet entropy (SWE), classifies and trains the input dataset by extreme learning machine (LEM), and finally determines the model through k-fold cross-validation (k-fold CV). By detecting 296 chest CT images of healthy individuals and COVID-19 patients, the algorithm outperforms state-of-the-art methods in sensitivity, specificity, precision, accuracy, F1, MCC, and FMI.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129310344","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}
{"title":"Theory and Practice of Person-Centered Mental Health Services","authors":"A. Rudnick","doi":"10.4018/ijpch.2021010103","DOIUrl":"https://doi.org/10.4018/ijpch.2021010103","url":null,"abstract":"Person-centered approaches have been demonstrated to be effective in a variety of mental healthcare contexts and across different service user populations. In this overview, the author explores fundamental notions of such person-centered approaches (PCAs) with examples from health (clinical) care, research, education, and leadership of successful person-centered applications in the mental health field. He then extends this conversation further by looking at related challenges, particularly where person-centered approaches are lacking and may be applied. He challenges various areas in mental health services by addressing commonplace examples in which person-centered approaches do not appear to occur. He concludes with a call to action for profound person-centered change at the level of self and with others, which may lead to fundamental transformation of the mental health system.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023511","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}
{"title":"Motivation for Older Adult Participation in Community-Based Physical Exercises","authors":"Theresa Abah, Gayle L. Prybutok","doi":"10.4018/ijpch.2021010101","DOIUrl":"https://doi.org/10.4018/ijpch.2021010101","url":null,"abstract":"About 70-80% of adults who participate in any leisure exercise can reduce their risk of dying from noncommunicable diseases and promote healthy lifestyles. This study investigates relevant services offered in a community-based program that influenced healthy behavior adoption among older adults. Using semi-structured interviews, responses were collected from 20 participants (mean age, M = 77.7, SD = 9.3), then transcribed and analyzed using Max QDA qualitative data analytic tool. Pre-assigned themes based on theory helped to understand participants' reasons to exercise. Motivation was influenced by multiple factors, grouped under three categories: attitude, belief, and enablers towards physical activity. Healthy behavior adoption was influenced by access to community resources, coordinated care, affordable care, person-focused care, and professional care. These findings are essential to program managers and policymakers working with this population, as it provides guidance in designing community-based prevention programs and policies to standardize practice.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115370266","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}
H. Mushcab, Jalal Al Alwan, S. Ashrafi, Maesoon Abusadah, David Bunting, Saeed Al Yami
{"title":"Evaluating the Implementation of Electronic Medical Records in the Emergency Medical Services Department at Johns Hopkins Aramco Healthcare","authors":"H. Mushcab, Jalal Al Alwan, S. Ashrafi, Maesoon Abusadah, David Bunting, Saeed Al Yami","doi":"10.4018/ijpch.2020070102","DOIUrl":"https://doi.org/10.4018/ijpch.2020070102","url":null,"abstract":"This paper evaluates the implementation of EPIC system on the quality performance of Johns Hopkins Aramco Healthcare's (JHAH) Emergency Medical Services (EMS) department. This is a retrospective observational study conducted to compare the EMS department performance prior and post the implementation of EPIC on January 26th, 2018. A total number of 49,006 patients visited the EMS department during the control period (pre EPIC) while a total number of 42,431 patients visited the department during the study period (post EPIC). A statistically significant improvement with P<0.05 was found in the waiting time for patients triaged in all acuity levels. The volume of patients visiting the EMS department had a statistically significance increment after the implementation of EPIC. EHRs use the advancement in technology to store and instantly provide clinical information to the healthcare providers. Integrating EHRs to the person-centered care culture has a great potential of empowerment, education, and engagement of individuals in a more effective manner.","PeriodicalId":296225,"journal":{"name":"International Journal of Patient-Centered Healthcare","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127919924","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}