Published: Sep 16, 2022
Converted to Gold OA:
DOI: 10.4018/IJPCH.309117
Volume 12
Archana Kumari Ghildiyal, Jitendra Chandra Devrari, Atul Dhyani
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...
Show More
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.
Content Forthcoming
Add to Your Personal Library: Article
Cite Article
Cite Article
MLA
Ghildiyal, Archana Kumari, et al. "Determinants of Service Quality in Healthcare: Patient and Provider Perspectives." IJPCH vol.12, no.1 2022: pp.1-12. http://doi.org/10.4018/IJPCH.309117
APA
Ghildiyal, A. K., Devrari, J. C., & Dhyani, A. (2022). Determinants of Service Quality in Healthcare: Patient and Provider Perspectives. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-12. http://doi.org/10.4018/IJPCH.309117
Chicago
Ghildiyal, Archana Kumari, Jitendra Chandra Devrari, and Atul Dhyani. "Determinants of Service Quality in Healthcare: Patient and Provider Perspectives," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-12. http://doi.org/10.4018/IJPCH.309117
Export Reference
Published: Sep 22, 2022
Converted to Gold OA:
DOI: 10.4018/IJPCH.309118
Volume 12
Jiaji Wang, Logan Graham
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...
Show More
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.
Content Forthcoming
Add to Your Personal Library: Article
Cite Article
Cite Article
MLA
Wang, Jiaji, and Logan Graham. "COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO." IJPCH vol.12, no.1 2022: pp.1-14. http://doi.org/10.4018/IJPCH.309118
APA
Wang, J. & Graham, L. (2022). COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-14. http://doi.org/10.4018/IJPCH.309118
Chicago
Wang, Jiaji, and Logan Graham. "COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-14. http://doi.org/10.4018/IJPCH.309118
Export Reference
Published: Oct 7, 2022
Converted to Gold OA:
DOI: 10.4018/IJPCH.309950
Volume 12
Kiana S. Zanganeh, Darrell Norman Burrell
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...
Show More
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.
Content Forthcoming
Add to Your Personal Library: Article
Cite Article
Cite Article
MLA
Zanganeh, Kiana S., and Darrell Norman Burrell. "An Educational Solution-Driven Discussion About Racial Public Health Disparities During the COVID-19 Pandemic." IJPCH vol.12, no.1 2022: pp.1-12. http://doi.org/10.4018/IJPCH.309950
APA
Zanganeh, K. S. & Burrell, D. N. (2022). An Educational Solution-Driven Discussion About Racial Public Health Disparities During the COVID-19 Pandemic. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-12. http://doi.org/10.4018/IJPCH.309950
Chicago
Zanganeh, Kiana S., and Darrell Norman Burrell. "An Educational Solution-Driven Discussion About Racial Public Health Disparities During the COVID-19 Pandemic," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-12. http://doi.org/10.4018/IJPCH.309950
Export Reference
Published: Sep 30, 2022
Converted to Gold OA:
DOI: 10.4018/IJPCH.309951
Volume 12
Xiaoyan Jiang, Mackenzie Brown, Hei-Ran Cheong, Zuojin Hu
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...
Show More
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.
Content Forthcoming
Add to Your Personal Library: Article
Cite Article
Cite Article
MLA
Jiang, Xiaoyan, et al. "COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm." IJPCH vol.12, no.1 2022: pp.1-14. http://doi.org/10.4018/IJPCH.309951
APA
Jiang, X., Brown, M., Cheong, H., & Hu, Z. (2022). COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-14. http://doi.org/10.4018/IJPCH.309951
Chicago
Jiang, Xiaoyan, et al. "COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-14. http://doi.org/10.4018/IJPCH.309951
Export Reference
Published: Sep 30, 2022
Converted to Gold OA:
DOI: 10.4018/IJPCH.309952
Volume 12
Xue Han, Zuojin Hu, William Wang, Dimas Lima
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...
Show More
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.
Content Forthcoming
Add to Your Personal Library: Article
Cite Article
Cite Article
MLA
Han, Xue, et al. "COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine." IJPCH vol.12, no.1 2022: pp.1-13. http://doi.org/10.4018/IJPCH.309952
APA
Han, X., Hu, Z., Wang, W., & Lima, D. (2022). COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-13. http://doi.org/10.4018/IJPCH.309952
Chicago
Han, Xue, et al. "COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-13. http://doi.org/10.4018/IJPCH.309952
Export Reference
Published: Oct 26, 2022
Converted to Gold OA:
DOI: 10.4018/IJPCH.311444
Volume 12
Jiaji Wang
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...
Show More
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.
Content Forthcoming
Add to Your Personal Library: Article Published: Oct 27, 2022
Converted to Gold OA:
DOI: 10.4018/IJPCH.313195
Volume 12
Fatimah AlBeesh, Jalal Al Alwan
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...
Show More
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.
Content Forthcoming
Add to Your Personal Library: Article
Cite Article
Cite Article
MLA
AlBeesh, Fatimah, and Jalal Al Alwan. "30-Days Same-Cause Congestive Heart Failure Readmission Rate at JHAH." IJPCH vol.12, no.1 2022: pp.1-10. http://doi.org/10.4018/IJPCH.313195
APA
AlBeesh, F. & Al Alwan, J. (2022). 30-Days Same-Cause Congestive Heart Failure Readmission Rate at JHAH. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-10. http://doi.org/10.4018/IJPCH.313195
Chicago
AlBeesh, Fatimah, and Jalal Al Alwan. "30-Days Same-Cause Congestive Heart Failure Readmission Rate at JHAH," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-10. http://doi.org/10.4018/IJPCH.313195
Export Reference
IGI Global Open Access Collection provides all of IGI Global’s open access content in one convenient location and user-friendly interface
that can easily searched or integrated into library discovery systems.
Browse IGI Global Open
Access Collection
All inquiries regarding IJPCH should be directed to the attention of:
Submission-Related InquiriesYu-Dong Zhang
Editor-in-Chief
International Journal of Patient-Centered Healthcare (IJPCH)
Email:
yudongzhang@ieee.orgAuthor Services Inquiries
For inquiries involving pre-submission concerns, please contact the Journal Development Division:
journaleditor@igi-global.comOpen Access Inquiries
For inquiries involving publishing costs, APCs, etc., please contact the Open Access Division:
openaccessadmin@igi-global.comProduction-Related Inquiries
For inquiries involving accepted manuscripts currently in production or post-production, please contact the Journal Production Division:
journalproofing@igi-global.comRights and Permissions Inquiries
For inquiries involving permissions, rights, and reuse, please contact the Intellectual Property & Contracts Division:
contracts@igi-global.comPublication-Related Inquiries
For inquiries involving journal publishing, please contact the Acquisitions Division:
acquisition@igi-global.comDiscoverability Inquiries
For inquiries involving sharing, promoting, and indexing of manuscripts, please contact the Citation Metrics & Indexing Division:
indexing@igi-global.com Editorial Office
701 E. Chocolate Ave.
Hershey, PA 17033, USA
717-533-8845 x100