https://hspublishing.org/JRECS/issue/feed 2024-12-09T17:44:10+00:00 Faruk Soban office@headstartnetwork.org Open Journal Systems <p><em><strong>Journal of Research in Engineering and Computer Sciences (JRECS)</strong></em> is a peer-reviewed academic journal published on bi-monthly bases that publishes high-quality research in the fields of engineering and computer sciences. The journal provides a platform for researchers, engineers, and scientists from around the world to share their latest research findings, ideas, and innovations.</p> <p>Engineering and computer sciences are two fields that are constantly evolving and pushing the boundaries of what is possible. They are integral to the development of new technologies and innovations that have transformed the way we live and work. Research in these fields seeks to understand the underlying principles that govern complex systems, as well as to develop new tools and techniques for solving complex problems. From artificial intelligence and machine learning to robotics and biotechnology, engineering and computer science research are at the forefront of many cutting-edge fields. As the demand for new technologies and innovative solutions continues to grow, the importance of research in these fields cannot be overstated.</p> https://hspublishing.org/JRECS/article/view/725 2024-11-26T05:50:19+00:00 Darlington Ekweli stan.okoro@gmail.com Callistus Obunadike stan.okoro@gmail.com Lochan Pokharel stan.okoro@gmail.com Abdul-Waliyyu Bello stan.okoro@gmail.com Chinenye Obunadike stan.okoro@gmail.com Emmanuel Okoro stan.okoro@gmail.com Stanley Okoro stan.okoro@gmail.com

<p>The objective of this study is to enhance risk management practices in healthcare administration through the application of computational risk pooling methods. Healthcare systems are becoming increasingly complex due to advancements in technology and evolving practices, necessitating more effective risk management strategies. The study employs a survey design with a quantitative research approach, collecting data from 150 respondents across various healthcare centers, including hospitals, clinics, and care facilities. Stratified sampling was used to ensure a representative selection of participants. Data was analyzed adopting descriptive tools such as frequency tables and percentages to interpret the results. The key findings reveal that computational risk pooling significantly reduces financial losses and improves patient outcomes by providing a comprehensive view of potential risks and enabling better resource allocation. However, the implementation of these advanced techniques is challenged by resource constraints, lack of expertise, and data management issues within healthcare settings. The study recommends that healthcare administrators prioritize the integration of computational risk pooling into their risk management strategies. This integration should be supported by investments in training, data management infrastructure, and the development of standardized protocols to overcome the identified challenges. By doing so, healthcare organizations can enhance their ability to predict, assess, and mitigate risks, leading to improved patient safety and more efficient healthcare operations.</p>

2024-12-09T00:00:00+00:00 Copyright (c) 2024 Journal of Research in Engineering and Computer Sciences https://hspublishing.org/JRECS/article/view/726 2024-11-26T05:55:39+00:00 Florence Ademeji stan.okoro@gmail.com Emmanuel Okoro stan.okoro@gmail.com Gbenga Akingbulere stan.okoro@gmail.com Tosin Clement stan.okoro@gmail.com Stanley Okoro stan.okoro@gmail.com

<p>Undeserved communities are usually at the receiving ends of resources allocation, particularly healthcare resources, which calls for understanding the key factors that contributes to efficient allocation of resources to the areas. This study investigates the use of predictive analytics for healthcare resource allocation in underserved communities. With the aid of predictive analytics, government can allocate resources effectively, which is sufficient enough to cater for the health needs of the people. The study adopted machine learning techniques, with 26 features included in the model to predict healthcare resource allocation. Data utilized in the study was collected from survey with the participants being residents in underserved communities. The study found that the important features are age, mental health status, visit to the hospital, and healthcare facilities quality, among others. It was recommended that Enhance mental health services and prioritize high-quality healthcare facilities to improve patient outcomes. Increase the availability and accessibility of healthcare providers, especially in areas with frequent hospital visits.</p>

2024-12-09T00:00:00+00:00 Copyright (c) 2024 Journal of Research in Engineering and Computer Sciences