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Article: A Systematic Literature Review of Health Information Systems for Healthcare.

Epizitone, Ayogeboh / Moyane, Smangele Pretty / Agbehadji, Israel Edem

2023 Volume 11, Issue 7

Abstract: Health information system deployment has been driven by the transformation and digitalization currently confronting healthcare. The need and potential of these systems within healthcare have been tremendously driven by the global instability that has ... ...

Abstract	Health information system deployment has been driven by the transformation and digitalization currently confronting healthcare. The need and potential of these systems within healthcare have been tremendously driven by the global instability that has affected several interrelated sectors. Accordingly, many research studies have reported on the inadequacies of these systems within the healthcare arena, which have distorted their potential and offerings to revolutionize healthcare. Thus, through a comprehensive review of the extant literature, this study presents a critique of the health information system for healthcare to supplement the gap created as a result of the lack of an in-depth outlook of the current health information system from a holistic slant. From the studies, the health information system was ascertained to be crucial and fundament in the drive of information and knowledge management for healthcare. Additionally, it was asserted to have transformed and shaped healthcare from its conception despite its flaws. Moreover, research has envisioned that the appraisal of the current health information system would influence its adoption and solidify its enactment within the global healthcare space, which is highly demanded.
Language	English
Publishing date	2023-03-27
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2721009-1
ISSN	2227-9032
ISSN	2227-9032
DOI	10.3390/healthcare11070959
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications.

Epizitone, Ayogeboh / Moyane, Smangele Pretty / Agbehadji, Israel Edem

Journal of multidisciplinary healthcare

2023 Volume 16, Page(s) 4015–4025

Abstract: Introduction: Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the ... ...

Abstract	Introduction: Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention. Objective: This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS. Method: Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery. Results: This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed. Discussions: Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements. Conclusion: The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.
Language	English
Publishing date	2023-12-12
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2453343-9
ISSN	1178-2390
ISSN	1178-2390
DOI	10.2147/JMDH.S433299
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Article ; Online: A Systematic Literature Review of Health Information Systems for Healthcare

Ayogeboh Epizitone / Smangele Pretty Moyane / Israel Edem Agbehadji

Healthcare, Vol 11, Iss 959, p

2023 Volume 959

Abstract	Health information system deployment has been driven by the transformation and digitalization currently confronting healthcare. The need and potential of these systems within healthcare have been tremendously driven by the global instability that has affected several interrelated sectors. Accordingly, many research studies have reported on the inadequacies of these systems within the healthcare arena, which have distorted their potential and offerings to revolutionize healthcare. Thus, through a comprehensive review of the extant literature, this study presents a critique of the health information system for healthcare to supplement the gap created as a result of the lack of an in-depth outlook of the current health information system from a holistic slant. From the studies, the health information system was ascertained to be crucial and fundament in the drive of information and knowledge management for healthcare. Additionally, it was asserted to have transformed and shaped healthcare from its conception despite its flaws. Moreover, research has envisioned that the appraisal of the current health information system would influence its adoption and solidify its enactment within the global healthcare space, which is highly demanded.
Keywords	health information system ; information system ; knowledge management ; healthcare ; Medicine ; R
Subject code	306
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Health Information System and Health Care Applications Performance in the Healthcare Arena: A Bibliometric Analysis.

Epizitone, Ayogeboh / Moyane, Smangele Pretty / Agbehadji, Israel Edem

Healthcare (Basel, Switzerland)

2022 Volume 10, Issue 11

Abstract: There have been several studies centred on health information systems with many insights provided to enhance health care applications globally. These studies have provided theoretical schemes for fortifying the enactment and utilisation of the Health ... ...

Abstract	There have been several studies centred on health information systems with many insights provided to enhance health care applications globally. These studies have provided theoretical schemes for fortifying the enactment and utilisation of the Health Information System (HIS). In addition, these research studies contribute greatly to the development of HIS in alignment with major stakeholders such as health practitioners and recipients of health care. Conversely, there has been trepidation about HIS' sustainability and resilience for healthcare applications in the era of digitalization and globalization. Hence, this paper investigates research on HIS with a primary focus on health care applications to ascertain its sustainability and resilience amidst the transformation of the global healthcare space. Therefore, using a bibliometric approach, this paper measures the performance of health information systems and healthcare for health care applications using bibliometric data from the web of science database. The findings reveal solid evidence of the constructive transformation of health information systems and health care applications in the healthcare arena, providing ample evidence of the adaptation of HIS and health care applications within the healthcare arena to the fourth industrial revolution and, additionally, revealing the resilient alignment of health care applications and health information systems.
Language	English
Publishing date	2022-11-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2721009-1
ISSN	2227-9032
ISSN	2227-9032
DOI	10.3390/healthcare10112273
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Article ; Online: Nature-Inspired Search Method and Custom Waste Object Detection and Classification Model for Smart Waste Bin.

Agbehadji, Israel Edem / Abayomi, Abdultaofeek / Bui, Khac-Hoai Nam / Millham, Richard C / Freeman, Emmanuel

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 16

Abstract: Waste management is one of the challenges facing countries globally, leading to the need for innovative ways to design and operationalize smart waste bins for effective waste collection and management. The inability of extant waste bins to facilitate ... ...

Abstract	Waste management is one of the challenges facing countries globally, leading to the need for innovative ways to design and operationalize smart waste bins for effective waste collection and management. The inability of extant waste bins to facilitate sorting of solid waste at the point of collection and the attendant impact on waste management process is the motivation for this study. The South African University of Technology (SAUoT) is used as a case study because solid waste management is an aspect where SAUoT is exerting an impact by leveraging emerging technologies. In this article, a convolutional neural network (CNN) based model called You-Only-Look-Once (YOLO) is employed as the object detection algorithm to facilitate the classification of waste according to various categories at the point of waste collection. Additionally, a nature-inspired search method is used as learning rate for the CNN model. The custom YOLO model was developed for waste object detection, trained with different weights and backbones, namely darknet53.conv.74, darknet19_448.conv.23, Yolov4.conv.137 and Yolov4-tiny.conv.29, respectively, for Yolov3, Yolov3-tiny, Yolov4 and Yolov4-tiny models. Eight (8) classes of waste and a total of 3171 waste images are used. The performance of YOLO models is considered in terms of accuracy of prediction (Average Precision-AP) and speed of prediction measured in milliseconds. A lower loss value out of a percentage shows a higher performance of prediction and a lower value on speed of prediction. The results of the experiment show that Yolov3 has better accuracy of prediction as compared with Yolov3-tiny, Yolov4 and Yolov4-tiny. Although the Yolov3-tiny is quick at predicting waste objects, the accuracy of its prediction is limited. The mean AP (%) for each trained version of YOLO models is Yolov3 (80%), Yolov4-tiny (74%), Yolov3-tiny (57%) and Yolov4 (41%). This result of mAP (%) indicates that the Yolov3 model produces the best performance results (80%). In this regard, it is useful to implement a model that ensures accurate prediction to develop a smart waste bin system at the institution. The experimental results show the combination of KSA learning rate parameter of 0.0007 and Yolov3 is identified as the accurate model for waste object detection and classification. The use of nature-inspired search methods, such as the Kestrel-based Search Algorithm (KSA), has shown future prospect in terms of learning rate parameter determination in waste object detection and classification. Consequently, it is imperative for an EdgeIoT-enabled system to be equipped with Yolov3 for waste object detection and classification, thereby facilitating effective waste collection.
MeSH term(s)	Algorithms ; Neural Networks, Computer
Language	English
Publishing date	2022-08-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22166176
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Article ; Online: COVID-19 Pandemic Waves

Israel Edem Agbehadji / Bankole Osita Awuzie / Alfred Beati Ngowi

Sustainability, Vol 13, Iss 10168, p

4IR Technology Utilisation in Multi-Sector Economy

2021 Volume 10168

Abstract: In this paper, we reviewed the Fourth Industrial Revolution (4IR) technologies applied to waves of the coronavirus disease (COVID-19). COVID-19 is an existential threat that has resulted in an unprecedented loss of lives, disruption of flight schedules, ... ...

Abstract	In this paper, we reviewed the Fourth Industrial Revolution (4IR) technologies applied to waves of the coronavirus disease (COVID-19). COVID-19 is an existential threat that has resulted in an unprecedented loss of lives, disruption of flight schedules, shutdown of businesses and much more. Though several researchers have highlighted the enormous benefits of 4IR technologies in containing the COVID-19 pandemic, the recent waves of the pandemic call for a thorough review of these technological interventions. The cyber-physical space has had its share of the COVID-19 pandemic effect, and through this review, we highlight the salient issues to help policy formulation towards managing the impact of subsequent COVID-19 waves within such environments. Hence, the purpose of this paper is to review the application of 4IR technologies during the COVID-19 pandemic waves and to highlight their shortcomings. Recent research articles were sourced from an online repository and thoroughly reviewed to highlight 4IR technology applications, innovations, shortcomings and multi-sector challenges. The outcome of this review indicates that the second wave of the pandemic resulted in a lower proportion of patients requiring invasive mechanical ventilation and a lower rate of thrombotic events. In addition, it was revealed that the delay between ICU admissions and tracheal intubation was longer in the second wave in the health care sector. Again, the review suggests that 4IR technologies have been utilized across all the sectors including education, businesses, society, manufacturing, healthcare, agriculture and mining. Businesses have revised their service delivery models to include 4IR technologies and avoid physical contacts. In society, digital certificates, among other digital platforms, have been utilized to assist with the movements of persons who have been vaccinated. Manufacturing concerns have also utilized robots in manufacturing to reduce human-to-human physical contact. The mining sector has automated their work ...
Keywords	digital technologies ; coronavirus disease (COVID-19) pandemic wave ; 4IR technology utilisation ; multi-sector economy ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	303
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing.

Agbehadji, Israel Edem / Awuzie, Bankole Osita / Ngowi, Alfred Beati / Millham, Richard C

International journal of environmental research and public health

2020 Volume 17, Issue 15

Abstract: The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate ...

Abstract	The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.
MeSH term(s)	Artificial Intelligence ; Betacoronavirus ; Big Data ; COVID-19 ; Computer Simulation ; Contact Tracing ; Coronavirus Infections ; Humans ; Pandemics/prevention & control ; Pneumonia, Viral ; SARS-CoV-2
Keywords	covid19
Language	English
Publishing date	2020-07-24
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph17155330
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Article: Review of Big Data , Artificial Intelligence and Nature-Inspired Computing Models for Performance Improvement towards Detection of COVID-19 Pandemic Case and Contact Tracing Review of Big Data , Artificial Intelligence and Nature-Inspired Computing Models

Agbehadji, Israel Edem / Awuzie, B. O. / Ngowi, Alfred

Www.Researchgate.Net

Abstract: The emergence of the 2019 Novel Coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide It has significantly affected the health system, the economic, educational and social facets of contemporary society As the rate of ...

Abstract	The emergence of the 2019 Novel Coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide It has significantly affected the health system, the economic, educational and social facets of contemporary society As the rate of transmission continues to rise, various collaborative approaches among stakeholders to develop innovative means of screening and detecting COVID-19 cases among human beings at a commensurate rate has been observed In addition, the utility of computing models associated with the 4 th Industrial revolution technologies in achieving the desired feat has been highlighted However, there is a gap in terms of accuracy of detection and prediction of COVID-19 cases and tracing of contacts This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases We focus on big data, artificial intelligence and Nature-Inspired Computing models that can be adopted in the current pandemic The review suggested that Nature-Inspired Computing models have demonstrated good performance in feature selection of medical issues Additionally, contact tracing using big data analytics should be explored in pandemic related cases such as COVID-19
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #691596
Database	COVID19

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Article ; Online: Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Israel Edem Agbehadji / Bankole Osita Awuzie / Alfred Beati Ngowi / Richard C. Millham

International Journal of Environmental Research and Public Health, Vol 17, Iss 5330, p

2020 Volume 5330

Abstract	The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19’s cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.
Keywords	contact tracing ; 2019 novel coronavirus disease (COVID-19) ; nature-inspired computing (NIC) ; artificial intelligence (AI) ; big data ; Medicine ; R
Subject code	006
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Agbehadji, Israel Edem / Awuzie, Bankole Osita / Ngowi, Alfred Beati / Millham, Richard C

Int. j. environ. res. public health (Online)

Abstract	The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #669606
Database	COVID19

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