A Systematic Literature Review of Health Information Systems for Healthcare

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

doi:10.3390/healthcare11070959

Cited by 9 publications

(10 citation statements)

References 64 publications

(93 reference statements)

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“…Therefore, credible health organizations are expected to publicly disclose infectious disease-related biomedical data immediately from the onset of the pandemic. It is imperative that responsible governance systems are organized at the national level [ 39 , 40 ]. International organizations such as WHO and the UN need to be involved in monitoring, collecting, and sharing biomedical data using effective health information systems [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…It is imperative that responsible governance systems are organized at the national level [ 39 , 40 ]. International organizations such as WHO and the UN need to be involved in monitoring, collecting, and sharing biomedical data using effective health information systems [ 40 ]. Such a global healthcare ecosystem architecture will enable the rapid development of customized ML-based diagnosis systems in case of the next rounds of pandemic outbreaks.…”

Section: Discussionmentioning

confidence: 99%

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Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis

Cho

Hong

2023

Healthcare

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The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis

Cho

Hong

2023

Healthcare

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“…In keeping with the protocol by Templier and Paré (2015) to use set of keywords to identify latent research publication and grey literature that is suitable for this paper. This paper made use of Google Scholar, SCOPUS, and Web of Science databases given that they are commonly used databases in the system literature reviews (Tremblay-Cantin et al, 2023;Epizitone et al, 2023;Vargo et al, 2020;Haddaway et al, 2015a) to conduct searches in the previous eight years (2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023). The SCOPUS and Web of Science databases are well recognized among the research practitioners' cycle and their advanced search functionalities made provision for efficient searches.…”

Section: Literature Search Criteria and In/exclusionmentioning

confidence: 99%

Conceptual change management framework for effective use of examination administration system: a systemic review

Mphahlele,

Kekwaletswe,

Seaba

2024

IJRBS

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This paper systematically reviews literature with the aim of conceptualizing a change management framework for the effective use of the Examination Administration System (EAS). Information systems (IS) change management factors in the implementation and the post-implementation phases are systematically reviewed to identify 37 reports between 2015 and 2023 and content was analyzed. Post-acceptance phase poses many challenges that often result in resistance from the user community thus relinquishing the expected gains. This paper explains change management factors in the post-implementation phase of information systems as they are not adequately addressed in existing literature. The conceptualization of the change management framework draws on the integration of the Unified Theory of Acceptance, Use of Technology and Effective Use Theory and nine change management factors as facilitating conditions. The paper concludes that effective use of EAS is realized through change management factors (i.e., user training, top management support activities, information flow, planning EAS as a change, change shared vision, performance measurement, user involvement and change recognition, user satisfaction and effective communication). This study further adds the indirect mechanisms (i.e., technology use and IS adaptive use) through which the change management factors can impact effective use. The study provides a useful guide to the practitioners and researchers to explain the change management strategies for the effective use of information systems and improve it. This paper also proposes that future studies adapt and/or empirically test conceptual change management apparatus for effective use to ascertain its validity within various contexts.

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“…This constrained the ability to track patient outcomes and the quality of healthcare [ 5 , 10 , 12 ]. Despite the limited usage of electronic records in Syria, there was a gradual interest in applying such technologies in health information management [ 11 , 13 ]. This has mainly emerged in private clinics and health facilities that developed their own electronic information systems to collect, manage, store, and retrieve records [ 11 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Health information management systems and practices in conflict-affected settings: the case of northwest Syria

Ladadwa,

Hariri,

Alatras

et al. 2024

Global Health

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Background In conflict settings, as it is the case in Syria, it is crucial to enhance health information management to facilitate an effective and sustainable approach to strengthening health systems in such contexts. In this study, we aim to provide a baseline understanding of the present state of health information management in Northwest Syria (NWS) to better plan for strengthening the health information system of the area that is transitioning to an early-recovery stage. Methods A combination of questionnaires and subsequent interviews was used for data collection. Purposive sampling was used to select twenty-one respondents directly involved in managing and directing different domains of health information in the NWS who worked with local NGOs, INGOs, UN-agencies, or part of the Health Working Group. A scoring system for each public health domain was constructed based on the number and quality of the available datasets for these domains, which were established by Checci and others. Results & conclusions Reliable and aggregate health information in the NWS is limited, despite some improvements made over the past decade. The conflict restricted and challenged efforts to establish a concentrated and harmonized HIS in the NWS, which led to a lack of leadership, poor coordination, and duplication of key activities. Although the UN established the EWARN and HeRAMS as common data collection systems in the NWS, they are directed toward advocacy and managed by external experts with little participation or access from local stakeholders to these datasets. Recommendations There is a need for participatory approaches and the empowerment of local actors and local NGOs, cooperation between local and international stakeholders to increase access to data, and a central domain for planning, organization, and harmonizing the process. To enhance the humanitarian health response in Syria and other crisis areas, it is imperative to invest in data collection and utilisation, mHealth and eHealth technologies, capacity building, and robust technical and autonomous leadership.

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A Systematic Literature Review of Health Information Systems for Healthcare

Cited by 9 publications

References 64 publications

Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis

Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis

Conceptual change management framework for effective use of examination administration system: a systemic review

Health information management systems and practices in conflict-affected settings: the case of northwest Syria

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