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      Feasibility and utility of Point-of-Care electronic clinical data capture in Uganda’s healthcare system: a qualitative study

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          Abstract

          Objective

          This study aimed to assess Uganda’s readiness for implementing a national Point-of-Care (PoC) electronic clinical data capture platform that can function in near real-time.

          Methods

          A qualitative, cross-sectional design was adopted to obtain a snapshot of Uganda’s eHealth system landscape with an aim to assess the readiness for implementing PoC platform. A purposive sampling strategy was used to select the study districts per region, health facilities per district, and participants per facility or district.

          Results

          Nine facilitators were identified, including health worker motivation to serve the community, affirmative action on eHealth financing, improved integrating information and communication technology (ICT) infrastructure, Internet and electricity power connectivity, improved human resource skills and knowledge, the culture of sensitizing and training of stakeholders on eHealth interventions, the perceived value of the platform, health workers’ motivation to improve health data quality, interest to improve data use, and continuous improvement in the eHealth regulatory environment. Other suggestions entailed several requirements that must be met, including infrastructure, eHealth governance, human resources, as well as functional and data requirements.

          Discussion

          Uganda, like other low-income countries, has adopted ICT to help solve some of its health system challenges. Although several challenges face eHealth implementations in Uganda, this study revealed facilitators that can be leveraged and requirements that, if met, would facilitate the successful implementation of a near real-time data capture platform capable of improving the country’s health outcomes.

          Conclusion

          Other countries with eHealth implementations similar to those faced in Uganda can also leverage identified facilitators and address the stakeholders’ requirements.

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          Most cited references45

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          User Acceptance of Information Technology: Toward a Unified View

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            Information Systems Success: The Quest for the Dependent Variable

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              Artificial intelligence in radiology

              Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
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                Author and article information

                Contributors
                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                May 2023
                08 March 2023
                08 March 2023
                : 30
                : 5
                : 932-942
                Affiliations
                Department of Information Systems, School of Computing and Informatics Technology, Makerere University , Kampala, Uganda
                Department of Information Systems, School of Computing and Informatics Technology, Makerere University , Kampala, Uganda
                Department of Medicine, University of Cambridge , Cambridge, UK
                Department of Information Systems, School of Computing and Informatics Technology, Makerere University , Kampala, Uganda
                Department of Epidemiology & Biostatistics, School of Public Health, Makerere University , Kampala, Uganda
                Author notes
                Corresponding Author: Josephine Nabukenya, Department of Information Systems, School of Computing and Informatics Technology, Makerere University, PO Box 7062, Kampala, Uganda; josephine@ 123456cit.ac.ug
                Article
                ocad034
                10.1093/jamia/ocad034
                10114114
                36888891
                9c2d1524-6bf1-4697-aaa8-07ba50a8f834
                © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 November 2022
                : 30 January 2023
                : 21 February 2023
                : 25 February 2023
                Page count
                Pages: 11
                Funding
                Funded by: United Kingdom Research and Innovations;
                Funded by: Department of Health and Social Care, DOI 10.13039/501100000276;
                Funded by: Foreign, Commonwealth & Development Office, DOI 10.13039/501100020171;
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Funded by: Economic and Social Research Council, DOI 10.13039/501100000269;
                Funded by: Wellcome, DOI 10.13039/100004440;
                Award ID: MR/S013164/1
                Categories
                Research and Applications
                AcademicSubjects/MED00580
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01530

                Bioinformatics & Computational biology
                ehealth,electronic clinical data capture,feasibility,healthcare system,point-of-care

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