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Barriers and enablers for implementation of digital-linked diagnostics models at point-of-care in South Africa: stakeholder engagement
The integration of digital technologies holds significant promise in enhancing accessibility to disease diagnosis and treatment at point-of-care (POC) settings. Effective implementation of such interventions necessitates comprehensive stakeholder engagements. This study presents the outcomes of a wo...
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Published in: | BMC health services research 2024-02, Vol.24 (1), p.216-216, Article 216 |
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Main Authors: | , , , , , , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | The integration of digital technologies holds significant promise in enhancing accessibility to disease diagnosis and treatment at point-of-care (POC) settings. Effective implementation of such interventions necessitates comprehensive stakeholder engagements. This study presents the outcomes of a workshop conducted with key stakeholders, aiming to discern barriers and enablers in implementing digital-connected POC diagnostic models in South Africa. The workshop, a component of the 2022 REASSURED Diagnostics symposium, employed the nominal group technique (NGT) and comprised two phases: Phase 1 focused on identifying barriers, while Phase 2 centered on enablers for the implementation of digital-linked POC diagnostic models. Stakeholders identified limited connectivity, restricted offline functionality, and challenges related to load shedding or rolling electricity blackouts as primary barriers. Conversely, ease of use, subsidies provided by the National Health Insurance, and 24-h assistance emerged as crucial enablers for the implementation of digital-linked POC diagnostic models. The NGT workshop proved to be an effective platform for elucidating key barriers and enablers in implementing digital-linked POC diagnostic models. Subsequent research endeavors should concentrate on identifying optimal strategies for implementing these advanced diagnostic models in underserved populations. |
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ISSN: | 1472-6963 1472-6963 |
DOI: | 10.1186/s12913-024-10691-z |