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Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study
BACKGROUND: Low- and middle-income countries face difficulties in providing adequate health care. One of the reasons is a shortage of qualified health workers. Diagnostic decision support systems are designed to aid clinicians in their work and have the potential to mitigate pressure on health care...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214611/ https://www.ncbi.nlm.nih.gov/pubmed/35671073 http://dx.doi.org/10.2196/34298 |
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author | Schmude, Marcel Salim, Nahya Azadzoy, Hila Bane, Mustafa Millen, Elizabeth O’Donnell, Lisa Bode, Philipp Türk, Ewelina Vaidya, Ria Gilbert, Stephen |
author_facet | Schmude, Marcel Salim, Nahya Azadzoy, Hila Bane, Mustafa Millen, Elizabeth O’Donnell, Lisa Bode, Philipp Türk, Ewelina Vaidya, Ria Gilbert, Stephen |
author_sort | Schmude, Marcel |
collection | PubMed |
description | BACKGROUND: Low- and middle-income countries face difficulties in providing adequate health care. One of the reasons is a shortage of qualified health workers. Diagnostic decision support systems are designed to aid clinicians in their work and have the potential to mitigate pressure on health care systems. OBJECTIVE: The Artificial Intelligence–Based Assessment of Health Symptoms in Tanzania (AFYA) study will evaluate the potential of an English-language artificial intelligence–based prototype diagnostic decision support system for mid-level health care practitioners in a low- or middle-income setting. METHODS: This is an observational, prospective clinical study conducted in a busy Tanzanian district hospital. In addition to usual care visits, study participants will consult a mid-level health care practitioner, who will use a prototype diagnostic decision support system, and a study physician. The accuracy and comprehensiveness of the differential diagnosis provided by the diagnostic decision support system will be evaluated against a gold-standard differential diagnosis provided by an expert panel. RESULTS: Patient recruitment started in October 2021. Participants were recruited directly in the waiting room of the outpatient clinic at the hospital. Data collection will conclude in May 2022. Data analysis is planned to be finished by the end of June 2022. The results will be published in a peer-reviewed journal. CONCLUSIONS: Most diagnostic decision support systems have been developed and evaluated in high-income countries, but there is great potential for these systems to improve the delivery of health care in low- and middle-income countries. The findings of this real-patient study will provide insights based on the performance and usability of a prototype diagnostic decision support system in low- or middle-income countries. TRIAL REGISTRATION: ClinicalTrials.gov NCT04958577; http://clinicaltrials.gov/ct2/show/NCT04958577 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34298 |
format | Online Article Text |
id | pubmed-9214611 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-92146112022-06-23 Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study Schmude, Marcel Salim, Nahya Azadzoy, Hila Bane, Mustafa Millen, Elizabeth O’Donnell, Lisa Bode, Philipp Türk, Ewelina Vaidya, Ria Gilbert, Stephen JMIR Res Protoc Protocol BACKGROUND: Low- and middle-income countries face difficulties in providing adequate health care. One of the reasons is a shortage of qualified health workers. Diagnostic decision support systems are designed to aid clinicians in their work and have the potential to mitigate pressure on health care systems. OBJECTIVE: The Artificial Intelligence–Based Assessment of Health Symptoms in Tanzania (AFYA) study will evaluate the potential of an English-language artificial intelligence–based prototype diagnostic decision support system for mid-level health care practitioners in a low- or middle-income setting. METHODS: This is an observational, prospective clinical study conducted in a busy Tanzanian district hospital. In addition to usual care visits, study participants will consult a mid-level health care practitioner, who will use a prototype diagnostic decision support system, and a study physician. The accuracy and comprehensiveness of the differential diagnosis provided by the diagnostic decision support system will be evaluated against a gold-standard differential diagnosis provided by an expert panel. RESULTS: Patient recruitment started in October 2021. Participants were recruited directly in the waiting room of the outpatient clinic at the hospital. Data collection will conclude in May 2022. Data analysis is planned to be finished by the end of June 2022. The results will be published in a peer-reviewed journal. CONCLUSIONS: Most diagnostic decision support systems have been developed and evaluated in high-income countries, but there is great potential for these systems to improve the delivery of health care in low- and middle-income countries. The findings of this real-patient study will provide insights based on the performance and usability of a prototype diagnostic decision support system in low- or middle-income countries. TRIAL REGISTRATION: ClinicalTrials.gov NCT04958577; http://clinicaltrials.gov/ct2/show/NCT04958577 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34298 JMIR Publications 2022-06-07 /pmc/articles/PMC9214611/ /pubmed/35671073 http://dx.doi.org/10.2196/34298 Text en ©Marcel Schmude, Nahya Salim, Hila Azadzoy, Mustafa Bane, Elizabeth Millen, Lisa O’Donnell, Philipp Bode, Ewelina Türk, Ria Vaidya, Stephen Gilbert. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 07.06.2022. https://creativecommons.org/licenses/by/4.0/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 use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included. |
spellingShingle | Protocol Schmude, Marcel Salim, Nahya Azadzoy, Hila Bane, Mustafa Millen, Elizabeth O’Donnell, Lisa Bode, Philipp Türk, Ewelina Vaidya, Ria Gilbert, Stephen Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study |
title | Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study |
title_full | Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study |
title_fullStr | Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study |
title_full_unstemmed | Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study |
title_short | Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study |
title_sort | investigating the potential for clinical decision support in sub-saharan africa with afya (artificial intelligence-based assessment of health symptoms in tanzania): protocol for a prospective, observational pilot study |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214611/ https://www.ncbi.nlm.nih.gov/pubmed/35671073 http://dx.doi.org/10.2196/34298 |
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