Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app

O’Donovan, J; Kahn, K; MacRae, M; Namanda, AS; Hamala, R; Kabali, K; Geniets, A; Lakati, A; Mbae, SM; Winters, N

AI Collection

Journal article

Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app

Abstract:: DATA AVAILABILITY : No data was used for the research described in the article.The widespread adoption of artificial intelligence (AI) technology globally has brought significant changes to various sectors. AI-assisted algorithms have notably improved decision-making, operational efficiency, and productivity, especially in healthcare and medicine. However, in low and middle-income countries (LMICs), particularly in sub-Saharan Africa (SSA), the integration of medical AI has faced delays and challenges, slowing its acceptance and implementation in medical interventions. This thematic narrative critically explores the current trends and patterns in applying medical AI in SSA, with a specific focus on its potential impact on medical laboratories. The review covers the general use of medical AI in SSA, examining factors like enablers, challenges, and opportunities that influence healthcare systems. Additionally, it looks into the implications of medical AI for medical laboratories and suggests context-specific and practical recommendations for potential integration. We highlight various challenges, including data availability, security concerns, resource limitations, regulatory gaps, poor internet connectivity, and digital literacy issues, contributing to the slow integration of AI in healthcare systems in SSA. Despite challenges, the adoption of medical AI in SSA medical laboratories holds latent potential for improving diagnostic accuracy, streamlining workflows, and enhancing patient care. Further exploration and careful consideration are necessary to unlock these possibilities.https://www.elsevier.com/locate/smhlhj2024School of Health Systems and Public Health (SHSPH)SDG-03:Good heatlh and well-beingSDG-09: Industry, innovation and infrastructur

Publication status:: Published

Peer review status:: Peer reviewed

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Share
Cite

Cite this record

APA Style

O’Donovan, J., Kahn, K., MacRae, M., Namanda, A. S., Hamala, R., Kabali, K., Geniets, A., Lakati, A., Mbae, S. M., & Winters, N. (2022). Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app. Human Resources for Health, 20(1), 6–6.

MLA Style

O’Donovan, J, et al. “Analysing 3429 Digital Supervisory Interactions between Community Health Workers in Uganda and Kenya: the Development, Testing and Validation of an Open Access Predictive Machine Learning Web App.” Human Resources for Health, vol. 20, no. 1, 2022, pp. 6–6.

Chicago Style

O’Donovan, J, K Kahn, M MacRae, et al. 2022. “Analysing 3429 Digital Supervisory Interactions between Community Health Workers in Uganda and Kenya: the Development, Testing and Validation of an Open Access Predictive Machine Learning Web App.” Human Resources for Health 20 (1): 6–6.
Print