Artificial intelligence and digital health in improving primary health care service delivery in LMICs: A systematic review.

Journal: Journal of evidence-based medicine

Volume: 16

Issue: 3

Year of Publication: 

Affiliated Institutions:  College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland. School of Public Health, University of Queensland, Brisbane, Australia. Department of Psychology, Maynooth University, Kildare, Ireland. Sacred Heart Hospital, Abeokuta, Ogun State, Nigeria. Laboratory of Gut-Brain Signaling, Laboratory Sciences and Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh. Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada. Department of Operations, Marie Stopes International, Kathmandu, Nepal. General Hospital of Haji Padjonga, South Sulawesi, Indonesia. Department of Mother and Child Health, Ministry of Health, Lusaka, Zambia. Lewanika School of Nursing and Midwifery, Ministry of Health, Mongu, Zambia. SingHealth Duke-NUS Global Health Institute, National University of Singapore, Singapore.

Abstract summary 

Technology including artificial intelligence (AI) may play a key role to strengthen primary health care services in resource-poor settings. This systematic review aims to explore the evidence on the use of AI and digital health in improving primary health care service delivery.Three electronic databases were searched using a comprehensive search strategy without providing any restriction in June 2023. Retrieved articles were screened independently using the "Rayyan" software. Data extraction and quality assessment were conducted independently by two review authors. A narrative synthesis of the included interventions was conducted.A total of 4596 articles were screened, and finally, 48 articles were included from 21 different countries published between 2013 and 2021. The main focus of the included studies was noncommunicable diseases (n = 15), maternal and child health care (n = 11), primary care (n = 8), infectious diseases including tuberculosis, leprosy, and HIV (n = 7), and mental health (n = 6). Included studies considered interventions using AI, and digital health of which mobile-phone-based interventions were prominent. m-health interventions were well adopted and easy to use and improved the record-keeping, service deliver, and patient satisfaction.AI and the application of digital technologies improve primary health care service delivery in resource-poor settings in various ways. However, in most of the cases, the application of AI and digital health is implemented through m-health. There is a great scope to conduct further research exploring the interventions on a large scale.

Authors & Co-authors:  Saif-Ur-Rahman K M KM Islam Md Shariful MS Alaboson Joan J Ola Oluwadara O Hasan Imran I Islam Nazmul N Mainali Shristi S Martina Tina T Silenga Eva E Muyangana Mubita M Joarder Taufique T

Study Outcome 

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Statistics
Citations :  Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Global Health. 2018;3(4):e000798.
Authors :  11
Identifiers
Doi : 10.1111/jebm.12547
SSN : 1756-5391
Study Population
Male,Female
Mesh Terms
Other Terms
LMICs;artificial intelligence;digital health;primary health care
Study Design
Study Approach
Country of Study
Publication Country
England