Location
Kumasi, Ghana & Virtual
Start Date
6-8-2026 5:00 PM
End Date
6-8-2026 5:30 PM
Description
Limited labor capacity, disjointed information flows and delays in clinical decision-making are among the ongoing issues that primary health care (PHC) systems in resource-constrained settings must deal with. These issues limit service delivery and impede efforts to undergo digital transformation. This study investigates how improved decision support at the primary care level can facilitate incremental digital transformation through human-in-the-loop (HITL) artificial intelligence (AI). In order to identify system gaps, design a context-sensitive HITL AI prototype and assess its viability, usability and integration within standard PHC workflows, the study employs a design-based implementation research approach that incorporates qualitative interviews, workflow observations and quantitative readiness assessments. According to preliminary research, when integrated with current procedures and bolstered by focused capacity training, low-complexity, human-centered AI technologies can enhance digital capabilities and improve frontline decision-making. The study offers useful insights for using HITL AI in settings with limited resources and offers a scalable, phased strategy to digital transformation in healthcare.
Included in
Health Information Technology Commons, Other Engineering Commons, Systems Science Commons
HITL AI for Digital Transformation in Healthcare
Kumasi, Ghana & Virtual
Limited labor capacity, disjointed information flows and delays in clinical decision-making are among the ongoing issues that primary health care (PHC) systems in resource-constrained settings must deal with. These issues limit service delivery and impede efforts to undergo digital transformation. This study investigates how improved decision support at the primary care level can facilitate incremental digital transformation through human-in-the-loop (HITL) artificial intelligence (AI). In order to identify system gaps, design a context-sensitive HITL AI prototype and assess its viability, usability and integration within standard PHC workflows, the study employs a design-based implementation research approach that incorporates qualitative interviews, workflow observations and quantitative readiness assessments. According to preliminary research, when integrated with current procedures and bolstered by focused capacity training, low-complexity, human-centered AI technologies can enhance digital capabilities and improve frontline decision-making. The study offers useful insights for using HITL AI in settings with limited resources and offers a scalable, phased strategy to digital transformation in healthcare.
