The findings from a Global Mapping of AI in CHW Programs across low- and middle-income countries by Nate Miller is very telling. He found 38 different AI systems to support community health workers, with a large majority (87%) of programs in sub-Saharan Africa and South Asia. Outside of those regions, there were 4 programs in Latin America and 2 in East Asia.
Subscribe Now for more digital health innovations
The data reveals a troubling pattern of fragmented, donor-driven experiments that seem to ignore fundamental questions about sustainability, governance, and local agency. Only 34% of these AI initiatives show any signs of scaling beyond pilot phases, creating a pilot purgatory – an endless cycle of promising demonstrations that never reach the community health workers who need them most.
This pilot purgatory isn’t accidental. It reflects systematic failures in how we design and fund digital health interventions.
Programs are launched without clear scaling plans, sustainable financing models, or integration strategies with existing health systems. Meanwhile, we continue pouring resources into redundant solutions rather than addressing the fundamental barriers to successful implementation.
Copilot AI Era Is Missing the Point
The most dominant trend in the dataset is the deployment of LLM-powered chatbots designed to act as always-on supervisors for community health workers. Tools like ASHABot in India, HealthVaani, and HEP Assist represent over 40% of analyzed implementations.
These systems promise to provide instant access to medical protocols and clinical guidance through conversational interfaces. However, research shows CHWs perceive AI applications to be infallible. CHWs often form incorrect mental models about how AI systems work, assuming they’re rule-based systems rather than probabilistic outputs.
Sadly, another chatbot isn’t what community health workers actually need .
ChatGPT training for frontline health workers shows the challenge isn’t knowledge access. Most CHWs already know the protocols. The real barriers are stockouts of essential medicines, broken referral systems, and inadequate supervision structures that no chatbot can fix.
When a CHW correctly diagnoses pneumonia using an AI assistant but has no amoxicillin to prescribe, the health outcome is zero. Yet the development sector continues funding WhatsApp chatbots for ASHAs rather than addressing supply chain predictive analytics or supervisory logistics where AI could have genuine impact.
Operations are the Missing Middle
AI applications for supply chain forecasting, workforce planning, and supervisory logistics are notably underrepresented. A missing middle of operational backbones that determines whether any health intervention succeeds or fails.
The development sector has a bias toward clinical solutions that photograph well in reports rather than unsexy operational improvements that actually save lives. An AI system that optimizes medicine distribution routes or predicts which CHWs are likely to drop out could have far greater impact than another diagnostic chatbot, but such applications don’t generate the same donor enthusiasm.
The preference for clinical applications also reveals how little we understand CHW workflows. Community health workers spend more time on administrative tasks, community mobilization, and supply management than on complex diagnostics. Yet our AI investments completely ignore these time-consuming activities that prevent CHWs from reaching more patients.
Three AI Applications for CHWs
While the development sector obsesses over diagnostic chatbots and clinical decision support, the AI applications with genuine transformative potential for community health workers remain tragically underfunded.
The AI for CHW mapping reveals a glaring gap: virtually no investment in supply chain optimization, workforce planning, or supervisory logistics—the operational backbone that determines whether any health intervention succeeds or fails.
1. Supply Chain Predictive Analytics
Consider this scenario: An AI system analyzes historical consumption patterns, seasonal disease trends, and transportation disruptions to predict exactly when each CHW will run out of oral rehydration salts. Instead of reactive stockouts that leave children dying of preventable diarrhea, the system triggers automated resupply orders and optimizes delivery routes to ensure continuous availability.
This isn’t science fiction. Retail giants use identical algorithms to manage inventory across thousands of stores. The technology exists. We’re just not applying it where it matters most. A predictive supply chain AI could eliminate the CHW visits that end with “sorry, no medicine available” responses.
2. Workforce Retention
Similarly, AI systems could analyze patterns in CHW performance data, supervision frequency, and compensation delays to predict which workers are likely to drop out before it happens. Early intervention, like additional training, adjusted workloads, or timely payments, could prevent the catastrophic loss of experienced CHWs who take years to replace.
The data already exists in most health information systems. We’re simply not using machine learning to identify warning signs before valuable human resources disappear.
3. Supervisory Route Optimization
CHW supervisors often spend more time traveling between sites than actually supporting workers. AI route optimization could reduce supervision costs by 40% while increasing visit frequency, ensuring struggling CHWs receive timely support rather than quarterly check-ins.
These operational AI applications lack the glamour of diagnostic tools, but they address the systemic failures that prevent existing interventions from reaching scale. A CHW with reliable supplies, adequate support, and optimized workflows can serve more patients than any diagnostic algorithm ever could.
Please tell Nate if you know of more AI-infused CHW initiatives!
