In global health, communication is everything. Whether it’s reminding a parent to give a child malaria medicine, guiding a community health worker through triage, or helping a ministry monitor national programs – the right message, at the right time, can save lives.

But traditional tools are blunt. SMS blasts are generic. Health apps often sit unused. Data systems record events, but rarely guide action. What’s missing is an intelligence layer – one that can adapt, personalize, and orchestrate millions of interactions at once.

This is where the Transformer architecture – the engine behind today’s most powerful AI – enters the picture.

Just as Social and Behavior Change Communication (SBCC) organizes messaging to shift health behaviors, transformer AI models organize information to generate meaningful outputs.

And at DPE, we’re bringing these two together to drive a bold mission: 10 billion trusted health messages delivered to the people who need them most in the next 5 years.

Understanding Transformers (Without the Jargon)

Imagine a classroom. A student asks a question. The teacher recalls past lessons, decides which parts are most relevant, and crafts an answer. That’s exactly what transformers do.

• Query (Q): The new question.
• Key (K): Past lessons, stored context.
• Value (V): The knowledge that might answer the question.

The model calculates: Which past context matters most for this new question? That’s the attention mechanism.

This cycle – Q, K, V – is the engine behind everything from chatbots to translation. In math form, it looks daunting. In practice, it’s a disciplined way of paying selective attention.

From SMS to Smart Care: Health Use Cases

SBCC has always been about attention too. Campaigns don’t just blast information – they frame it carefully, target it strategically, and reinforce it consistently.

So what does this mean for public health? Imagine three frontline scenarios:

Behavior Change Messaging (SMS)

• Old way: “Remember malaria meds.”
• Transformer way: “Your child’s next malaria dose is due in 8 hours.”

Chatbot Support

• Old way: Rule-based scripts, often breaking when users go off-path.
• Transformer way: Adaptive, multilingual assistants that recognize intent, context, and cultural nuance.

Feedback Loops

• Old way: 5% of citizen reports ever reach decision-makers.
• Transformer way: Automated synthesis of thousands of frontline voices into actionable insights.

Before, During, After: SBCC Meets Transformers

A Transformer is made up of repeating blocks. Each block is like a mental muscle. Together, they allow the system to:

1. Normalize: Take a deep breath so nothing gets out of proportion.
2. Embed time and space: Remember when and where something happened.
3. Pay attention: Look at information from multiple angles at once.
4. Combine logic and intuition: Mix different signals into one insight.
5. Decide: Produce the next best action, whether that’s a word, a number, or a health message.

This is not just computer science, it’s a design pattern we already see in SBCC practice. Normalize = address biases and stigma. Embed = place messages in cultural and social context. Pay attention = tailor campaigns to different groups. Combine = integrate data and lived experience. Decide = act, at scale.

SBCC professionals use a Before-During-After framework to guide campaigns. Surprisingly, it maps beautifully to the functioning of transformer architectures.

Before (Preparation)

• SBCC: Advocate for supportive policies, craft protocols, develop strategies, counter misinformation.
• Transformers architecture: Pre-layer normalization, embeddings, and context setup – laying the groundwork before the model “pays attention.”

During (Execution)

• SBCC: Outreach, community champions, job aids, campaigns in motion.
• Transformers architecture: Attention layers calculate which words, facts, or histories matter most, filtering noise from signal.

After (Sustain & Measure)

• SBCC: Indicators, M&E frameworks, reinforcing supportive norms.
• Transformers architecture: Outputs pass through residual layers and normalization, refining answers, ready for feedback and improvement.

Both systems thrive on feedback loops. In SBCC, we call that community evaluation. In LLMs and transformers, we call it training and fine-tuning.

Why This Powers Frontline-Aware AI™

Here’s the crucial connection: the very features that make Transformers powerful in language prediction also make them perfect for healthcare at the edge.

• Attention enables personalization across millions of SMS or WhatsApp conversations.
• Context embedding ties every message back to policy, clinic guidelines, and household realities.
• Decision layers drive next best actions for patients, caregivers, or health workers.

At DPE, we call this Frontline-Aware AI™. It’s our strategy to move beyond “AI in the cloud” toward AI that understands and adapts to frontline realities – from low-resource clinics to households receiving life-saving reminders. It’s not just about accuracy; it’s about alignment, trust, and impact.

And this is why we have built Interch™ to bridge these. And our modular product suite (InfoAFYA™, Signal™, etc.) ensures each piece strengthens the next – the “Better Together” strategy.

Bold Mission, Grounded Execution

So why talk about transformers in the context of SBCC? Because it reframes what’s possible.

• Behavior science integration: Like queries, they help us frame the right question and context.
• SMS generation: Attention-driven personalization means more effective nudges.
• Chatbot support: Contextual memory ensures people feel heard and guided, not just “replied to.”

Every cycle of attention, every campaign iteration, brings us closer to our north star: 10B health messages delivered safely, inclusively, and impactfully.

Transformers and SBCC may seem like odd companions. One is math-heavy, the other human-centered. But when put together, they unlock something rare: precision with empathy.

And that is why we are aggressively betting on Large Language Models in SBCC. We believe they hold the key in getting people to receive the right health message, at the right time, in the right way, saving lives as a result.