Beyond the Impact Tracker: The Next Wave of AI Applications

We have mastered retrospective analysis. This document outlines the next three strategic AI initiatives to achieve proactive, real-time, and population-level impact.

Back to Documentation

The Strategic Shift: Your AI is currently a historian, brilliantly documenting the past. It is time to promote it to a Co-Pilot, a Supervisor, and an Epidemiologist, actively shaping the future of our program in real-time.

Initiative 1: The AI Co-Pilot (Dynamic Conversation Guidance)

[Chief Technology Officer & Head of Operations Hats]

The Problem: Your agents are managing immense cognitive load during a live call—they must listen with empathy, recall case history, follow protocols, and type notes simultaneously. This is where mistakes or missed opportunities happen.

The Solution: Create an AI-powered "Co-Pilot" panel within our web application that assists the agent *during* the live call.

Key Features:

  1. Post-Call Automation Suite:
    • How it works: After a call ends, the agent clicks a "Generate Summary" button. The AI reads the raw notes they just typed and instantly produces a clean, structured summary for the official record.
    • Why it's a game-changer: This saves each agent 3-5 minutes per call, massively increasing their capacity and reducing documentation burnout. It also standardizes the quality of notes across all agents.
  2. Dynamic "Next Best Question" Suggester:
    • How it works: Based on the patient's risk profile and the notes being typed *in real-time*, the AI Co-Pilot suggests the most critical next question. For example, if the agent types "patient is feeling tired," the AI might suggest: "Ask her to describe the tiredness. Is it accompanied by shortness of breath or dizziness?"
    • Why it's a game-changer: This turns every agent into our best clinical investigator, ensuring they probe deeper on subtle symptoms that could indicate a serious underlying condition.
  3. On-Demand Knowledge Base:
    • How it works: If a patient asks a complex question ("What should I eat for my gestational diabetes?"), the agent can type the query into the Co-Pilot. The AI provides a concise, program-approved answer based on our training manuals.
    • Why it's a game-changer: This ensures every patient receives consistent, high-quality advice and empowers agents to answer questions confidently without putting the call on hold.

Initiative 2: The AI Supervisor (Automated Quality & Performance Auditing)

[Chief Quality Officer & COO Hats]

The Problem: A manager can only manually review a tiny fraction of calls. They have no systematic way to ensure quality, protocol adherence, and agent empathy across their entire team.

The Solution: Create a new, automated script that runs nightly, using Gemini to "read" 100% of the previous day's call notes and generate a Quality & Performance dashboard for managers.

The AI Supervisor's Nightly Analysis Prompt:

"We are a Call Center Quality Supervisor. For the following call note, score it from 1-10 on these three dimensions and provide a 1-sentence justification for each score.
1. Protocol Adherence: Did the agent address the primary high-risk flags for this patient?
2. Note Quality: Is the note clear, concise, and does it document a clear action plan?
3. Empathetic Language (Experimental): Does the language used in the notes suggest an empathetic, patient-centric conversation?

Return ONLY a valid JSON object: `{\"protocol_score\": 8, \"protocol_justification\": \"...\", \"quality_score\": 9, ...}`"

Managerial Dashboard Features:

Why it's a game-changer: This transforms quality assurance from a subjective, random process into an objective, comprehensive, and data-driven system for continuous improvement. It allows managers to focus their limited time on coaching the agents who need it most.

Initiative 3: The AI Epidemiologist (Population-Level Health Surveillance)

[Chief Strategy Officer & Public Health Analyst Hats]

The Problem: Your call logs contain a real-time stream of public health data from the ground, but it's unstructured. We can't see the "forest for the trees."

The Solution: Create a new strategic AI tool that scans all incoming call notes in aggregate to identify emerging trends, outbreaks, and systemic barriers at a population level.

The AI Epidemiologist's Quarterly Analysis Prompt:

"We are a Public Health Epidemiologist. We have been given 10,000 call notes from the last quarter. Analyze them in aggregate to answer the following:
1. Emerging Health Trends: Are there any unusual spikes in keywords like 'fever,' 'diarrhea,' or 'breathing difficulty' in specific geographic clusters (villages/districts)?
2. Systemic Barrier Analysis: What are the top 3 most frequently mentioned reasons for missing an ANC appointment (e.g., 'no transport,' 'could not afford,' 'facility was closed')?
3. Intervention Effectiveness Hypothesis: Is there a correlation between agents who mention 'counseling on diet for anemia' and patients who show an improvement in their 'Latest HB'?

Provide a concise summary report."

Strategic Outputs:

Why it's a game-changer: This elevates our organization from a service provider to a vital Public Health Intelligence Partner. It gives we a seat at the policy-making table and unlocks new funding streams related to public health surveillance and research.

Explore Related Documentation

Dive deeper into system features and user guides