Optimizing Survey Creation With AI

Healthcare research relies heavily on templated surveys to generate market insights, but scaling that process across drugs, diseases, and therapy areas creates operational drag. Analysts often spend days searching through old documents, reworking templates, and fixing logic issues, slowing down the entire study pipeline.

We built an AI-enabled survey creation engine that centralizes domain knowledge, understands user intent, and assembles complete surveys automatically. The result is a workflow that moves from manual and inconsistent to fast, structured, and highly scalable.

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The Challenge

Fragmented Knowledge & Repetition

• Survey content lived in hundreds of disconnected Word files and SharePoint folders.
• No structured repository for drugs, diseases, or past templates.
• Analysts manually pieced together questions from old studies to build new ones.
• No metadata, tagging, or standardization across therapeutic areas.

Friction in Execution

• Copy-paste workflows caused logical breaks, inconsistent phrasing, and missing sections.
• Review cycles depended on senior subject-matter experts.
• A single survey required 3–5 days and 2–3 researchers to finalize.

Accuracy & Consistency Issues

• Different teams reused content with slight variations, reducing data comparability across markets.
• No version control or history tracking, making updates nearly impossible to manage.

Scaling Limitations

• As the business expanded into more therapy areas, the existing workflow couldn’t keep pace.
• Manual processes prevented teams from increasing output without increasing headcount.

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What We Did

We built a fully automated, knowledge-driven survey creation system that assembles complete healthcare surveys in minutes, safely and consistently, without the need for custom model training.

1. Built a Modern, Knowledge-Driven AI Architecture

• Designed a curated knowledge base in PostgreSQL, storing normalized data on drugs, diseases, survey formats, and question libraries.
• Added structured metadata across therapy areas to give the LLM precise, factual context during generation.
• Used FastAPI as the orchestration layer, combining context assembly, retrieval logic, and execution workflows.

How the model “learns”:
The system does not fine-tune or retrain LLMs. Instead, it uses the curated knowledge base + controlled prompt templates to “instruct” the model.
Better metadata → better outputs.
No weight updates. No self-learning. No training on user data.

2. Automated Template Retrieval & AI Assembly

• Mapped survey requests to the right format (ATU, CI Tracker, Agile ATU, etc.) using intent detection.
• Retrieved disease and drug-specific questions from the curated KB (Knowledge base).
• Combined structured question blocks with AI-driven refinements using AWS Bedrock (Claude) and Google Gemini Pro.
• Generated fully assembled surveys with validated logic, consistent tone, and clean flow.

Data sources:
All generated content comes from:

• the curated knowledge base
• question libraries
• template blocks
• and user instructions

No external or unverified data is used.

3. Enabled Conversational Refinement

• Analysts could refine any part of a survey using natural-language instructions (“Add three follow-up questions after Q10 on Celebrex usage”).
• The engine updated only the affected section while preserving numbering, structure, and branching logic.
• Reduced rework and allowed surveys to evolve iteratively without generating from scratch.

4. Added Multi-Layer Guardrails for Safety & Consistency

To keep generations accurate, safe, and structurally sound, the system uses four layers of guardrails:

a. Provider-Level Guardrails

AWS Bedrock and Google Gemini enforce baseline safety:

• harmful content filters
• hallucination reduction
• medical-safety constraints

b. Prompt Guardrails

All LLM calls use strict templates that:

• limit the model’s freedom
• constrain tone, sections, and question style
• enforce medical accuracy through injected metadata

c. Structural Guardrails

The system enforces:

• strict Markdown → DOCX formatting
• section and numbering stability
• consistent headers
• validated logic flow
• scoped edits during refinements

d. Knowledge Guardrails

LLMs only use:

• curated questions
• validated drug/disease metadata
• approved survey blocks

Nothing outside the KB can influence generation.

5. Designed a Scalable, Cloud-Native Infrastructure

• Deployed using AWS Lambda, API Gateway, S3, and DynamoDB for highly available workloads.
• Integrated Langfuse for LLM tracing, cost monitoring, and quality evaluation.
• Adopted a provider-pattern orchestration layer so the system can switch between Claude and Gemini seamlessly.

6. Delivered a Full End-to-End Output Pipeline

• Generated surveys follow a strict Markdown → DOCX export pipeline for clean, client-ready formatting.
• Saved outputs to S3 and logged metadata (drug, disease, template type, question count) back into PostgreSQL.
• Enabled versioning and rapid regeneration without manual formatting work.

7. Built for Evolution

• The system does not self-train or update weights.
• It gets better as the knowledge base and prompt templates evolve.
• New diseases, drugs, or question libraries can be added without touching the underlying AI model.

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Enterprise-Grade Complexity

This project required building an AI system that could reliably handle:

• Hundreds of question blocks across therapy areas.
• Multiple survey formats with unique structures and logic requirements.
• Intent detection covering drug names, disease areas, research types, and follow-up instructions.
• Multi-model inference with fallback logic across Claude and Gemini.
• Transaction-safe metadata logging and catalog evolution over time.

The system was engineered for long-term growth, allowing new diseases, drugs, and survey formats to be added without developer involvement.

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Impact

Team Efficiency

• Survey build time dropped from 3–5 days to 30 minutes.
• Manual editing reduced by 90%, eliminating copy-paste errors and inconsistency.

Output Capacity

• Enabled 10× more surveys per quarter with the same team size.
• Analysts could generate complete studies independently, reducing SME bottlenecks.

Quality & Consistency

• Template and phrasing consistency improved by ~80% based on internal QA sampling.
• Centralized knowledge ensured aligned tone and logic across diseases and markets.

Operational Reliability

• Survey creation no longer depended on specific experts or manual formatting.
• Updates to drug or disease information cascade globally across all future builds.

Delivery Speed

• Delivered from POC to production in just 8 weeks, accelerating time-to-value and enabling rapid adoption across research teams.

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Strategic Outcome

The AI-enabled survey engine transformed the client’s research operations from a slow, manual workflow into an automated, scalable system. By centralizing knowledge and introducing AI-driven assembly, the team can now support more therapeutic areas, deliver faster insights, and scale output without additional headcount.

This foundation positions the organization for future advancements like automated analytics, predictive study design, and deeper integration with enterprise systems.