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Case Study: Compliance and Policy Information Search Bot at a 500-Bed
Health System
Scalefresh built an internal AI knowledge assistant to eliminate the hours our clinical associates spent hunting through policy manuals and SharePoint folders. The result: a system that answers policy questions in seconds, traces every response to its source document, and cut new hire onboarding time nearly in half.
01.
Problem
We needed to support 500+ clinical associates across multiple specialties searching for information while managing constant policy updates, high staff turnover, and strict compliance requirements.
Traditional document search was slow, hard to use and the manual knowledge access process was disruptive and not scalable: nurses and medical assistants interrupted supervisors 10-15 times per day with basic policy questions, new hires took 4-6 weeks to learn organizational protocols, and outdated information in circulation created compliance exposure.
01.
Problem
We needed to support 500+ clinical associates across multiple specialties searching for information while managing constant policy updates, high staff turnover, and strict compliance requirements.
Traditional document search was slow, hard to use and the manual knowledge access process was disruptive and not scalable: nurses and medical assistants interrupted supervisors 10-15 times per day with basic policy questions, new hires took 4-6 weeks to learn organizational protocols, and outdated information in circulation created compliance exposure.
02.
Solution
The system is built on a Retrieval-Augmented Generation (RAG) pipeline that combines semantic search with LLM-powered synthesis.
Ingestion: PDF → PyMuPDF extraction → sentence-aware chunking (1,000 chars, 150 overlap) → Azure OpenAI embeddings (1,536-dim vectors) → ChromaDB storage with metadata (specialty, document type)
Retrieval: User query → embedding generation → semantic search → metadata filtering by specialty/type
Generation: Azure OpenAI GPT-4 synthesizes answers with source citations and conversation memory
RAG ensures every answer is grounded in the organization's actual policy documents rather than relying on the LLM's training data, eliminating hallucinations and maintaining compliance accuracy.
The AI knowledge assistant keeps clinical associates focused on patient care: (1) Natural language search retrieves answers from policies, procedures, and clinical guidelines in seconds; (2) Source attribution automatically references the exact document, page, and specialty for every response; (3) Conversation memory allows follow-up questions without re-establishing context.
Models used: Azure OpenAI GPT-4, text-embedding-ada-002
Tools used: Qdrant, ChromaDB, PyMuPDF, Python REST API integration
02.
Solution
The system is built on a Retrieval-Augmented Generation (RAG) pipeline that combines semantic search with LLM-powered synthesis.
Ingestion: PDF → PyMuPDF extraction → sentence-aware chunking (1,000 chars, 150 overlap) → Azure OpenAI embeddings (1,536-dim vectors) → ChromaDB storage with metadata (specialty, document type)
Retrieval: User query → embedding generation → semantic search → metadata filtering by specialty/type
Generation: Azure OpenAI GPT-4 synthesizes answers with source citations and conversation memory
RAG ensures every answer is grounded in the organization's actual policy documents rather than relying on the LLM's training data, eliminating hallucinations and maintaining compliance accuracy.
The AI knowledge assistant keeps clinical associates focused on patient care: (1) Natural language search retrieves answers from policies, procedures, and clinical guidelines in seconds; (2) Source attribution automatically references the exact document, page, and specialty for every response; (3) Conversation memory allows follow-up questions without re-establishing context.
Models used: Azure OpenAI GPT-4, text-embedding-ada-002
Tools used: Qdrant, ChromaDB, PyMuPDF, Python REST API integration
03.
Impact
The system reduced new hire onboarding time by 40-60%, answering policy questions in seconds rather than requiring supervisor interruptions or manual document searches.
Clinical associates now access current, approved policies instantly, improving both compliance posture and knowledge retention through interactive Q&A rather than passive document reading.
03.
Impact
The system reduced new hire onboarding time by 40-60%, answering policy questions in seconds rather than requiring supervisor interruptions or manual document searches.
Clinical associates now access current, approved policies instantly, improving both compliance posture and knowledge retention through interactive Q&A rather than passive document reading.
Case Study: Recovering 14 Hours of Physician Time Per Week at a High-Volume
Specialty Practice
Scalefresh built an agentic prior authorization system that autonomously handles the documentation, submission, and tracking work that consumes a physician's week. The result—a multi-agent system that determines authorization requirements, drafts clinical justification, and submits requests without manual intervention, while keeping physicians in control of every final decision.
01.
Problem
We needed to support 500+ clinical associates across multiple specialties searching for information while managing constant policy updates, high staff turnover, and strict compliance requirements.
The manual process couldn't keep pace: staff faxed documents, waited on hold with payers, and tracked approvals through spreadsheets and sticky notes. Every delay in the PA queue meant delayed patient care, sometimes by days or weeks.
01.
Problem
We needed to support 500+ clinical associates across multiple specialties searching for information while managing constant policy updates, high staff turnover, and strict compliance requirements.
The manual process couldn't keep pace: staff faxed documents, waited on hold with payers, and tracked approvals through spreadsheets and sticky notes. Every delay in the PA queue meant delayed patient care, sometimes by days or weeks.
02.
Solution
The prior authorization agent keeps physicians focused on patients:
Automated requirements analysis checks patient data, insurance policies, and medical orders to determine whether authorization is needed before anyone picks up a phone;
Clinical justification drafting generates payer-ready documentation in seconds, pulling from the patient record;
Azure OpenAI GPT-4 synthesizes answers with source citations and conversation memory
Physicians review exceptions and sign off on final submissions. Everything else runs autonomously.
Models used: Azure OpenAI GPT-3.5 Turbo
Tools used: Streamlit, PostgreSQL, Langfuse, Synthea
02.
Solution
The prior authorization agent keeps physicians focused on patients:
Automated requirements analysis checks patient data, insurance policies, and medical orders to determine whether authorization is needed before anyone picks up a phone;
Clinical justification drafting generates payer-ready documentation in seconds, pulling from the patient record;
Azure OpenAI GPT-4 synthesizes answers with source citations and conversation memory
Physicians review exceptions and sign off on final submissions. Everything else runs autonomously.
Models used: Azure OpenAI GPT-3.5 Turbo
Tools used: Streamlit, PostgreSQL, Langfuse, Synthea
03.
Impact
The system recovers 14+ hours of physician time per week per practice, eliminating the fax queues, hold times, and manual tracking that consume clinical staff capacity.
By catching authorization requirements before submission and drafting accurate clinical justification upfront, the system reduces denial rates and removes the rework cycle that delays patient access to care.
03.
Impact
The system recovers 14+ hours of physician time per week per practice, eliminating the fax queues, hold times, and manual tracking that consume clinical staff capacity.
By catching authorization requirements before submission and drafting accurate clinical justification upfront, the system reduces denial rates and removes the rework cycle that delays patient access to care.