Premera — Scope of Work

Last updated: 2025-02-10 Source: Near-final proposal draft (not yet sent/confirmed with Premera, but expected to land close to this)

Summary

We partner with Premera to reduce manual utilization review time by generating structured medical chart summaries, extracting UM-relevant flags, automating key sub-workstreams, and providing usage analytics. We integrate with Premera's data sources (EMR, secure file share, PDF packets, HL7 feeds), align outputs with their nursing workflow templates, and deliver a production-ready HIPAA-aligned application with customized development for integration and template work.

Objectives

Reduce nurse review time → allocate towards higher-value activities
Surface UM-specific flags (start of service timing, critical diagnoses, queue priorities) → proactive management of high-risk cases
Structured summaries mapped to Premera's templates → cross-team consistency and quality
Low-friction data ingestion from hospital systems / Premera intake channels → better data integration, improved hospital communication
Analytics on usage, throughput, cost → C-level visibility, management-level ops control

In Scope

Clinical Chart Summaries. Structured summaries from PDFs and other clinical artifacts
Data Ingestion. Connect to agreed sources (Premera-hosted data mgmt, PDF packets, secure file share, HL7 feeds — ADT, ORU, CCDA)
Template Alignment. Map outputs to Premera's existing summary templates and QA expectations
Analytics. Dashboard: volume, processing time, usage cost
Integration. Training, support, iterative tuning with nurses and executive team

Out of Scope

Formal integration of proprietary criteria engines (MCG/InterQual)
Automated clinical determinations without human review

Deliverables

Discovery Summary: Workflow map, data sources, feature requests
UM Flag Specification: Written list of UM flags + extraction logic
Technical Brief: Agent architecture, skill orchestration, technical logic
Summary Template Implementation: Output matched to Premera's template
Integration: Live data ingestion from approved sources
Dashboard: Usage + throughput analytics
Training: 1-2 sessions for reviewers and administrators

Responsibilities

Daisy AI

AI-driven workflow for drafting/supporting clinical reviews (human-in-the-loop)
Summary and UM flag extraction based on Premera input
Build and maintain ingestion pipeline(s)
Analytics and admin visibility into usage/costs
Onboarding, training, success review

Premera

Nurse SME availability — for tuning, discovery, UM flag definition
Review templates — existing summary template and QA checklist
Hospital coordination — data source access (EMR integrations, file sharing)
Feedback loop — validate outputs, provide iterative feedback, participate in recurring meetings

Data Access and Security

HIPAA-aligned data processing
Secure data transfer method approved by Premera
Daisy AI provides security deck and standard controls for review
BAA with Premera with detailed provisions on acceptable data use and access

Timeline (Draft)

Phase	Weeks	Activities
0: Contracting & Security	0-4	Exchange security materials, finalize SOW, confirm data access
1: Discovery & Setup	4-8	Nurse interviews, UM flag definitions, template mapping, test data
2: Integration & Launch	8-12	Data ingestion live, initial review runs, QA feedback loop
3: Evaluation & Scale	12+	KPI review, time savings, quality, roadmap for scale

Exact dates TBD after onboarding and access approvals.

Success Criteria

Metric	Target	Measurement
Time savings	>50% reduction in summary prep	Baseline vs. tracking (20 min → 10 min)
Template fit	Usable with <2 min edits	Nurse feedback survey
UM flag accuracy	>85% reviewer agreement	Spot check sample
Nurse satisfaction	>8/10 post-training	Survey
Security	Pass review	Architecture walkthrough + questionnaire

Discussion Topics (for working session)

Discovery: End-to-end UM workflow, current tech stack, ADT feed architecture, past deployment failures Technical: Where to start among ~12 initiatives, data requirements, ownership preferences, scope/responsibility split Business: Regulatory/compliance gates, AI governance, timeline expectations, success definition, commercial terms

Outstanding Questions for Premera

UM Workflow: Org structure, key contacts, KPIs, teams, criteria sets, vendors, tech systems, lingo, case routing, bottlenecks (initial reviews vs. P2P vs. appeals) Tech Stack: Dev standards, platform vendors, approval process, build-in-environment preference, languages/frameworks, AI vendor relationships, existing AI infra, EMR integrations Project Management: POC, update communication, milestone cadence Commercial: Contract formality needed, diligence requirements, mutual assurances Forward Looking: Broader UM transformation alignment

Our Tech Stack (as presented)

Core: Next.js 15, React 19, TypeScript, PostgreSQL (Neon), Drizzle ORM AI: Claude Sonnet 4.5 / Opus 4.5 (primary), GPT-5.2 / GPT-4o (secondary), Vercel AI SDK, specialized agents with Zod-validated schemas Security: Clerk (mandatory MFA), HIPAA-aligned architecture (row-level security, audit logging, PHI isolation), Neon branch separation (prod PHI never touches dev) Infrastructure: Vercel (hosting, edge, preview deploys), Stripe (billing) Data Ingestion: HL7 v2 parser (ADT/ORU), CCDA/CDA processing, PDF/DOCX extraction UI: Tailwind CSS v4, shadcn/ui, Radix UI, MUI DataGrid, resizable multi-pane layout

Premera Blue Cross