AI & Workflow Modernization Audit
Why This Sprint Exists
Many healthcare companies are “AI-ready” in vision, but not in workflow or data.
Common challenges:
Manual clinical tasks slow throughput
Data is scattered across PDFs, CCDs, HL7, or free text
AI pilots stall because the workflow doesn’t support them
Clinicians experience burden instead of relief
The roadmap doesn’t reflect what’s technically feasible
Integrations are brittle or inconsistent
This audit gives you a clear, practical modernization plan rooted in both clinical reality and technical feasibility.
Who is this for?
Platforms seeking AI automation opportunities
Teams moving from manual → automated workflows
Companies with data ingestion challenges
Healthtech startups wanting to use RWD operationally
Teams already building AI but lacking structure or direction
If “AI” feels exciting but the path feels unclear, this is for you.
What’s Included
1. Workflow Mapping & Burden Analysis
Where clinicians, nurses, or coders spend time — and what can be automated, simplified, or redesigned.
2. Data & Integration Readiness Review
Deep analysis of:
HL7/FHIR structures
CCDs, PDFs, unstructured content
API and ingestion patterns
Data quality
Gaps preventing automation
3. AI Feasibility & Sequencing
NLP, coding automation, intake, triage, documentation, RWD pipelines —
evaluated by value, effort, and clinical implications.
4. Technical Constraints & Realistic Paths Forward
What’s possible today and what needs to change.
5. Modernization Roadmap
A focused 6–12 month plan that ties together:
automation
workflow redesign
engineering effort
data prep
product sequencing
clinical implications
Outcomes
You walk away with:
A clear automation strategy
A realistic AI roadmap
Reduced burden for clinicians and ops
Faster throughput and lower manual steps
Clear data readiness requirements
A modernization plan engineering can execute
Modernization only works when technical feasibility meets clinical reality.
This audit ties both together.