Notes from inside the data.
Practical writing on healthcare data modernization, Epic analytics, reporting, and data leadership. From someone who has done the work. Not just advised on it.
Off legacy, onto a platform
you can trust.
Seven notes on the move off on-prem and onto a governed lakehouse with sub-second serving. Where the trade-offs are. How to sequence the migration. What changes when the data is federally-funded.
Epic Clarity on Oracle: getting ahead of the migration
A migration off Oracle is also a once-a-decade chance to modernize. How to use it instead of just surviving it.
Read →Fabric, Databricks, or Snowflake for Epic data
There is no universally right platform, only the right one for your team and roadmap. A practitioner's honest comparison.
Read →Modernizing Epic data for federal health agencies
Where Epic actually lives in federally-funded healthcare. FedRAMP High lakehouse patterns, NIST SP 800-171, and the Clarity migration question under federal-adjacent constraints.
Read →Beyond Snowflake: choosing the right cloud for your Epic data
Moving Epic data to the cloud isn't picking a database. It's picking a long-term partner. The three most common patterns.
Read →The hidden cost of undocumented data warehouses
An undocumented warehouse is a liability that grows more expensive every day. How to stop the bleeding.
Read →Why your Epic metrics might be lying to you
Data-driven is only as good as your timestamps. Why order time versus event time quietly breaks every downstream number.
Read →Power BI in healthcare: 5 patterns that actually work
Leaders don't need more data. They need a clear path to a decision. Five patterns that have proven to stick.
Read →Compliance, solved
as a data problem.
Six notes on continuous survey readiness, the analytics under it, and the manual work it replaces. The Joint Commission overhaul, mock-survey blind spots, and the $7.6M compliance burden.
What a real compliance dashboard looks like
Most fail because BI teams don't know the regulations and compliance teams can't build analytics. What an effective one requires.
Read →What continuous survey readiness actually requires
Always-on readiness isn't a mindset. It's a data-infrastructure capability. The checklist your health system needs.
Read →Accreditation 360 and the end of cyclical survey prep
The Joint Commission overhaul means every hospital must remap. Why continuous readiness analytics is the missing layer.
Read →Why mock surveys fail without analytics
Mock surveys tell you where you stand, not why or how to fix it. What data-driven survey readiness looks like.
Read →Leapfrog grade improvement: closing the gap from C to A
When grades stall despite operational effort, the root cause is a data problem. How EHR analytics changes the trajectory.
Read →The $7.6 million compliance burden
Hospitals spend $7.6M and 59 FTEs a year on compliance, mostly manual data work. Where it goes, and how to reduce it.
Read →Hiring the role
that builds the team.
One note on why most first-CDO hires fail, and what health systems get wrong about the role.
How this site
got built.
One note on the methodology behind dadosconsulting.com. Two weeks, 27 pages, Lighthouse 98, Mozilla A+. Plus the actual Claude prompt we used, free to copy and run on your own business.
How we built this site with Claude. The prompt is free.
Six structured briefing rounds. A generated BRAND.md style document. The methodology behind dadosconsulting.com, captured in a single prompt. What Claude does well, what it can't do, and why the BRAND.md file is the entire trick.
Read →The prompt itself, on its own page
Skip the post. Just want the working asset? The prompt lives on its own page with a copy button. ~480 lines, copy-paste into any Claude conversation. No email gate.
Open the prompt →Stop reading. Start fixing.
The notes are the half-hour version. Twenty minutes to map your reporting and the fastest, lowest-risk path to numbers your team can trust. Not a pitch. References on request.