Power Platform
Power Automate
Workflow and integration. Moves data between systems, routes approvals and removes the manual re-keying that rarely appears in anyone's job description.
Healthcare · Modern Work & Automation
Twenty million records a year, processed without new headcount
A national acute care provider group, United States. Medical records arrived from a wide variety of providers in inconsistent formats and had to be assembled into a coherent history for each patient, for both care and billing. Volume growth meant hiring.
The situation
What the organization was dealing with
Medical records arrived from a wide variety of providers in inconsistent formats and had to be assembled into a coherent history for each patient, for both care and billing. Volume growth meant hiring.
Records processing is the sort of work that never appears on a strategy slide and quietly consumes a department. Medical records arrive from hundreds of providers in formats that were never standardised, and someone has to turn them into one coherent history before either a clinician or a biller can use them.
What was done
The work, and the part that was actually hard
The DevOps team automated intake and processing with Power Automate, taking records from every source and normalising them into one patient history without manual handling.
The scale here is what makes it notable: twenty million records a year is well past the point where adding staff is a viable answer, and the organization was facing exactly that decision. Automating the intake and normalisation with workflow tooling rather than a bespoke integration platform kept the solution inside the existing licensing and skill set.
Results
What was published
These are the figures exactly as reported in the source. Nothing has been rounded, extrapolated or restated.
Records processed per year20M
Hours of work saved annually100,000+
New hires required to scale0
What changed
- Records from many provider formats normalised into one coherent patient history
- Care history and billing both served from the same processed record
- New clients and processes onboarded without adding staff
- Skilled people redeployed from data handling onto exception work
Platforms involved
What each product was doing here
What transfers
If you were to attempt this
Any organization ingesting high volumes of unstructured documents from many external parties — claims, applications, invoices, referrals — has a version of this problem and usually has not measured what it costs.
Where it usually gets harder than expected: Automation at this volume makes exception handling the whole job. The design question is not what happens to the 95% that process cleanly, but who looks at the rest and how quickly they find them.
How we would take it on
Our approach to Modern Work & Automation work
1
Check what you already own
Most organizations are licensed for more than they have deployed. This step frequently reduces the engagement we were about to be paid for.
2
Assess permissions before AI
Copilot reaches whatever the user can reach. A decade of casual oversharing becomes visible on day one, and it is entirely avoidable if you look first.
3
Pick scenarios with the business
The functions that will use it know where their week goes. Scenario selection done inside IT produces pilots that impress nobody.
4
Measure in tiers
Usage, then time and quality, then business KPIs. Anyone promising the third before the first exists is guessing.
Recognise the problem?
If any of the above describes your organization, tell us where it hurts most. We will tell you what the same platforms could realistically do in your environment, what we would measure, and whether we think it is worth doing at all.
Talk to our teamMore Modern Work & Automation storiesWe reply to every message within one business day.
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