The concepts are the repeatable parts. Cases show the particulars. Reviews test vendor and infrastructure claims. The spine below is how I sort the work.

01

Workflow fit

What job is the system actually entering, and what has to change around it for the tool to matter?

Related AI buildout cases
02

Data reliability

Which fields are complete, current, and operationally meaningful enough to support a decision?

Related data-work cases
03

Ownership and handoffs

Who owns the work before, during, and after the software touches it?

Related clinic-ops cases
04

Review burden

How much work moved from doing to checking, and is that review path real?

Related reviews
05

Source boundaries

What does the system know, what is it guessing, and where should it refuse to sound complete?

Related AI-infra cases
06

Evidence before dashboards

Dashboards are only useful after the source, definition, and exception logic are clear enough to survive scrutiny.

Related strategy cases

Concept notes

concept working thesis strategy diligence

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