How should I evaluate an AI platform for senior housing & care?
Direct answer
Evaluate architecture and authority, not feature counts. Ask where each number comes from, who rules when sources disagree, what happens to the evidence behind a recommendation, who owns the definitions, what the exit looks like, and which claims are in production versus validation. A platform that cannot answer those six questions in writing is not evaluable, however long its feature list is.
Why feature lists mislead
Feature counts measure surface area, not reliability. Two platforms can both offer occupancy reporting while differing entirely on the question that matters: whose occupancy definition governs, and what happens when the clinical record and the ledger disagree. The second question determines whether the number can be used in a board package.
Six questions to put in writing
Ask every vendor, including us, to answer these in a document you can keep.
- 01Definitions: who authors the definition of each metric, and can we change it?
- 02Authority: what is the deterministic rule when two source systems disagree?
- 03Evidence: does each recommendation arrive with the values and rules behind it?
- 04Ownership: do we own the definitions, the mappings, the lineage and the history?
- 05Exit: what exactly can we export, in what format, and how quickly?
- 06Maturity: for each capability, is it design intent, built, validated, or in operator production — and what evidence supports the state claimed?
How to read maturity claims
A capability can be genuinely built without being in operator production. Both statements are legitimate; conflating them is not. Require each vendor to state the maturity of each capability separately and to say what evidence supports it, then compare the evidence rather than the adjectives.
Read the full argument
How to evaluate a platform on architecture and authority instead of feature counts.
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