Writing · September 2026 · 4 min read

Data Doesn't Decide

The missing middle between your dashboard and your board vote.

Every health system I talk to has made the same investment, twice. First they bought the systems that record what happens — the EHR, the warehouse, the dashboards. Then they bought the people and reports that explain what happened — analysts, consultants, board packets.

And then, on a Tuesday afternoon, the board faces the question all of that was supposedly for: should we acquire the practice, employ the physicians, or walk away? — and the decision is made from a spreadsheet, a slide, and memory.

The usual diagnosis is that something is missing: not enough data, not enough discussion, not enough documentation. I want to argue something more uncomfortable.

Nothing is missing. Every step of a rigorous decision is already happening — invisibly, unexamined, and off the record.

A decision that commits capital always answers a chain of questions on its way to "yes": What are we optimizing? What were the real options? What do we believe happens under each one? What are we trading away? Which assumptions carry the answer? Where are our hard lines? What would change our mind?

You cannot skip these steps. You can only skip writing them down. When they aren't made explicit, they don't disappear — they get answered by default, by whoever happens to be standing closest:

The step	Where it lives today	What that costs	On the record
Objectives — what are we optimizing?	In the weights someone silently built into the spreadsheet	The institution votes on a principle it never chose	A governing principle, chosen and signed before results
Options	Whatever made it onto the slide	The unexamined alternative is the one you live with	Options enumerated, including "do nothing," each scored
Prediction — what happens if we act?	Last year's trend, extended	History breaks precisely when you intervene	Structural models that carry the intervention
Trade-offs	A hallway argument after the meeting	The losing value never gets priced, just outvoted	Every axis scored; the tension made explicit
Sensitivity	"Well, it depends" — said once, then dropped	Nobody knows which assumption carries the answer	The two or three assumptions that actually move the decision, named
Constraints & thresholds	A gut feeling nobody signed	Hard lines discovered after they're crossed	Guardrails stated up front, on the record
Dissent	Remembered selectively, later	"I never agreed to this" — unfalsifiable	Who disagreed, about what, recorded
The decision	A motion in the minutes	The why evaporates within a quarter	A signed recommendation under a named principle
Reversal	Revisited by crisis, or never	Decisions outlive the facts that justified them	Explicit conditions that reopen the decision — monitored
Outcome	Rarely compared to the promise	The institution never learns from its own choices	The record stays alive; assumptions checked against reality

Read the middle column again. That is not a picture of negligence. It is a picture of competent people running an implicit process because no explicit one exists. The weighting was chosen — by whoever built the model. The sensitivity analysis did happen — as an argument. The reversal condition exists — as a feeling someone will have in eighteen months.

This is why "we need better data" so rarely fixes decision-making, and why one more dashboard never ends the debate. The data was never the missing piece. The middle was — the layer where evidence, values, trade-offs, and thresholds get assembled into a choice someone must sign.

Healthcare has spent a decade making its data AI-ready, and that work was necessary. But AI-ready data answers "what happened." The question a board signs its name to is "what should we do — and can we defend it?" That second question is not a reporting problem or a vibes problem. It is a discipline — decision science has spent fifty years building exactly this middle layer — and it deserves the same infrastructure we built for records and billing.

The institutions that get this right won't decide more slowly. They'll decide faster, because the arguments happen once, in the open, against a principle everyone already ratified — instead of forever, in hallways, against principles nobody wrote down.

Next: a worked example — a public decision brief on a real regional healthcare question, built entirely from public data, with every claim tagged as evidence, assumption, or to-verify. Not to advocate for an answer, but to show what the middle looks like when it's written down.