The Problem None of these would top a spend report.

2017 — Hurricane Maria knocks out three Baxter plants in Puerto Rico — ~50% of US small-volume saline bags. Nurses go back to pushing drugs by syringe.

2022 — A COVID lockdown shuts a single GE plant in Shanghai. Hospitals ration contrast media — triaging which patients get CT scans.

2024 — A national shortage of blood culture bottles (CDC HAN-00512).

Materiality is measured to the penny. Criticality is tracked in adjectives. When the two argue, dollars win every time — not because they matter more, but because they have units.

The Replacement-Level Baseline Value is measured against replacement.

What happens if this specific thing vanishes and the slot is filled from the open market?

Measure vs. zero

Inflates everything — every product looks priceless.

Measure vs. average

Punishes solid performers, rewards mediocrity.

Measure vs. replacement ✓

The decision-relevant counterfactual: what do I lose, given the freely available alternative?

The Currency Not dollars. Dollars are the input.

The output of a hospital supply chain is patient health. Health economics already has a unit for it: the QALY — a quality-adjusted life year.

\[ \frac{\$100{,}000 \text{ per QALY}}{8{,}766 \text{ hours per year}} \;\approx\; \$11.40 \text{ per quality-adjusted hour} \]

That exchange rate — call it v — is what lets a $2M savings opportunity and a supply-disruption scenario be compared as quantities, not argued as a spreadsheet vs. an anecdote.

Before You Trust This Number Two honest problems with the exchange rate.

1. It isn’t settled. Supply-side (~£13k/QALY, NHS marginal productivity) vs. demand-side ($100–150k/QALY, willingness-to-pay) disagree 3–5×. For procurement savings, supply-side is causally correct.

2. It crosses asymmetrically. Risk is born in hours — no valuation step. Savings is born in dollars and only becomes hours through v. So v is a weighting knob between channels, not a shared unit conversion.

Mandatory phrasing rule Externally, dollars are “valued at the standard cost-effectiveness threshold.” Never “lives saved.”

The Metric HAR = savings + risk, in one currency.

\[ \mathrm{HAR}(p) = \mathrm{HAR}_{\mathrm{savings}}(p) + \mathrm{HAR}_{\mathrm{risk}}(p) \qquad \mathrm{risk\_share} = \frac{\mathrm{HAR}_{\mathrm{risk}}}{\mathrm{HAR}} \]

HAR_savings — the money channel

Well-measured, high-resolution, computed from millions of observed transactions.

HAR_risk — the clinical channel

Noisier, log-resolution — and the reason the composite exists at all.

The guardrail: never let the savings channel’s four-significant-figure precision lend its credibility to the risk channel. They share a unit, not an error bar.

Channel 1 — Savings Three rules keep the naive version honest.

  1. Replacement price is the cohort median, not the best price — what a competent buyer gets without special leverage.
  2. Replacement level is conditional on who you are — class of trade, size, 340B account type.
  3. An achievability haircut — not every dollar of measured excess is capturable.

Worked example — knee implants \(300/\text{yr} \times (\$5{,}000 - \$4{,}200) \times 0.5 \div \$11.40/\text{h} \approx \mathbf{10{,}500\ \text{hours/year}}\)

Channel 2 — Risk Three terms, in descending order of measurability.

\[ \mathrm{HAR}_{\mathrm{risk}}(p) = P(\text{pool shrink}) \times \text{downstream hours at stake} \times P(\text{no viable path}) \]

  1. Volume — exact, straight from transaction data.
  2. P(disruption) — order-of-magnitude, from FDA shortage lists and recall feeds.
  3. Downstream hours at stake — the hard one. Log-resolution is the honest resolution.

Worked example — contrast media 20,000 scans/yr, log-bin terms multiplied through ≈ 10²–10³ expected h/yr — but the year it lands: 10⁴–10⁵ realized hours, at every hospital simultaneously.

Reading The Number Rank by the sum. Act by the ratio.

HAR recovers the Kraljic matrix — taught everywhere, quantified almost nowhere — as a scalar and a ratio.

risk_share Posture
≈ 0 (savings-dominated) Negotiate hard, commoditize
≈ 1 (risk-dominated) Secure supply — dual-source, hold inventory, stop squeezing
both channels large Strategic — the metric flags it, judgment settles it

“This category’s supply risk is worth ten times its savings opportunity, so we’re buying continuity, not discounts.”

What Tops HAR Right Now An illustrative national leaderboard.

Illustrative, order-of-magnitude, US national scale. Sorted by total HAR.
# Product family HARsavings (h/yr) HARrisk (h/yr) risk_share Posture
1 Hip & knee implants 10⁸ 10⁴·⁵ ≈ 0 Negotiate
2 Stents & cardiac rhythm devices 10⁷·⁵ 10⁵·⁵ 0.01 Negotiate
3 IV saline & solutions 10⁵·⁹ 10⁷·³ 0.96 Secure supply
4 Iodinated contrast media 10⁶·¹ 10⁷·¹ 0.91 Secure supply
5 Platinum chemotherapies 10⁴·⁵ 10⁷ ≈ 1 Secure supply
6 Exam & surgical gloves 10⁶·⁸ 10⁵·² 0.02 Negotiate
7 Blood culture bottles 10⁵ 10⁶·⁸ 0.98 Secure supply
8 Dialysis consumables 10⁶·³ 10⁶·⁵ 0.61 Strategic
9 Infusion pumps & sets 10⁶·⁵ 10⁶·² 0.33 Strategic
10 Heparin 10⁵·⁵ 10⁶·⁵ 0.91 Secure supply

Closing The Loop Our own hours live on the same scale.

At ~$100/hour loaded cost and v ≈ $11.40/hour, one analyst hour costs ≈ 9 health-hours — every review task must move ~10 HAR-hours per analyst-hour to be worth doing.

\[ \mathrm{queue\_score} = \frac{E[\Delta\mathrm{HAR} \mid \text{review}] \times P(\text{action}) \times \text{persistence}}{\text{expected analyst-hours to review}} \]

Items below the line are correctly declined, not backlog. Data-quality queues, enrichment targeting, and classification training data are all prioritized by the HAR at stake.

The Discipline Falsify cheap before building expensive.

The expensive parts of HAR are all in the risk channel — shortage base rates, hours anchors, the dependency graph. The cheap part is a few hours of work on data we already have.

First computation = a falsification test Does the risk term ever move a rank?

If not — stop. Keep the sharpened savings metric (it improves the existing deliverable on its own) and retest at finer grain before spending more.