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Large US health system / Optum algorithm

Verified evidenceQueue eliminationContinuous decisioning

Commercial cost-proxy risk ranking

A predictable proxy can still allocate scarce care unjustly.

Enrollment · United States

Collections: Queue eliminated · Negative results

An editorial scene for Large US health system / Optum algorithm contrasts has a limited number of intensive nursing and coordination slots to allocate. with ranks patients by predicted future cost as a proxy for health need. in the commercial cost-proxy risk ranking workflow.

Executive brief

The operating-model shift, in one view.

Target validity matters more than headline predictive accuracy.

AI value · Black share identified for extra care

Verified

46.5% if ranked by actual health need

Counterfactual repair in one system; vendor disputed use characterization.

Before

Has a limited number of intensive nursing and coordination slots to allocate. → Use clinical knowledge to identify patients with complex needs.

After

Ranks patients by predicted future cost as a proxy for health need. → Use the score among selection inputs and allocate scarce extra care.

Human boundary

Programs own enrollment; clinicians retain judgment.

Why it matters

A predictable proxy can still allocate scarce care unjustly.

How the work changed

Before

How the work ran before the change.

  1. Step 1 of 2

    Care-management program

    Has a limited number of intensive nursing and coordination slots to allocate.

    ControlThe Science study does not provide a dated pre-algorithm workflow; this is a bounded resource-allocation baseline.

  2. Step 2 of 2

    Clinicians and program staff

    Use clinical knowledge to identify patients with complex needs.

    ControlHuman judgment, constrained by program capacity.

What changed

A predictable proxy can still allocate scarce care unjustly.

Decision rightHuman authority remains at the consequential boundary

After

How the same work runs now.

  1. Step 1 of 2

    Commercial risk algorithm

    Ranks patients by predicted future cost as a proxy for health need.

    ControlTop 97% risk threshold at the studied site; proxy choice embeds unequal access and spending.

  2. Step 2 of 2

    Program staff and clinicians

    Use the score among selection inputs and allocate scarce extra care.

    ControlClinicians can supplement the score, but scaled allocation is score-shaped; health-need counterfactual exposed bias.

Process model built from the published workflow evidence for Large US health system / Optum algorithm. Every step, actor, and control appears in full below.
Every step, actor, and control

Exception path

Clinical judgment can add patients, but the queue is score-shaped.

Work removed

  • Some manual population prioritization

Decision authority

Programs own enrollment; clinicians retain judgment.

Before

  1. 01

    Care-management program

    Has a limited number of intensive nursing and coordination slots to allocate.

    Control: The Science study does not provide a dated pre-algorithm workflow; this is a bounded resource-allocation baseline.

  2. 02

    Clinicians and program staff

    Use clinical knowledge to identify patients with complex needs.

    Control: Human judgment, constrained by program capacity.

After

  1. 01

    Commercial risk algorithm

    Ranks patients by predicted future cost as a proxy for health need.

    Control: Top 97% risk threshold at the studied site; proxy choice embeds unequal access and spending.

  2. 02

    Program staff and clinicians

    Use the score among selection inputs and allocate scarce extra care.

    Control: Clinicians can supplement the score, but scaled allocation is score-shaped; health-need counterfactual exposed bias.

Work that left the path

  • Some manual population prioritization

Human role before

Clinicians and care-program staff used clinical knowledge and limited program capacity to select patients for intensive care management; the study does not provide a dated pre-algorithm queue.

Human role after

Program teams set enrollment thresholds and clinicians may supplement the score.

AI roleRanks patients by predicted future cost as a proxy for need.

Outcomes

Black share identified for extra care

Verified

17.7% under cost proxy46.5% if ranked by actual health need

Published 2019 · 43,539 White and 6,079 Black patients

Counterfactual repair in one system; vendor disputed use characterization.

What leaders can reuse

Anti-pattern

Do not call cost prediction inaccurate; the target was wrong.

Questions

  1. 01Where is the operating threshold set and who can override it?
  2. 02What measured result would trigger rollback or retraining?
  3. 03Which residual decisions must remain human-owned?

Portability conditions

  • Need-based targets
  • Subgroup audits
  • Override capacity

Reputation risk

high

Evidence and authority

What the public record supports.

Current · updated

1 independent, 1 peer reviewed; publication outcomes are verified.

Bundle 1.0.0 · reviewed 2026-08-23 · stable ID 81c7b1bc482d6d8f

Related transformations

More in Enrollment

Sources

Read the evidence, freshness, caveat, and version policy.