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PAN Lab example

Allegheny Hello Baby

The help that keeps a file: a birth-risk prevention model

Every newborn in the county is scored from old records and sorted into service tiers — the top tier gets a knock on the door offering help, not an investigation. Modeled on Allegheny's Hello Baby. Two things make it different: the score is computed from records the family never gave for this, and the people offering help are mandated reporters. Watch the firewall that keeps the score out of the investigation channel, and the loop where accepting help can put a new note in the file that raises tomorrow's score.

Stylized model of a documented deploymentChild welfare & family services

Open this example in PAN Lab v0.1 to apply pressures and levers and watch what the system does.

What this models

This example runs on the Hello-Baby-class universal-prevention risk model network: 4 components and 9 pathways between them. Every context in the Lab is a stylized model, never a reconstruction of any actual deployment, and each assumption behind it carries a provenance label.

Evidence base: 5 assumed · 1 published baseline. In the Lab, the shaded evidence band behind each headline readout draws its width from the least-established class below.

  • assumed

    This models the universal-prevention risk-scoring pattern documented in the Allegheny Hello Baby case file — not a reconstruction of the actual program.

  • baseline

    The feedback loop from cumulative administrative records into each newborn's score is present at baseline and strong, reflecting the case documentation's account that the score is computed from existing county records — prior system contact raises the score.

  • assumed

    The score is deliberately kept out of child-welfare intake and investigation; the replication pathway into the child-protective channel is drawn on the map but runs dry at baseline — the case's defining firewall, which an unreviewed connector (function creep) would open.

  • assumed

    Outreach is delivered by mandated reporters, so service contact can itself generate a new report that re-enters the record. The case documentation records this surveillance-bias risk as contested: critics treat it as central, while the county argues its empirical size is small.

  • assumed

    Peer pathways are authored on both signs: engagement practices spread across outreach teams, while one model scoring every newborn makes its blind spots correlated across the cohort rather than idiosyncratic.

  • assumed

    Demographics and differential harm to newborns and families are not modeled here; the Lab models institutional propagation only, and any such harm is documented in the case file and measured outside a diagram like this one. No measured racial disparity figure specific to Hello Baby is asserted.

What this example does not show

  • Differential harm to newborns and families is not modeled here; the Lab models institutional propagation only, and that harm is documented in the case file and measured outside any diagram like this one. The surveillance-bias effect the memory loop stands in for is contested: critics document it as a core risk, while the county cites evidence that its empirical size is small.

Sources and evidence

What this example rests on, claim by claim. Every entry resolves to the same ledger the Evidence Registry publishes.

  • In Allegheny County's Hello Baby program, the top-tier roughly 5% of newborns by predictive risk score accounted for about 54% of children later removed from the home by age three, at roughly twenty times the removal risk of other newborns (methodology relative risk 22.24, 95% CI 17.50-28.25); the model reported an AUC of about 0.93 on holdout data.

    empirical
    • Government evaluation Centre for Social Data Analytics (AUT) for Allegheny County DHS, Implementing the Hello Baby Prevention Program in Allegheny County: Methodology Report Version I (2020) https://analytics.alleghenycounty.us/wp-content/uploads/2020/12/Hello-Baby-Methodology-v6.pdf
    • Academic Vaithianathan, Benavides-Prado, Rebbe & Putnam-Hornstein, Using a Predictive Risk Model to Prioritize Families for Prevention Services: The Hello Baby Program in Allegheny County, PA, Prevention Science (2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC12064473/

Where this connects

Institutional pressures in this domain

  • Workload surge — Demand outruns staffing; per-case attention shrinks and review becomes triage.
  • Deadline pressure — Statutory or managerial timeliness rules reward fast approval of machine output over slow disagreement.
  • Staff turnover — Experienced skepticism leaves; new staff calibrate their trust on the tool itself.
  • Data & policy drift — The world, the intake process, and the rules change under a system trained on how things used to be — two mechanisms with different remedies: the statistical properties of what the system processes move (concept drift), or the mixture of inputs arriving in deployment differs from the mixture it was trained on (covariate shift).
  • Compliance over substance — Paper controls (sign-offs, checklists) satisfy audits while the behavior they describe erodes.

All of them in context on the Child welfare & family services domain page.

Levers available here and the patterns behind them

Documented case histories