APHI works with public health teams to define practical AI use cases, design validation, and build tools that fit existing surveillance and decision workflows.
Purpose-built for the realities of modern public health departments
Surveillance teams often work across delayed feeds, manual review queues, and competing priorities. APHI prototypes are designed to organize signals for faster human review.
ED visits, lab results, environmental signals, and public reports often sit in separate systems. Integration only helps when sources are trusted, governed, and interpretable.
Vulnerable populations often have weaker data coverage. Every prototype needs missingness review, subgroup checks, and a plan for communities poorly represented in the data.
Limited staff cannot review every possible signal. Automated triage should reduce noise, not create a larger queue.
Legacy systems don't talk to each other. We support HL7 FHIR, LOINC, SNOMED and integrate with CDC's National Syndromic Surveillance Program infrastructure.
Leadership needs proof before scale. APHI work starts with prototype cards, validation plans, and plain documentation of what is known, unknown, and not yet claimed.
We connect to your existing systems via secure APIs:
Privacy-first: Differential privacy and federated learning keep sensitive data on-premises while enabling collaborative intelligence.
Our ensemble models run continuously:
Design standard: Models must produce reviewable evidence, not opaque alerts.
Epidemiologists receive:
Models improve with your feedback:
APHI will not publish proprietary performance numbers until they are sourced, scoped, and reviewed.
CDC and other public health groups have documented AI uses in surveillance support, coding, summarization, and response readiness.
These examples inform APHI's design, but they are not APHI deployment metrics.
Any pilot should compare APHI outputs against the existing local workflow, including alert burden, missed signals, and reviewer usefulness.
Claims should be specific to disease area, data source, jurisdiction, and evaluation period.
Evaluation must ask which communities are poorly represented, which signals are less reliable, and whether recommendations shift resources fairly.
Equity checks are part of product readiness, not a separate compliance exercise.
APHI may cite public CDC or peer-reviewed metrics when the source is linked and the scope is clear. APHI will not transfer those numbers to its own prototypes.
A useful pilot should answer operational questions before scale.
Note: APHI will report outcomes only after partner-approved validation. Until then, this page describes the evaluation standard.
A useful first pilot should stay narrow enough to validate honestly.
Early conversations should focus on one specific workflow, not a broad platform rollout.
Transparency Commitment: APHI will keep concept-stage claims separate from validated results. Partner pilots should produce honest evidence, including limitations and reasons not to scale.