Health missions · Data science
From claim line to care decision.
Health data is unlike any other: fragmented across standards, governed by layered privacy law, and consequential in a way ad clicks never are. This is the domain we chose — and the discipline it demands shapes everything we build.
Hale MDS · Geo surveillance
TAMPA BAY · WK 30 · PER 100K
Choropleth map of the Tampa Bay region — real Census ZIP-code boundaries for Hillsborough, Pinellas, and southern Pasco counties — shaded light to dark blue by weekly syndromic rate per 100,000. A three-ZIP cluster in East Tampa is flagged in amber and under review; a milder rise shows in south St. Petersburg. Illustrative rates over real geography.
The console on this page is an illustrative product concept, not a screenshot of a delivered system.
Mission domains
Where we operate
Four problem spaces, each with its own data shapes, statutes, and stakes.
CMS-scale missions
Claims & Payment Integrity
Fraud, waste, and abuse detection across billions of claim lines; risk-adjustment analytics; provider network analysis; and prior-authorization decision support.
Public-health missions
Population Health & Surveillance
Syndromic surveillance pipelines, outbreak detection models, social-determinants integration, and jurisdiction-level dashboards that hold up during a crisis.
VA / DHA-scale missions
Clinical Operations
Risk stratification for care management, capacity and wait-time forecasting, clinical NLP over provider notes, and decision support embedded in the EHR workflow.
NIH-scale missions
Research Data Platforms
OMOP-based research enclaves, registry and trial data systems, de-identification pipelines, and reproducible analysis environments for scientific teams.
Method
The study is designed before the data is touched.
Health missions don't tolerate improvisation. Every analysis here starts from a written plan and ends with a result a review board can weigh.
Protocol before analysis
The question, the cohort, the outcome and the analysis plan are written down before the first query runs — so the result answers the question that was asked, not the one the data suggested afterwards.
Reproducible end to end
Versioned data and code; every figure regenerates from source. An analysis that cannot be rerun cannot be trusted, and a reviewer should not have to take our word for it.
Reported with its uncertainty
Intervals, sensitivity analyses and calibration travel with every estimate, so a decision-maker knows how much weight a result bears before acting on it.
Fluency
We speak the standards natively.
No translation layer between your data and the people who research it.
- FHIR R4
- HL7v2
- C-CDA
- X12 837/835
- OMOP CDM
- USCDI v4
- SNOMED CT
- LOINC
- RxNorm
- ICD-10-CM
Trust architecture
Compliance, designed in.
The regulatory frame isn't an obstacle to good research — it's part of the study design.
- HIPAA
- Privacy by design. De-identification, minimum-necessary access, and audit trails built into every data flow.
- NIST SP 800-171
- Controls as code. Security controls implemented as code with evidence collected automatically, so accreditation is not a retrofit.
- Section 508
- Accessible by default. WCAG 2.2 AA conformance designed in and tested with assistive technology.
- 42 CFR Part 2
- Part 2–protected records segmented and consented where a mission requires it.
- IRB & data use
- Human-subjects governance and data-use agreements for research on data agencies already hold.