TeamStation AI
Home / Case Studies / Healthcare Revenue Cycle Platform

Audit-Ready Delivery for Healthcare Revenue | TeamStation AI Case Study

Hero image for Healthcare Revenue Cycle Platform case study

Executive Summary

A U.S. healthcare revenue cycle platform engaged TeamStation AI to stabilize delivery, harden security, and scale engineering capacity under a rigorous MSA/SOW framework. We converted contractual intent into a documentation‑first delivery system and activated our Nearshore IT Copilot to assemble, verify, and onboard a blended team across backend, data, QA, and product. The outcome was predictable throughput, audit‑ready operations, and faster time‑to‑value—without exposing PHI or slowing releases.

Highlights

  • Documentation‑driven execution: SOW/MSA terms translated into technical runbooks, RACI, and measurable SLAs/SLOs.
  • Precision hiring at speed: AI‑assisted vetting across 12k+ interview signals; bias‑reduced selection; verified seniority fit.
  • Onboarding you can audit: Day‑0 to Day‑10 dossier (devices, access, environments) with evidence trails.
  • Security by default: SOC 2/ISO‑aligned controls, MDM‑managed devices, least‑privilege access, and pen‑test cadence.
  • Predictable delivery: Definition of Ready/Done, CI gates, and release guardrails embedded from sprint one.

Context & Challenges (from MSA/SOW scope)

  • Scope volatility & compliance pressure: Healthcare workflows and payer integrations demanded strict change safety and auditability.
  • Fragmented documentation: Prior engagements lacked living architecture docs, onboarding playbooks, and test strategy → recurrent rework.
  • Talent misalignment: Mixed seniority and culture fit led to inconsistent estimates, incident spikes, and missed dates.
  • Time‑zone friction: Slow feedback loops and handoff loss across distributed stakeholders.

Why TeamStation AI

  • Hiring Engine: Transformer‑based parsing + structured work‑sample testing → verified skills, communication patterns, and delivery history.
  • Onboarding Engine: A standard, evidence‑backed onboarding dossier: device/MDM proof, identity/access approvals, environment readiness, and data‑handling attestations.
  • Documentation as a Product: We treat docs as first‑class deliverables with owners, review SLAs, and version control.
  • Governed Nearshore: Same‑time‑zone collaboration, secure devices, and insurance coverage with executive‑level reporting.

The TeamStation AI Documentation System

What we produce and maintain (living artifacts):

  • Onboarding Dossier (Day‑0 → Day‑10): Device/MDM verification; MFA/SSO; access approvals; environment setup checklists; data‑handling attestations.
  • Architecture Decision Records (ADRs): Rationale, trade‑offs, impact radius, and rollback plans for every significant change.
  • Service Catalog & Dependency Map: Ownership, SLAs, APIs, data contracts, and observability endpoints.
  • Runbooks: Incident, release, and backfill procedures; response matrices and escalation ladders.
  • Test Strategy: Unit/integration/contract suites; release gates; flake management; non‑prod data policy.
  • Security Baseline Kit: Secrets policy, least‑privilege roles, code‑signing, and pen‑test schedule.

Outcomes (Representative)

  • Predictable delivery: Fewer failed deployments and reduced incident frequency via embedded release gates.
  • Faster onboarding: New engineers productive in ≤10 business days with audited evidence.
  • Lower coordination cost: Time‑zone alignment and documented rituals cut decision latency.
  • Compliance confidence: Clean audits/QBRs anchored by maintained artifacts.