Healthcare Technology

AI-Powered Care Coordination Platform Built for Compliance and Scale

A benchmark engagement. We designed and built a HIPAA-compliant care management platform from the ground up — streamlining patient intake, remote monitoring, and multi-provider coordination.

Hoss Care
Product engineeringAI integrationHIPAA-aware architectureFull-stack development

60%

Reduction in intake time

100%

HIPAA audit-ready at launch

3x

Provider capacity

Ongoing

Active development partnership

The challenge

Hoss Care is a chronic care management company serving patients with complex conditions across multiple providers. Their existing workflow relied on manual intake forms, phone-based check-ins, and spreadsheet-driven care plans — a process that was slow, error-prone, and impossible to scale. They needed a modern platform that could handle remote patient monitoring, care plan management, and multi-provider coordination, all while meeting strict HIPAA requirements.

Our approach

Discovery and architecture

We began with a deep discovery phase — mapping every manual workflow, identifying the highest-friction points, and designing the data model before writing a line of code. The key insight was that the bottleneck was not any single feature but the handoff points between intake, scheduling, and care plan assignment. We designed around those handoffs first.

HIPAA-first engineering

Healthcare software fails compliance not because teams are careless but because compliance is treated as a layer on top rather than a foundation. We built audit logging, role-based access control, and PHI encryption into the data layer from day one — not retrofitted. Every API endpoint enforces role checks. Every read and write of patient data generates an immutable audit trail.

AI-assisted intake and care plans

The most impactful AI integration was in intake and care plan generation. When a new patient is added to the system, an AI layer analyzes their intake information, flags risk factors, and pre-populates a draft care plan for the provider to review. What previously took 20–30 minutes of provider time now takes under 5. The AI handles the structured administrative work so providers can focus on clinical judgment.

Remote monitoring infrastructure

We built a real-time monitoring pipeline that ingests data from connected devices — blood pressure monitors, glucose meters, weight scales — and surfaces alerts when readings fall outside patient-specific thresholds. The alerting layer uses configurable rules per patient rather than population-level defaults, which dramatically reduced false positive alert fatigue.

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