The Problem
A US healthcare provider was collecting patient data across three separate systems — EHR records, wearable device telemetry, and genetic testing results — with no infrastructure to consolidate them into a coherent patient profile. Care pathways were based on general clinical guidelines rather than individual patient data. Preventive care plans had low adherence because they were not specific enough to feel actionable.
The provider needed a platform that could unify heterogeneous data at scale, run AI-driven analysis on the consolidated profile, and deliver personalised care recommendations — within the strict data handling requirements that HIPAA imposes on every layer of the system.
The Constraints
HIPAA compliance throughout every layer. Every component — data ingestion, storage, processing, and output — had to meet HIPAA requirements before any patient data entered the system. This meant TLS encryption in transit, AES-256 at rest, role-based access control down to the field level, full audit logging of every data access event, and Business Associate Agreements in place with each third-party infrastructure provider.
Multi-source data reconciliation without human intervention. EHR records, wearable telemetry, and genetic results arrive in different formats, on different cadences, and with different completeness guarantees. The reconciliation pipeline had to handle missing fields, conflicting values, and variable latency without producing unreliable patient profiles — and without requiring manual intervention for each exception.
Clinical interpretability as a design constraint. AI recommendations presented to physicians had to be explainable. Clinicians needed to understand the reasoning behind a specific recommendation and be able to override it. This constrained model architecture choices away from approaches that maximised predictive accuracy at the cost of interpretability.
Our Approach
We built the platform in three layers: a data integration layer handling multi-source ingestion, an AI reasoning engine generating personalised care recommendations, and a clinical presentation layer designed for both physician and patient-facing use.
For data ingestion, Apache Kafka handled high-throughput streaming from wearables and event-based EHR updates, with HL7 FHIR as the canonical data model for patient records. Apache Spark processed batch loads of historical EHR data and genetic testing results. A deterministic reconciliation pipeline resolved conflicts across sources using a defined precedence order, with every exception logged against the patient record.
The reasoning engine combined deep neural networks for longitudinal pattern recognition with NLP to parse unstructured clinical notes and specialised models for genetic data analysis. Every recommendation output included the feature weights that generated it — allowing physicians to evaluate the underlying reasoning and maintain clinical oversight rather than accepting opaque outputs.
Security architecture was scoped from the first design review: encryption throughout, RBAC with role definitions mapped to clinical job functions, infrastructure restricted to HIPAA-eligible AWS services, and BAAs confirmed before any patient data entered the pipeline.
The Outcome
- 30% increase in patient adherence to prescribed preventive care plans
- 10% reduction in preventable hospital visits
- 20% increase in completed risk assessments; 15% boost in physician efficiency through structured, evidence-backed recommendations
- HIPAA-compliant architecture with full audit trail and access control in production
Team
Engagement: 6 months, 5 engineers (2 AI/ML, 1 data platform, 1 backend, 1 security and DevOps).
Stack: Python, TypeScript, TensorFlow, PyTorch, Apache Kafka, Apache Spark, PostgreSQL, AWS (S3, EC2, RDS), Docker, Kubernetes, React, HL7 FHIR