Portfolio Accelerator · Healthcare · Amazon Web Services
Patient Engagement AI on AWS: Implementation Partner for Healthcare Systems
A documented, buildable patient engagement architecture on AWS using Amazon Bedrock, HealthLake, and Comprehend Medical.
problem
Why Patient Engagement Needs a Governed AI Architecture, Not a Chatbot Bolt-On
Patient engagement AI that touches scheduling, care-gap outreach, or intake has to handle PHI-adjacent conversations safely, log every interaction for audit, and integrate with the clinical data model rather than sitting outside it as a disconnected chat widget. Most patient-engagement point solutions solve the conversational layer and leave the data governance and audit trail as someone else's problem.
demo
Inside the Accelerator: A Documented AWS Architecture, Built in the Open
This page showcases Kriv AI's patient engagement accelerator on AWS. In the interest of transparency: the environment, infrastructure-as-code, and agent configuration are built; the data pipeline execution and end-to-end demo run are the next phase. We show the real state below.
Architecture: Bedrock, HealthLake, and Comprehend Medical
The design routes patient-facing conversations through Amazon Bedrock (using Claude via Bedrock) with Bedrock Guardrails enforcing HIPAA-aligned content boundaries, resolves clinical context against a synthetic FHIR store in AWS HealthLake, and extracts structured clinical information from unstructured text with Amazon Comprehend Medical. Every interaction is logged to CloudWatch and archived to S3 for audit.
Five Bedrock agent definitions are already written and configured, along with a HIPAA guardrails configuration, Lake Formation fine-grained access-control policies, and infrastructure-as-code (CloudFormation) for the VPC, S3, KMS, and IAM foundation. A working care-gap detection engine (15 rules) is already coded as an AWS Glue job.
What's Real Today, and What's Roadmap
Honestly: the environment setup, infrastructure-as-code, agent configuration, HIPAA guardrails, and the care-gap rule engine are built and version-controlled. Loading the synthetic patient population, standing up the HealthLake FHIR store, and running the full demo end-to-end is the next phase, not yet complete. No real PHI is used anywhere in this build, at any phase.
differentiation
Why an AWS Implementation Partner Beats a Point-Solution Chatbot Vendor
A typical patient-engagement SaaS vendor sells a chat widget and leaves clinical data governance to your team. Kriv AI starts from the governance layer, HIPAA guardrails, and clinical data model first, with the conversational layer built on top, adapted to your existing HealthLake or FHIR environment during implementation.
governance
What We'll Measure Once the Demo Is Live
Metrics This Accelerator Is Designed to Track
Once the full pipeline is running, the architecture is designed to report: average agent response latency, containment and resolution rate (conversations resolved without human escalation), intake-form completion and accuracy rate, and cost per interaction based on real Bedrock token usage. These are the four numbers a healthcare operations leader should ask any patient-engagement vendor to show, measured rather than estimated.
engagement
From Blueprint to Your Health System's Environment
A scoped engagement adapts this architecture to your existing AWS footprint, EHR integration points, and patient population, carrying the same HIPAA guardrails and audit-logging design through from day one.
Straight answers
Frequently asked questions about Patient Engagement AI on AWS: Implementation Partner for Healthcare Systems
Is the patient engagement accelerator fully built and running?
Not yet, and we would rather be upfront about it. The environment, infrastructure-as-code, agent configuration, HIPAA guardrails, and care-gap rule engine are built and version-controlled. Loading synthetic patient data and running the full demo end-to-end is the next phase.
What AWS services does the architecture use?
Amazon Bedrock (Claude via Bedrock) for conversational AI with Bedrock Guardrails for HIPAA-aligned content boundaries, AWS HealthLake for a synthetic FHIR store, Amazon Comprehend Medical for clinical text extraction, and CloudWatch and S3 for interaction logging and audit.
Is any real patient data used?
No. No real PHI is used anywhere in this build, at any phase. All patient data is synthetic, generated specifically for this portfolio accelerator.
What metrics will this accelerator report once it's running?
Average agent response latency, containment and resolution rate, intake-form completion and accuracy rate, and cost per interaction based on real Bedrock token usage.
How is HIPAA compliance handled in the architecture?
Through Bedrock Guardrails enforcing content boundaries, Lake Formation fine-grained access-control policies, and full interaction logging to CloudWatch and S3 for audit, designed around HIPAA technical safeguards from the start.
Can Kriv AI build this out for our health system's environment?
Yes. A scoped engagement adapts this architecture to your existing AWS footprint and EHR integration points. Contact us to discuss scope and timeline.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
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