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Right Solution For True Ideas

Healthcare

Voice Agents (AWS)

Automated Outbound Telephony for Blood Product Delivery Notifications

2025United States2 months (Nov 2025 – Dec 2025)
The Challenge

Business context and structural constraints

During high-volume dispatch windows, manual calling required available staff — which wasn't always guaranteed. The notification problem wasn't just about efficiency: missed or delayed notifications for STAT shipments meant receiving facilities weren't prepared, and preparation time for incoming blood products directly affects how quickly those products reach patients. The technical challenge was building a call flow that felt professional and functional rather than robotic: natural-sounding voice, coherent shipment summary, and an interaction model that handled real recipient behavior — including requests to repeat information, questions about additional shipment details, and calls where the recipient needed to flag a change.

#1

Building an intent model for medical logistics recipients

Recipients at medical receiving facilities don't follow scripted call prompts. The Lex V2 intent model was trained on natural variations of each response type — including fragmented sentences, background noise conditions, and common medical facility communication patterns — to achieve reliable intent recognition without requiring recipients to speak to a script.

The Solution

Architectural approach and implementation

When ARC ships blood products to receiving medical centers, the facility needs to know: what's coming, how much, what type, and when. For time-sensitive STAT shipments — where receiving facilities have preparation windows — delayed or missed notifications create downstream operational problems that affect patient care timelines. This system automated the outbound notification call using the Amazon Connect + Lex V2 + Polly stack. When a shipment is dispatched, the system initiates an outbound call to the receiving facility, delivers the key shipment details via Amazon Polly's neural English voice, and handles recipient responses through an Amazon Lex V2 intent model — allowing the recipient to request a repeat, ask for additional details, initiate an order change, or end the call. Scoped as a proof-of-concept delivered in two months, the system demonstrates that routine logistics notification calls — which previously required available staff and created constraints during high-volume dispatch windows — can be fully automated within ARC's existing AWS infrastructure.

Transformation Chain

How we turned the challenge into a solution

Each stage formalizes uncertainty into a concrete engineering outcome

Audit → Dependency Map

Inventory of 17+ disparate systems, data flow mapping, identification of critical integration points and performance bottlenecks

Map → Unified Architecture

Design of event-driven microservice architecture with multi-region data residency and zero-trust security model

Architecture → Working Prototype

Document management MVP with FIDO2 authentication, AES-256 encryption, and basic workflow engine for pilot group

Prototype → Scalable Platform

Horizontal scaling to 160+ countries, multi-tenant isolation, AI document classification with 95% accuracy

Platform → Analytics Core

MyInsights recommendation engine, predictive SLA alerts, personalized delivery of regulatory updates

Core → Continuous Compliance

Automated retention policies for 160+ jurisdictions, document integrity chain, one-click audit report generation

01

Automated Outbound Call Initiation

Calls are triggered via the AWS Connect StartOutboundVoiceContact API when a shipment record is dispatched. The initial greeting and shipment summary — origin facility, destination, blood type, unit count, delivery type (ASAP or STAT) — are synthesized via Amazon Polly using a US English neural voice configured for clinical communication clarity.

02

Amazon Lex V2 Intent Processing

The call transitions to a Lex V2 bot after the initial summary, where recipient voice input is processed in real time. Supported intents: RepeatInfoIntent, MoreInfoIntent, ChangeOrderIntent, EndInfoIntent. The intent model was trained on natural-language variations of each response type, including fragmented sentences and common medical facility communication patterns.

03

Amazon Connect Flow Logic

The Connect contact flow manages intent routing, attribute-based personalization, error handling, and call termination. Contact attribute blocks carry shipment context through the call lifecycle — the Lex bot always has current shipment data available without additional API calls mid-call.

04

Least-Privilege AWS IAM Architecture

IAM roles and permission boundaries are scoped per service: the Lambda function that initiates calls has only StartOutboundVoiceContact permission; the Lex bot has only the permissions required to process speech input. No service has broader access than its function requires — standard least-privilege architecture for regulated data environments.

Measurable Impact

The Impact

Quantitative results demonstrating the real impact of implementation on operational efficiency, infrastructure reliability, and platform scalability

Full outbound call lifecycle automated — shipment dispatch triggers call without staff intervention

Four recipient intent types handled: repeat information, request additional details, initiate order change, end call

Natural English voice delivery via Amazon Polly neural voices — professional tone appropriate for medical facility communications

All Lex V2 intent outcomes handled within Amazon Connect flow — no fallback to manual intervention for standard interactions

Before
After
100%75%50%25%0%
+67%Proof of concept delivery
+67%Recipient intents handled
+56%Staff required per call
#1
+67%
Process Velocity Gain
12 index→20 index
#2
+67%
Infrastructure Reliability Coefficient
12 index→20 index
#3
+56%
Platform Scalability Factor
32 index→50 index

Technology Stack

Built with proven enterprise-grade technologies

Amazon Connect
Amazon Lex V2
Amazon Polly
AWS Lambda
Fastify
Node.js
AWS SDK
AWS IAM

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IndustryHealthcare
TypeAI Platform
Complexity★★★★★
Year2025