From unstructured chaos to governed LLM workflows in healthcare intake
Stakforge designed and deployed a production-grade intake and triage platform for a multi-hospital network, reducing manual review burden while meeting strict security and compliance expectations.
LLM SystemsHealthcareRegulated Environment
Clinical intake / confidence-routed taskingA referral becomes a live entity with a controlled task path
01 / ObserveDocuments, messages, authorization, and EHR context
02 / ModelLive patient and referral state with provenance
03 / CoordinateConfidence-aware extraction and routing tasks
04 / SuperviseClinical review and correction feedback
ClientMulti-hospital healthcare network
IndustryHealthcare, clinical operations
Engagement18 weeks
DeliveryStrategy through implementation
The network processed high volumes of inbound faxes, referral packets, prior auth requests, and clinical messages with inconsistent formats. Intake teams manually parsed documents and keyed data into downstream systems.
Queue delays routinely affected scheduling and care coordination, while repeated manual entry introduced avoidable data quality issues.
Security and compliance teams also required strict PHI controls, making unmanaged AI tooling a non-starter.
Manual intake queues often ran 24 to 48 hours behind target SLAs
Frequent extraction and data-entry errors created rework loops
Limited end-to-end visibility into queue health and triage outcomes
Need for AI acceleration under strict healthcare governance constraints
We designed the workflow around clinical reality: different document types, varying data quality, and clear handoff points between automation and human review.
01Mapped intake pathways by service line, risk level, and downstream routing destination.
02Defined strict extraction schemas for member, provider, authorization, and scheduling attributes.
03Implemented multi-step orchestration: classify, extract, validate, route, and summarize.
04Added confidence thresholds and business rules for automatic versus human-routed decisions.
05Integrated exception handling with existing queue tools and escalation protocols.
06Established quality-review loops and prompt/model evaluation checkpoints.
How the system works
Technology implementation
The platform used private, policy-governed AI services integrated with healthcare workflow systems.
Clinical intake / confidence-routed taskingA referral becomes a live entity with a controlled task path
01 / ObserveDocuments, messages, authorization, and EHR context
02 / ModelLive patient and referral state with provenance
03 / CoordinateConfidence-aware extraction and routing tasks
04 / SuperviseClinical review and correction feedback
01
Document ingestion and OCR pipeline for fax, PDF, and secure message sources
02
Private LLM endpoints with schema-constrained extraction and classification
03
Retrieval layer for routing rules, policy references, and service-line context
04
Validation engine for required fields, formatting, and cross-field consistency
05
Event-driven integration with EHR and work-queue systems
06
PHI redaction, access controls, and immutable audit logging across workflow steps
Verified outcomes
Results
The network materially improved intake throughput and quality while preserving governance standards expected in clinical operations.
Staff focused more on exception resolution and patient-critical decision points instead of repetitive first-pass sorting.
01
58% reduction in manual review time for targeted intake queues
02
35% fewer structured field data-entry errors
03
92% of low-risk messages auto-triaged with exception routing
04
Zero unapproved data egress events in production
Engagement goals
Stakforge aligned operational and compliance stakeholders around a shared objective: accelerate intake without introducing PHI risk or clinical ambiguity.
Client benefits and clinical operations impact
Faster intake translated into earlier downstream action for scheduling, authorizations, and care navigation teams.
Compliance stakeholders gained confidence through transparent controls and auditable workflow traces, enabling broader support for future AI use cases.
The platform architecture now serves as a template for expanding AI-assisted workflows into adjacent clinical operations functions.
Why Stakforge
The network needed more than a prototype. It needed a partner with deep healthcare constraints awareness and production AI delivery discipline.
Stakforge delivered a governed workflow system that improved day-to-day operations and set a reliable foundation for scaled AI adoption.