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Confidential client / Healthcare, clinical operations

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 / GOVERNED AUTOMATIONA protected document becomes a decision-ready referralIllustrative model / anonymized engagement
Protected clinical intake, extraction, and reviewFaxes, referral packets, authorization requests, and secure messages cross into a controlled PHI environment. Document classification and schema-constrained extraction produce fields for member, provider, authorization, and scheduling. Validation and risk-confidence rules send complete low-risk items to EHR and work queues, while ambiguous or high-risk items go to clinician review. Audit events and corrections support evaluation.Clinical document intake with human review forkInside a PHI-controlled environment, a referral is read into structured fields. A risk and confidence decision routes complete low-risk work to an EHR queue or ambiguous work to clinician review. The decisions are audited.PHI-CONTROLLED INTAKEFAX · PDF · MESSAGEReferralMemberProviderAuthorizationSchedulingRISK +CONFIDENCEEHR queueClinicianEXTRACT → VALIDATE → ROUTE · AUDIT EACH DECISIONDetailed governed referral intake on mobileA private intake boundary contains document OCR and classification, constrained field extraction, schema and consistency validation, a risk-confidence branch to EHR queues or clinician review, and an immutable audit trace.PHI BOUNDARY / CONTROLLED ACCESS01 / READ THE INBOUND OBJECTFAX · REFERRAL · AUTH · SECURE MESSAGE02 / STRUCTURED FIELD CONTRACT03 / RISK + CONFIDENCE GATEIMMUTABLE TRACE / SOURCE · EXTRACTION · ROUTE · REVIEW
Extract constrained fields from irregular documentsRoute by completeness, confidence, and riskKeep clinician authority and a trace of each decision
ClientMulti-hospital healthcare network
IndustryHealthcare, clinical operations
EngagementGoverned intake + AI triage
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.

  1. 01Mapped intake pathways by service line, risk level, and downstream routing destination.
  2. 02Defined strict extraction schemas for member, provider, authorization, and scheduling attributes.
  3. 03Implemented multi-step orchestration: classify, extract, validate, route, and summarize.
  4. 04Added confidence thresholds and business rules for automatic versus human-routed decisions.
  5. 05Integrated exception handling with existing queue tools and escalation protocols.
  6. 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 / GOVERNED AUTOMATIONA protected document becomes a decision-ready referralIllustrative model / anonymized engagement
Protected clinical intake, extraction, and reviewFaxes, referral packets, authorization requests, and secure messages cross into a controlled PHI environment. Document classification and schema-constrained extraction produce fields for member, provider, authorization, and scheduling. Validation and risk-confidence rules send complete low-risk items to EHR and work queues, while ambiguous or high-risk items go to clinician review. Audit events and corrections support evaluation.Clinical document intake with human review forkInside a PHI-controlled environment, a referral is read into structured fields. A risk and confidence decision routes complete low-risk work to an EHR queue or ambiguous work to clinician review. The decisions are audited.PHI-CONTROLLED INTAKEFAX · PDF · MESSAGEReferralMemberProviderAuthorizationSchedulingRISK +CONFIDENCEEHR queueClinicianEXTRACT → VALIDATE → ROUTE · AUDIT EACH DECISIONDetailed governed referral intake on mobileA private intake boundary contains document OCR and classification, constrained field extraction, schema and consistency validation, a risk-confidence branch to EHR queues or clinician review, and an immutable audit trace.PHI BOUNDARY / CONTROLLED ACCESS01 / READ THE INBOUND OBJECTFAX · REFERRAL · AUTH · SECURE MESSAGE02 / STRUCTURED FIELD CONTRACT03 / RISK + CONFIDENCE GATEIMMUTABLE TRACE / SOURCE · EXTRACTION · ROUTE · REVIEW
Extract constrained fields from irregular documentsRoute by completeness, confidence, and riskKeep clinician authority and a trace of each decision
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.

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