- Normalize data across five source systems with schema mismatches and missing IDs
- Select the correct package from 10,000+ near-identical combinations
- Apply state- and facility-specific compliance rules accurately
- Replace manual review cycles with faster, auditable decision workflows
Turning Healthcare Staffing Complexity into Auditable AI Decisions
A healthcare staffing automation provider transformed its manual onboarding workflow through an AI-native automation approach, replacing slow record reviews with fast, auditable decision execution. By connecting multiple source systems, validating schemas, reconciling IDs, and orchestrating compliance logic, the business reduced record processing from 45-75 minutes to 15-30 seconds while improving speed, consistency, and operational control.
- 97% Execution Time Cut
- 90% Single-Pass Accuracy
- 3.9K Hrs Saved Annually
Business Requirements
Healthcare staffing workflows involved complex package selection, credential checks, and compliance decisions across multiple systems. Each record required manual review, creating onboarding cycles that took 2-3 days and increasing the risk of delays, errors, and inconsistent decisions.
Kagen’s AI-native delivery model was used to structure the workflow before applying intelligence, ensuring each step could be validated, gated, and audited. The solution connected Zendesk, Bullhorn, AHA, ABLE, and Snowflake to create a near real-time automation layer for compliant healthcare staffing decisions.
Solutions
Multi-Source Data Ingestion
- Connected five source systems, including Zendesk, Bullhorn, AHA, ABLE, and Snowflake
- Normalized inconsistent schemas to create a validated data foundation
- Reconciled IDs and removed duplicates to improve record accuracy
Compliance-Aware Orchestration
- Built step-gated workflows to control how records moved through the automation process
- Applied state- and facility-specific compliance logic before decisions were finalized
- Created audit trails to make every recommendation traceable and reviewable
Agent-Based Decision Layer
- Deployed schema, package, and compliance agents to evaluate each record independently
- Used ADD-based validation to reduce risk across complex package combinations
- Matched records against 10,000+ near-identical package options with high accuracy
Execution, Monitoring & Replay
- Integrated rules, validators, and writers to execute approved decisions
- Added dashboards and monitoring to track workflow performance in production
- Enabled replay and standalone evaluation scripts to validate decisions continuously
Business Results & Impact
Reduced record processing time from 45-75 minutes to 15-30 seconds, achieving an execution time cut of nearly 97%. This helped shift healthcare staffing workflows from slow batch review to near real-time decision automation, accelerating onboarding without removing auditability.
Achieved around 90% single-pass accuracy across more than 10,000 package combinations by combining structured data validation, compliance agents, and independent validation pipelines. This reduced manual review dependency while improving consistency across state- and facility-specific requirements.
Saved approximately 3,900 hours of manual effort annually and reduced onboarding turnaround from 2-3 days to same-day execution. The result was a faster, more scalable healthcare staffing workflow that supported compliant decisions with clear audit logs, monitoring, and replay capability.
Explore More Customer Stories
Let’s Build Something Great Together
Tell us what challenges you're solving, and we’ll show you how we can help.
We're here to help. Reach out to us with any questions or inquiries.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Closing the AI Gap — From Vision to Reality.
