AI Workflow Automation
Replace manual, repetitive knowledge work with AI automation — prior authorisations, clinical document processing, intelligent data extraction, and agentic workflows that operate at scale.
The Challenge
Manual Knowledge Work Is Expensive — AI Automation Changes the Economics
Healthcare organisations spend billions of hours per year on tasks that are fundamentally about reading documents, applying rules, and making decisions — prior authorisation review, clinical coding, care plan documentation, quality measure abstraction, and regulatory reporting. These tasks are expensive when done by skilled humans, slow when volumes peak, and inconsistent when staff turnover is high. AI automation with modern LLMs (Claude, GPT-4o) and agentic architectures makes it economically viable to automate these knowledge-intensive workflows in a way that was impossible before. The key difference from traditional RPA: LLM-based automation handles unstructured text, adapts to variation in document formats, and reasons about context — instead of breaking when a field moves two pixels to the left.
Deliverables
AI Automation Capabilities
- Prior authorisation automation — extract clinical criteria from PA requests, match against payer guidelines, generate approval/denial recommendations with supporting evidence
- Clinical document intelligence — extract structured data from clinical notes, discharge summaries, lab reports, and imaging reports using LLMs with structured output schemas
- Intelligent document classification — classify inbound documents by type, urgency, and routing destination without manual triage
- Healthcare coding assistance — ICD-10 code suggestion from clinical documentation, CPT code validation, and HCC risk adjustment coding support
- Quality measure automation — automated abstraction of HEDIS, CMS, and facility-specific quality measures from clinical records
- Regulatory reporting automation — MDS, OASIS, and SNF quality reporting workflows with AI data extraction and validation
- Agentic workflow orchestration — multi-step AI agents that read inputs, apply reasoning, call external systems, and produce structured outputs without human intervention
- Human-in-the-loop design — escalation triggers for low-confidence cases, audit trails, and override mechanisms for compliance
- Integration with existing systems — connect AI automation to Epic, Cerner, and other EHR/practice management systems via FHIR APIs
- AI automation monitoring — accuracy tracking, throughput metrics, exception rates, and cost-per-task dashboards
Stack
AI Automation Stack
Process
AI Automation Delivery Process
A clear, predictable engagement model with no surprises.
Workflow Discovery & ROI Assessment
Map the current manual workflow — inputs, steps, decision rules, exception handling, and outputs. Quantify the time and cost of the current process. Identify the automation opportunity and define the ROI case before any development.
Accuracy Target & Evaluation Design
Define the accuracy target for the AI system — what precision and recall are required for the use case to be valuable? Build a labelled test dataset. Design the evaluation framework. Automation with unknown accuracy is not safe to deploy.
AI Pipeline Build & Tuning
Build the AI automation pipeline — document ingestion, LLM processing with structured output, business rule application, and output formatting. Iterate on prompts and architecture until evaluation targets are met.
Integration & Human-in-Loop Design
Integrate with existing source and destination systems. Implement confidence-based escalation — low-confidence outputs route to human review. Build audit trail and override capability for compliance.
Production Deployment & ROI Measurement
Go-live with full observability. Track throughput, accuracy, exception rate, cost-per-task, and human review rate. Measure actual ROI against pre-automation baseline and report results.
FAQ
Frequently Asked Questions
Ready to Automate a High-Volume Clinical Workflow?
Free 30-minute call — map your workflow, estimate the automation opportunity, and scope an initial build.
Response within 24 hours · No commitment required