AI-Powered Intake Triage for Faster Clinical ReadinessAI-Powered Intake Triage for Faster Clinical Readiness

The client is a leading US-based healthcare organization specializing in independent medical review and clinical validation services. With over 40 years of experience, the client supports payers, employers, and government-aligned programs with evidence-based medical decisions in complex cases, including appeals of denied prior authorizations, where accuracy, HIPAA compliance, and auditability are critical.

Pre-submission intake was a critical control point in the client’s clinical review process. Every request had to be checked for completeness, eligibility, and scope before it could move to clinical teams.

As request volumes increased, manual intake review became difficult to scale. Nonclinical teams had to spend more time interpreting case packets, validating required information, and deciding whether a request could proceed.

The client needed a more consistent intake model that could reduce manual review burden, improve handoff quality, and support faster clinical readiness without compromising compliance or reviewer control.

Key Challenges

The client faced several interrelated challenges that slowed intake workflows and made it harder to maintain consistency across requests.

1. Review teams manually validated 40+ case-level attributes per request, increasing the risk of missed fields and inconsistent decisions.
2. Supporting documents arrived as scanned PDFs, forms, and handwritten notes, which made required information harder to find and verify.
3. Out-of-scope requests still consumed review time because teams had to inspect packets before deciding whether to proceed, request more information, or refuse the case.
4. Incomplete requests required clearer communication of deficiencies, but inconsistent reporting led to avoidable follow-ups.

Regulated intake workflows required more than an AI extraction layer. The client needed a partner that understood clinical review operations, secure healthcare integrations, reviewer workflows, and the audit expectations behind every intake decision.

Coditas brought healthcare domain context, Multi-Agent Systems capability, and AI-Driven User-Centric Design into one delivery model. This gave the client confidence that AI could support triage, validation, and deficiency handling while nonclinical reviewers retained control over final case movement.

We built an AI-powered Pre-Clinical Validation and Readiness Engine for regulated intake workflows. The platform processed inbound case packets, extracted required information from structured data and supporting documents, and helped intake teams determine whether a request was complete, in scope, or missing critical details.

The solution combined AI-assisted extraction, Google Vision OCR, MCP-backed access to SQL and file sources, and a configurable rule engine. Extracted fields were checked against intake rules, missing or conflicting information was flagged, and outputs were structured in JSON so reviewers could review the extracted fields, validation results, and deficiency outputs behind each intake recommendation.

The workflow supported triage, completeness validation, scope checks, and deficiency reporting without removing human control. Reviewers used the system’s guidance to request more information, refuse out-of-scope cases, or move complete packets forward with clearer documentation.

Gemini, Google Vision OCR, MCP Server for SQL, MCP Server for File Access, Configurable Rule Engine, and JSON output schema

1. 80% reduction in manual effort by automating extraction and readiness checks across 40+ required attributes
2. 95% data accuracy through rule-based validation and AI-assisted extraction
3. Reduced clinical rework by identifying incomplete and out-of-scope packets earlier
4. Clearer requester follow-ups through structured deficiency reporting
5. Improved audit support through consistent documentation of intake decisions

The engagement strengthened a critical intake gate where completeness, scope, and compliance directly affect clinical turnaround. Nonclinical teams gained a faster, more consistent way to evaluate inbound requests, while clinical reviewers received cleaner handoffs backed by documented validation outputs.

For our team, the project shows how AI can support regulated healthcare workflows when paired with clear rules, human oversight, and audit-ready evidence.