AI Referral Readiness & Authorization Workbench

Specialty practices often receive referral information across scanned forms, attachments, and separate administrative records. This custom workbench would give coordinators one place to assess whether a case contains the information their workflow requires. Its purpose is to organize preparation work, expose missing items, and clarify who owns the next administrative action.

Suitable use cases include incomplete referral follow-up, specialty-specific packet preparation, and authorization documentation review across multiple locations. Operational teams could compare reasons for rework without treating readiness as a clinical recommendation. The application would not diagnose patients, recommend treatment, determine medical necessity, or promise that a payer will approve a request.

AI-assisted extraction would propose structured fields while retaining page references and supporting text. Coordinators could accept, correct, or reject those suggestions before packet assembly. Deterministic checks would handle required fields and attachment rules, while uncertain identity matches, conflicting values, and unreadable pages would enter a review queue rather than silently passing.

Packet drafts would use verified case information and approved templates, with missing evidence clearly marked. Reviewers would see the checklist version and guidance used during preparation. External messages, exports, and any implemented submission action would require the configured approval gate, preserving a clear separation between drafting assistance and consequential operational actions.

Potential integration scope includes EHR referral records, document repositories, payer status feeds, identity services, and approved messaging systems. FHIR, HL7, REST APIs, or secure file exchange may be appropriate depending on the systems involved. Every connector would require access assessment, field mapping, permissions review, and testing; none is represented as already operational.

OpenAI, Anthropic, or a self-hosted model could be evaluated as proposed inference options, subject to contractual terms and approved data handling. Where an external system lacks an authorized interface, the workflow could retain a manual handoff. Portal automation would require explicit permission and feasibility review rather than assuming unrestricted access or reliable compatibility.

DOD IT Solutions can scope a hosted SaaS deployment or a client-server installation within an approved client environment. Discovery would establish tenancy, data residency, identity integration, backup ownership, and operational support boundaries. Infrastructure choices would follow these requirements instead of assuming that every healthcare organization can use the same hosting arrangement.

Client-server deployment can place application services and storage under client control, but it does not automatically prevent external data transfer. Model calls, telemetry, OCR services, and connector traffic must each be assessed. An optional local-processing architecture would require suitable infrastructure, model evaluation, maintenance planning, and explicit agreement on its operational limitations.

The knowledge layer would use client-approved referral procedures, payer guidance, and specialty checklists with identifiable owners and effective dates. Retrieval should favor relevant, current material and surface conflicting or expired guidance for review. A payer document would be treated as reference material, not as permission to override application controls or execute actions.

Adaptation would begin with extraction schemas, terminology mapping, prompts, and representative evaluation cases rather than automatic training on patient records. Reviewers would measure unsupported statements, field accuracy, and missing-item detection across document types. Optional fine-tuning would require separate authorization, appropriate datasets, documented evaluation, and a rollback plan before production consideration.

The security design would combine least-privilege access, encrypted storage and transport, managed secrets, and auditable administrative actions. Case access and export rights would be tested independently of the interface. Sensitive content would be excluded from routine logs where possible, with restricted diagnostic workflows for investigating processing failures involving protected information.

Uploaded files and retrieved text would remain untrusted inputs, with tool permissions enforced outside model responses. Live-data use would depend on approved processing arrangements and applicable agreements, including business associate agreements where required. These measures support organizational review; they do not establish certification, replace legal advice, or guarantee compliance in every deployment.

The agreed delivery package would include full custom application source code, implemented connector code, database migrations, deployment configuration, test assets, and administrator documentation. Knowledge-loading procedures and operational runbooks would explain how to maintain approved content and recover failed jobs. Third-party components and model services would be identified separately with their licensing and access dependencies.

Acceptance testing would use agreed sample packets and workflows to assess extraction accuracy, checklist behavior, reviewer corrections, access controls, and failure recovery. A limited operational pilot could establish baseline measures before broader rollout. Training, maintenance scope, and change-control responsibilities would be agreed explicitly, without promising fixed business outcomes or instant production readiness.