Product Summary

Clinova is a digital chronic UTI care platform supporting patient, provider, and administrative workflows. To help reduce manual effort and improve access to relevant information, smartData is implementing seven prioritized AI proof-of-concept workflows: staff message triage and routing, historical message analysis, an administrative support assistant, lab report extraction, patient lab and symptom trend summaries, appointment preparation summaries, and clinical documentation assistance. The POCs are designed to work with available Clinova records and workflows, with source traceability, permission-aware access, confidence and exception handling, and human review built into key steps. Examples include extracting structured values from lab PDFs for staff verification, preparing pre-visit briefs from existing patient history, and drafting clinical notes that remain pending clinician approval. The engagement is being delivered incrementally, with POC outputs and business value to be reviewed before deciding which capabilities should progress to lean MVPs.

Features

• Message Intelligence: Message Triage & Historical Analysis: classify and prioritize messages, identify communication patterns, and support staff-approved routing.
• AI Admin Assistant: Permission-aware answers to defined operational questions, with source records and audit logging.
• Lab Intelligence: Extract structured lab data from PDFs for verification; surface longitudinal lab and symptom trends for provider review.
• Appointment Preparation: Generate concise, source-linked pre-visit briefs from available patient history, medications, labs, and open items.
• Clinical Documentation: Draft structured clinical notes from visit data, with unsupported or missing information flagged and clinician sign-off required.

Technical Challenges

• Maintaining trust and traceability in AI-generated outputs. : Attach source references to AI statements, expose confidence where appropriate, version prompts, and log inputs, outputs, and staff actions.
• Handling clinical data extraction and incomplete or ambiguous records. : Use confidence and exception states, patient-match and duplicate checks, explicit missing-data indicators, and staff verification before committing extracted values.
• Preventing AI outputs from bypassing clinical and operational controls.    : Enforce role permissions, retain staff/clinician review, prohibit autonomous message replies or note sign-off, and provide fallback to existing workflows when AI is unavailable.