Grant preparation consumes weeks of principal investigator and research administrator time: assembling CVs, building budgets, drafting justifications, compiling compliance certifications, and formatting documents to agency specifications. Our AI grant automation extracts this information from institutional systems, generates compliant documents, validates budget calculations, and assembles submission packages — reducing preparation time by 60-80 percent.

The AI pulls biosketch data from faculty CV systems, constructs NIH-formatted biosketches with correct page limits and formatting, extracts facilities descriptions from institutional boilerplate, generates current-and-pending support lists from previous submissions, and applies sponsor-specific templates automatically. Budget modules calculate fringe rates, apply institutional overhead policies, enforce funding agency cost principles, and flag unallowable expenses before submission.

For multi-PI proposals and collaborative projects spanning institutions, the automation coordinates document assembly across sites, consolidates sub-award budgets, generates consortium agreements, and maintains version control so all collaborators work from the current draft. Post-award administration handles progress reporting, financial reconciliation, and closeout documentation with the same automation framework.

Integration with Cayuse, InfoEd, SAGE, Click, and other research administration systems ensures seamless data flow. Compliance controls validate that personnel have required certifications, check conflict-of-interest disclosures, enforce cost-sharing policies, and generate audit-ready documentation for sponsor and institutional reviews.

IRB offices process hundreds of protocols annually with limited staff, creating review backlogs that delay research initiation. Manual protocol triage, completeness checks, and routing consume significant administrative time. Our IRB automation performs intelligent protocol classification, automated completeness validation, and workflow routing — reducing initial review time by 50-75 percent while maintaining regulatory rigor.

The AI reads submitted protocols, classifies risk level (exempt, expedited, full board), identifies missing required elements (consent forms, recruitment materials, HIPAA authorizations), checks that study personnel have required training certifications, and flags regulatory concerns for human review. Protocols that pass automated validation enter the review queue immediately; those with deficiencies return to investigators with specific remediation guidance.

For continuing review and amendments, the automation compares new submissions to approved protocols, highlights changes requiring board review versus administrative approval, generates redlined documents showing modifications, and maintains complete protocol version history. Board meeting preparation includes automated agenda generation, document packet assembly, conflict-of-interest screening for reviewers, and post-meeting approval letter generation.

Integration with IRBManager, iRIS, Huron, Cayuse Human Ethics, and other IRB management systems maintains existing workflows while adding intelligent automation layers. Compliance documentation satisfies OHRP and FDA requirements with complete audit trails, version control, and timestamped approvals.

Clinical trial coordinators manually extract data from medical records, lab reports, and case report forms into EDC systems — a time-consuming process vulnerable to transcription errors that compromise data integrity. Our trial data automation reads clinical source documents, extracts structured data points, populates EDC systems, and validates against protocol-specified rules — eliminating 90 percent of manual data entry while improving accuracy.

The AI handles diverse source documents: EHR clinical notes, radiology reports with DICOM imaging, lab results in HL7 feeds or PDF format, pathology reports, pharmacy dispensing records, and patient-reported outcomes. It extracts discrete data points (vital signs, lab values, adverse event descriptions, concomitant medications) and maps them to CDISC-compliant EDC fields with complete data lineage from source to database.

For multi-site trials, the automation standardizes data across institutions with different EHR systems, normalizes terminology variations, converts local lab units to standardized ranges, and applies protocol-specific eligibility criteria and event grading scales. Data quality checks flag out-of-range values, inconsistent timelines, and protocol deviations before they propagate through downstream analyses.

FDA 21 CFR Part 11 compliance is foundational: electronic signatures for data validation, complete audit trails, role-based access controls, and system validation documentation. Integration with Medidata Rave, Oracle Clinical, REDCap, and OnCore supports existing EDC infrastructure while accelerating data capture and improving quality.

Academic medical centers face the dual challenge of training clinicians while maintaining clinical productivity and revenue cycle efficiency. Clinical documentation consumes resident and attending time, coding inaccuracies lead to undercoding and lost revenue, and teaching workflows complicate already-complex EHR interactions. Our clinical automation addresses these challenges with AI documentation assistants, intelligent coding suggestions, and workflow optimization.

AI documentation captures patient encounters, generates structured notes incorporating trainee and attending input, suggests appropriate E/M coding levels based on documented complexity, and pre-populates EHR fields — reducing documentation time by 40-60 percent while improving coding accuracy and compliance. The system adapts to teaching workflows where residents draft notes and attendings attest, maintaining appropriate supervision documentation.

Revenue cycle optimization includes charge capture monitoring that flags missed billable services, coding validation that ensures diagnosis codes support medical necessity, prior authorization automation for high-cost procedures and medications, and denial management workflows that prioritize high-dollar appeals. Academic medical centers typically recover 15-25 percent more revenue through improved charge capture and coding optimization.

Integration with Epic, Cerner, and other academic medical center EHR platforms operates within existing clinical workflows. HIPAA compliance includes BAA-covered infrastructure, PHI encryption, role-based access, and audit logging. Documentation satisfies Medicare teaching physician requirements, GME supervision policies, and Joint Commission standards.

Research compliance reporting to NIH, NSF, FDA, and institutional committees requires aggregating data from dozens of systems: grant management, IRB databases, lab safety records, conflict-of-interest disclosures, research misconduct files, and animal care protocols. Manual compilation consumes weeks of research administrator time annually. Our research compliance automation aggregates this data automatically, generates required reports, and maintains audit-ready documentation.

The AI pulls data from disparate systems, applies agency-specific reporting formats, validates completeness against regulatory requirements, and flags potential compliance issues for administrator review. NIH RPPR reports generate automatically from financial systems and progress narratives. FDA annual reports for IND/IDE studies compile safety data, protocol amendments, and investigator changes. Institutional annual reports to AAHRPP, AAALAC, and accrediting bodies assemble metrics and narrative responses.

Ongoing compliance monitoring identifies expiring certifications (CITI training, animal protocol renewals, IRB continuing reviews), alerts responsible parties before deadlines, escalates overdue items to supervisors, and generates institutional dashboards showing compliance status across units. Research integrity workflows track disclosures, manage potential conflicts, and maintain documentation for audit defense.

Integration with Cayuse, Click, eRA Commons, and institutional compliance systems creates a unified data layer. The automation generates evidence packages for site visits, organizes documentation by regulatory requirement, and produces executive summaries for leadership reporting on institutional research compliance posture.

Clinical trial recruitment fails to meet enrollment targets 80 percent of the time, delaying study completion and limiting statistical power. Manual chart review to identify eligible patients is time-consuming and incomplete. Our recruitment automation mines EHR data to identify eligible candidates, automates outreach with IRB-approved messaging, and manages recruitment workflows — accelerating enrollment by 40-70 percent.

The AI reads trial eligibility criteria, queries EHR databases for patients matching inclusion criteria and lacking exclusion criteria, generates candidate lists ranked by eligibility strength, and alerts coordinators to potential participants. Automated outreach via patient portals, secure messaging, and phone contact maintains HIPAA compliance while scaling communication beyond manual capacity.

For enrolled participants, the automation manages appointment scheduling, sends visit reminders, distributes study questionnaires via REDCap or patient portals, tracks protocol adherence, and flags participants at risk of dropout for coordinator intervention. Post-study follow-up maintains longitudinal contact for extended monitoring periods without ongoing manual effort.

All patient communication includes IRB-approved language, tracks consent status, logs all interactions for regulatory audit trails, and respects patient preferences for contact methods and frequency. Integration with Epic, Cerner, OnCore, and clinical trial management systems ensures recruitment workflows operate within existing infrastructure while dramatically improving enrollment velocity.