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Clinician-in-the-loop automated intake in integrative medicine: A multi-component quality-improvement evaluation
Oleksandr Sverlov1, Gregory Brodski1, Alex Tsatsulin1
1Autonomous Health, 399 Boylston St, Suite 650, Boston, MA, 02215, USA.
Background:
Integrative medicine intake requires a multidomain assessment. However, the necessary information is often scattered across electronic health records (EHRs), laboratory reports, clinical notes, and patient uploads, creating pre-visit and post-visit bottlenecks for care planning and insurance-support documentation.
Objective:
To introduce and evaluate a clinician-in-the-loop, automated, artificial intelligence (AI)-assisted intake model designed for data completeness, clinical usability, and reimbursement readiness improvement.
Methods:
To support the objectives, the system connects to compatible Fast Healthcare Interoperability Resources (FHIR)-compliant EHR and patient-portal sources, accepts patient uploads of fragmented records, consolidates available information, identifies gaps, generates adaptive intake questions, and prepares a clinician-facing summary. This quality-improvement evaluation examined information added or clarified after traditional intake, with manual verification against source records and patient responses. Automated laboratory interpretation and draft treatment strategies were assessed through clinician review.
Results:
Autonomous Health (AH) Intake recovered previously undocumented medication history and adverse reactions and organized functional limitations, safety concerns, and supporting test results for clinician review and prior-authorization submission. Using the methylation workflow as an example, we demonstrated that treatment strategies were approved without modification in 22/25 cases (88%); clinician review took approximately 10-15 minutes per completed case. In the separate laboratory test results evaluation, concordance with clinician review was 100% for 1927 extracted biomarker values and 72% (18/25) for overall care-plan summaries.
Conclusions:
The findings support the feasibility of automated, interactive, AI-assisted intake as a preparatory and documentation-support workflow for clinician review.
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