In today’s healthcare landscape, clinical documentation is a significant burden on healthcare professionals, contributing to burnout and reducing time available for direct patient care. This blog post explores a solution that leverages generative AI to automate the process of transforming doctor-patient audio conversations into structured electronic health data that adheres to the FHIR (Fast Healthcare Interoperability Resources) standard.
Clinicians spend a significant amount of time on charting and paperwork, leading to inefficiencies and frustration. Redundant and inefficient workflows in Electronic Health Record (EHR) systems exacerbate this issue.
This project addresses this problem by building a generative AI-based pipeline that automates medical documentation. The system processes audio-based doctor-patient interactions and outputs structured, FHIR-compliant resources.
The AI-powered workflow consists of three main steps:
Imagine a patient mentioning they previously took Secnidazole for a stomach ailment. The AI agent recognizes this as relevant medical history and generates a MedicationStatement resource, linking it to other related resources like the Encounter resource for the current consultation.
The agent also enriches the generated data using its embedded knowledge of medical terminologies, automatically incorporating standardized codes from SNOMED CT and LOINC for fields such as CodeableConcept, ensuring interoperability and semantic precision.
This project demonstrates the potential of generative AI to revolutionize medical documentation, freeing up clinicians to focus on what matters most: patient care. By automating the generation of FHIR resources from doctor-patient conversations, we can create a more efficient and effective healthcare system.