Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients(심부전 환자 관리에서 현장 진료 초음파 데이터를 수집하고 구현하기 위한 워크플로우 및 프레임워크)

Executive Industry Relevance

Standardized integration of point-of-care ultrasound (POCUS) data into electronic health records enables real-time, quantitative monitoring of physiological changes in heart failure patients. This workflow supports scalable, reproducible data capture for longitudinal assessment and treatment response, directly impacting predictive confidence in clinical management. The approach provides a template for enterprise-level adoption of imaging biomarkers in disease monitoring pipelines.

Strategic Applications in Biopharma R&D

Early Discovery & Target Validation

• Enables objective, serial measurement of physiological parameters relevant to disease progression.
• Supports biological de-risking by providing real-time imaging data for functional validation.
• Facilitates hypothesis testing regarding treatment effects on central venous pressure and fluid status.

Screening & Assay Development

• Standardizes imaging data collection for reproducible quantitative outputs across patient cohorts.
• Prepares validated imaging endpoints for downstream analysis and comparison.
• Enables scalable data capture suitable for integration into digital health platforms.

Translational & Preclinical Research

• Aligns imaging biomarkers with clinical endpoints for translational continuity.
• Supports risk-adjusted advancement decisions by providing actionable, patient-level data.
• Facilitates database creation for retrospective and prospective analyses of treatment response.

Pipeline & Workflow Integration

This POCUS workflow bridges clinical data acquisition with digital record integration, supporting discovery through translational research phases.

• Discovery Biology: Provides real-time physiological readouts for hypothesis testing and pathway clarification.
• Screening: Delivers standardized, reproducible imaging data for quantitative assessment.
• Analytics: Enables direct comparison of serial measurements and treatment responses.
• Translational Research: Connects imaging endpoints to clinical management and outcome tracking.
• Enterprise Reuse: Establishes a scalable framework for imaging data integration across studies and sites.

Operational & Enterprise Impact

• Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in patient monitoring.
• Operational Value: Promotes standardization, reproducibility, and scalability of imaging workflows.
• Strategic Value: Improves go/no-go decision-making and capital efficiency by enabling timely, data-driven interventions.
• Portfolio Impact: Supports risk-adjusted prioritization and advancement of digital health-enabled monitoring strategies.

Implementation Considerations

• Requires expertise in ultrasound imaging and data interpretation.
• Needs access to calibrated POCUS devices and electronic health record integration.
• Demands cross-team standardization of data fields and workflow steps.
• Must adapt protocols for different patient populations and clinical settings.
• Considers limitations in device availability and user training as operational barriers.

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