The Role of Artificial Intelligence for the Application of Integrating Electronic Health Records and Patient-Generated Data in Clinical Decision Support

This narrative review explores how artificial intelligence (AI) can enhance clinical decision-making by integrating Electronic Health Records (EHRs) with Patient-Generated Health Data (PGHD). PGHD includes data such as symptoms, medical history, biometric info, and lifestyle data collected by patients or caregivers.

Key Benefits of EHR-Integrated PGHD with AI

    1. Improved Clinical Decision Support (CDS):
          • Enables better diagnosis, risk classification, and personalized care.
          • Reduces unnecessary clinical visits and hospital admissions.
          • Empowers shared decision-making between patients and providers.
    2. AI’s Roles in Integration:
          • Automates documentation, coding, and workflows.
          • Extracts insights through predictive modeling and machine learning.
          • Enhances diagnostic accuracy and tailors treatment options.
    3. Applications in Practice:
          • Administrative: Auto-fill, coding assistance.
          • Clinical Management: Treatment reminders, holistic patient views.
          • Diagnostics: Risk detection using NLP and image recognition.
          • Patient Support: Personalized health insights and tracking.
          • Cost Containment: Reduces duplicative testing and provider workload.
          • Workflow Improvement: Enhances data access and reduces manual tasks.

Challenges Identified

      • Data Overload: Massive volume of PGHD and EHR requires robust storage and processing.
      • Interoperability: Lack of standardized formats for data exchange.
      • Privacy/Security: Inconsistent safeguards for PGHD vs. traditional medical data.
      • Data Quality: Variability in PGHD accuracy (e.g., consumer vs clinical devices).
      • Equity: AI must avoid bias and support all patient populations fairly.

Recommendations

      • Use AI-assisted patient matching and data cleaning tools.
      • Implement API standards like FHIR and SMART on FHIR for seamless integration.
      • Promote biometric authentication and secure data access.
      • Apply machine learning and deep learning to develop actionable clinical insights.

Conclusion

AI-powered integration of EHRs with PGHD can revolutionize clinical decision support by offering a more complete and real-time view of patient health. While promising, challenges around standardization, privacy, and equity must be addressed. Further research and system-wide efforts are needed to support adoption and optimize outcomes.

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