Advancing the Management of Long COVID by Integrating into Health Informatics Domain: Current and Future Perspectives

Area of Science:

• Medical Informatics
• Public Health
• Artificial Intelligence

Background:

• The COVID-19 pandemic has led to millions affected globally, with a significant subset experiencing persistent symptoms, termed long COVID.
• Effective monitoring and management of long COVID sequelae are critical for public health research, prevention, and control strategies.
• Digital health informatics applications are vital for surveillance and maintaining up-to-date health records of affected individuals.

Purpose of the Study:

• To review existing literature on identifying and characterizing long COVID manifestations using hierarchical classification based on Human Phenotype Ontology (HPO).
• To explore the role of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiatives in AI-driven long COVID identification.
• To present a concept map of clinical pathways for long COVID, offering insights into data requirements and innovative frameworks for health informatics applications.

Main Methods:

• Comprehensive literature review on long COVID identification and characterization techniques.
• Exploration of Human Phenotype Ontology (HPO) for hierarchical classification of long COVID phenotypes.
• Analysis of AI applications within N3C and RECOVER initiatives for long COVID research.
• Knowledge exploration to develop a concept map of clinical pathways and data requirements.

Main Results:

• The study provides a comprehensive overview of current long COVID identification and characterization methods.
• It highlights the potential of HPO for structured phenotyping of long COVID manifestations.
• The research explores AI's role in large-scale cohort studies like N3C and RECOVER for understanding long COVID.
• A conceptual framework for health informatics apps is proposed, integrating long COVID as a variable risk factor.

Conclusions:

• This review is the first to explore the incorporation of long COVID as a variable risk factor in digital health informatics applications.
• The findings offer valuable insights into the challenges and public health impact of long COVID.
• The proposed frameworks can guide the development of innovative digital health solutions for managing long COVID's long-term effects.