Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic...

Area of Science:

• Public Health
• Health Informatics
• Epidemiology

Background:

• The COVID-19 pandemic exposed challenges in managing infectious diseases.
• Timely adoption of learning healthcare systems (LHS) pillars—Knowledge, Data, Practice—could have mitigated global healthcare crises.
• Diverse datasets generated during COVID-19 offer opportunities for advanced analytics.

Purpose of the Study:

• To examine challenges and opportunities presented by COVID-19 for future pandemic preparedness.
• To propose strategies for utilizing AI and predictive modeling to enhance LHS functioning.
• To highlight the role of policymakers in supporting AI-driven healthcare initiatives.

Main Methods:

• Analysis of the COVID-19 pandemic's trajectory and data generation.
• Proposal of strategies for harnessing data through AI and predictive modeling.
• Review of potential applications of AI in infectious disease management and LHS implementation.

Main Results:

• AI and predictive models can significantly support patient and clinician management of infectious diseases.
• AI tools can aid in early disease detection, patient triage, outcome prediction, and monitoring of viral evolution and treatment efficacy.
• A patient-centric approach is crucial for effective disease mitigation.

Conclusions:

• Implementing AI and predictive analytics is vital for strengthening learning healthcare systems (LHS).
• These technologies can improve preparedness for future pandemics by enabling proactive disease management and real-time assessment of interventions.
• Legislative and policy support is essential for the successful integration of AI into healthcare systems.