Researchers and provider organizations have increasingly embraced artificial intelligence (AI) and machine learning (ML) tools to reduce and track the spread of COVID-19 and to improve their surveillance efforts.
While organizations should focus on immediate data-supported tactics, such as improving case detection, reducing transmission, and managing supplies, the longer-term strategies of adjusting care delivery, risk contracts, and operational processes cannot be neglected.
Machine learning allows health systems to break out of literature-based models, deliberately limited to relatively simple constructs, and finite numbers of potential contributing factors to allow clinical teams to find the data elements and perform the calculations manually.
Big data analytics and advanced AI integrated systems have helped health experts to stay ahead of the pandemic from predicting patient outcomes to anticipating future hotspots, resulting in more efficient care delivery.
With an ever-changing understanding of COVID-19 and a continually fluctuating disease impact, health systems can’t rely on a single, rigid plan to guide their response and recovery efforts.
The framework which include data-supported surveillance and containment strategies to enhance detection, manage capacity and supplies, providing a roadmap to respond to immediate support a sustainable long-term pandemic response.