Managing Pandemics with Health Informatics: Successes and Challenges

Abstract

Introduction: The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic.

Methods: The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals.

Results: An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic.

Conclusion: COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.

Fig. 1

Dallas County lab confirmed COVID-19 occupied beds and ICU occupied beds. The prediction and uncertainty bands are created using a stochastic Markov SEIR model.

Fig. 2

Length of stay boxplot of COVID-19 admissions with color gradients showing weekly admission numbers. This visualization was made possible through the collaboration of regional clinical partners and aggregation of the encounter level data model that contains one entry per hospitalization.