Data empowerment of decision-makers in an era of a pandemic: Intersection of "classic" and artificial intelligence in the service of medicine

Gil A. Geva, Itay Ketko, Maya Nitecki, Shoham Simon, Barr Inbar, Itay Toledo, Michael Shapiro, Barak Vaturi, Yoni Votta, Daniel Filler, Roey Yosef, Sagi A. Shpitzer, Nabil Hir, Michal Peri Markovich, Shachar Shapira, Noam Fink, Elon Glasberg, Ariel Furer*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Background: The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. Objective: We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. Methods: Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. Results: Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%).Conclusions: In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.

Original languageEnglish
Article numbere24295
JournalJournal of Medical Internet Research
Issue number9
StatePublished - Sep 2021
Externally publishedYes


  • Accuracy
  • Covid-19
  • Data
  • Data analysis
  • Decision-making
  • Medical informatics
  • Pandemic
  • Policy
  • Validation


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