A Digital Twin is a concise, current, and true representation “in silico” of a functioning real-world entity. With origins in industry manufacturing and design, it can be used to assist the assembly of many complex and interacting parts prior to an analysis. In healthcare, the creation of a Digital Twin of a person consists of assembling data from many sources and calculating the assembled result to obtain an accurate representation of an individual. That representation can then be used with the assembles of other persons to run “in silico” studies. The Digital Twin makes possible more accurate population studies based upon real world data (RWD). Performing an extra layer of data reconciliation in the form of producing Digital Twins allows a population study based in Digital Twins to arrive at more accurate conclusions than when using raw data.

In this bundle, we will begin to roll out beta versions of our i2b2 Digital Twin Tools:

1) Loyalty Cohorts: Determination that data completeness is sufficient for creation of a Digital Twin. This is done through calculation of a “loyalty cohort” to assured that most of the care is received in the hospital systems producing the data set that is used for calculation of the twin.1,2 This step will provide the logic to exclude the conditions the individual does NOT have, as well as assure there is sufficient data to calculate the conditions that the individual does have.