Interoperability in the exchange of clinical data can have many benefits, but there are also risks such as data pollution.
Data pollution refers to inadvertent errors introduced into data.
Data poisoning refers to intentional errors in data.
Sources of data pollution:
(1) unreliable or uncalibrated devices
(2) errors in test performance
(3) errors in data recording
(4) errors in calculations
(5) data corruption during storage or transmission
(6) misinterpretation or lack of experience
(7) heterogenous construct definition
(8) bias
(9) missing data
If unreliable data is used, then potentially incorrect conclusions will be made.
