Data quality monitoring is necessary for accurate disease surveillance. However it can be challenging, especially when “real-time” data are required. Data quality has been broadly defined as the degree to which data are suitable for use by data consumers. When compromised at any point in a health information system, data of low quality can impair the detection of data anomalies, delay the response to emerging health threats, and result in inefficient use of staff and financial resources. While the impacts of poor data quality on biosurveillance are largely unknown, and vary depending on field and business processes, the information management literature includes estimates for increased costs amounting to 8-12% of organizational revenue and, in general, poorer decisions that take longer to make.
Objective
To highlight how data quality has been discussed in the biosur- veillance literature in order to identify current gaps in knowledge and areas for future research.