Strategy for ensuring the quality, fit-for-purpose, and usefulness of available knowledge.

Detailed Description

The clinical interoperability strategy will result in two general categories of data: 1) Data about tests as a class, e.g. the IVD name, mapped code, and device that produces it; and 2) Individual testing data, i.e. the actual results being produced for use. A quality assurance strategy should aim to assess the quality of both sorts of data.

“Class” data quality assurance:

• Ensuring LIDR resources are generated according to defined practices

• Assuring IVDs are uniquely identifiable

• Assuring UDIs are generated according to defined practices

• Allowing laboratories to assess whether their testing menu is conformant to LIDR resources

• Allowing third parties to assess whether a laboratory’s menu is conformant to LIDR resources

“Instance” data quality assurance:

• Maintaining a repository of traceability/harmonization information

• Third party verification that individual testing data have mapping and harmonization elements required for interoperability

• Third party verification that individual tests do not have unexpected variation by performing laboratory

• Third party performance monitoring of devices and IVD kits

• IV&V on harmonized terminology

• Quality Assurance on knowledge representations

• Knowledge can be reliably represented but how do we mitigate “bad” knowledge

...