Even if we had well defined schemas/data models, and profiles thereof that guide the way data is captured,
it is almost never the case that the (patient’s) instance data is perfect.
In fact, imperfection can arise from multiple sources: missing data, uncertainty, confidence/error ranges, vagueness, etc.
(see e.g. https://www.researchgate.net/figure/different-kinds-of-information-imperfection-18_fig2_257211032)
Neither data elements nor the models processing them should ignore this imperfection, otherwise
leading to a scenario that is commonly referred to as ‘garbage in-garbage out’
Options:
(1) Do not execute unless all data is available and valid(ated)
(2) Look for alternatives (including humans)
(3) Ensure that the data is present implementing processes upstream.
(4) Acquire the .
(5) Run a range of values for missing data and see if outcome changes.