This is a very good question. Actually two:
- How short Observation Periods can be (longitudinal or horizontal separation of data)
- What do I do if I know I capture only a fraction of the data (vertical separation of data)
For 1): The idea with EHRs is that if you were sick you would return to the same hospital you went last time, and which probably is the closest to you. I know this is not a given. But the likelihood is there, so people make this approximation. They have nothing else to hang on to. Which means, if there are no data then you are healthy enough not to be in the hospital (even though you might be in a different one). For use cases that look at events inside a visit this is probably fine (rarely people get referred from one hospital to another just like that). For use cases that cover longer times (like studies with long-term follow up) this might create “Axiom 2” errors = You underestimate the rate of events because you would wrongly interpret the whitespace as a time without events.
The idea of making mini-Observation Periods around each visit is dangerous. Because you need the whitespace in-between for the correct prevalence assessment. Otherwise it looks like a patient is “always” in the hospital, or “always” has an asthma exacerbation, because during an Observation Period they do, and outside you are not supposed to look. You can also never define a washout period. In the extreme case, you have one-day Observation Periods: Everything has a prevalence of 100%. So, don’t throw away the whitespace.
That’s fine. I haven’t been in a hospital for 10 years thank God. But if something happened I would have gone.
For 2) This is really a problem for the Metadata and Annotation Workgroup. They need to solve the problem how to capture the fact that there is only partial information available.