It doesn't seem that hard or labor-intensive to me. Have volunteers who were going to have a battery of tests done at just one provider visit a second the same day. (At most, 2x the retail testing costs – but far less if any of the providers is a partner in the study.)
When the results come back, tabulate and compare.
If there are major discrepancies, they'd probably turn up in the first few dozen tests. But you could run this for hundreds of people with a single analyst, and just a few doctors soliciting their patients' participation, and without needing cooperation from Theranos.
But that's not the problem: the problem is you need to study how the test behaves on given conditions. There's a serious issue that a bunch of the type of people you could recruit for such a study, are likely to have ordinary looking bloodwork. Designing a test which mostly gives results that show "ordinary" is actually fairly easy - designing one which correctly picks up unusual or important conditions is harder.
To be valid, you'd want to go in and supply a bunch of baked-samples which should produce a certain definitive result, without the tester knowing this in advance.
But suppose you were running this 2-channel validation constantly, for people with both routine and exceptional needs. You'd be getting fresh information, in exactly real-need/real-disorder proportions, from both the traditional and new tests, in the cheapest and fastest way possible.
Storing 'baked-sample' blood, or doing extra tests against people who weren't otherwise needing tests, couldn't possibly be as representative. Diverting a few drops from tests that are already going through the traditional process, for research/calibration purposes, is likely already authorized by existing patient relationiships. Your "n" for analysis, across any disorder or rare test, would quickly exceed what's "recruitable" for some formal study.
Look at the list of Theranos partners/customers who have brought them to "cash-flow positive": hospitals, drug companies, insurance companies, and the US military.
It's naive to the ways of industry R&D to assume that such constant cross-validation hasn't been (and isn't still) happening.
When the results come back, tabulate and compare.
If there are major discrepancies, they'd probably turn up in the first few dozen tests. But you could run this for hundreds of people with a single analyst, and just a few doctors soliciting their patients' participation, and without needing cooperation from Theranos.