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Demonstration and educational project. Not medical, legal, or regulatory advice. Every participant, record, and organization shown is fictional or illustrative. Sample and template language must be reviewed and adapted with your own IRB and legal counsel before any real use. Not affiliated with, endorsed by, or representing any advocacy group, registry, company, or institution named.

Applications · fictional data

Record status & reports

The registry-side view of the same cohort. What has arrived, from whom, through which pathway, and where the gaps are. Every figure is invented, but the shape is deliberately realistic, including the parts a demo usually hides.

30participants enrolled
25full consent5 partial
20with EHR records67% of enrolled
4,191EHR documents received

The number to look at

10 of 30 enrolled participants have no records at all, they consented, answered at least one module, and then either did not start the records step or did not finish it. That gap is the single most common surprise in registry operations, and it is a design problem rather than a participant failing. Plan for it in your power calculation and in your follow-up workflow.

EHR record status EHR

One row per participant, tracking electronic health record acquisition only. The modules column alongside it is survey progress Survey. The two advance independently, and a participant can be complete on one and absent on the other.

Reports

Enrollment over time

New participants per quarter. A flat or declining curve after an initial launch spike is the normal pattern, and the reason recruitment needs an owner rather than a campaign.

Survey module completion Survey

Completions out of 30 enrolled. The fall-off after the first two modules is typical and is why sequencing drives completion. Whatever you most need answered should not be module five.

How EHR records arrived EHR

EHR records received by category EHR

Counts across the whole cohort. Labs and vitals dominate every EHR-derived dataset; conditions and medications are far scarcer and are usually what the research question actually needs.

Data quality indicators

Track these every load and set a threshold that fails the run rather than being noticed afterward. Note that a mapped rate says nothing about whether a mapping is correct, only clinical spot review catches that.