Skip to content

Appendix: Which OHDSI Tool Answers Each Check

Reference. Not part of the 30-minute clock.

The six feasibility checks are not abstract. Each one is answered by a specific tool in the OHDSI stack. This appendix maps them so you know exactly where to look, and it is where the demo's habits connect to the daily curriculum.

The mapping

# Feasibility check Primary tool What you do with it
1 Concepts exist (and are standard) Athena and ATLAS → Search Search the vocabularies; confirm a standard concept, its domain, and its class before you build anything
2 Concepts are present in the data Achilles and ATLAS concept set → Included Concepts Achilles profiles record and person counts per concept in a source; the concept-set panel shows what your rules resolve to
3 Population fits Achilles (age/sex/observation profiles), surfaced in ATLAS → Data Sources Read the age and sex distribution of the source; this is what tells you SynPUF is 65+
4 Time can be anchored ATLAS → Cohort Definition, then Cohort Diagnostics Define the index event and windows; Cohort Diagnostics breaks down index-event timing and observation time; pregnancy needs a pregnancy episode algorithm
5 Sample size is sufficient ATLAS cohort generation (attrition), then Cohort Diagnostics (incidence, cohort counts) Generate to see counts and attrition locally; Cohort Diagnostics is the standard pre-network check across databases
6 Governance clears Data Quality Dashboard (DQD) for data trust, plus institutional IRB/DUA DQD tells you whether the data are trustworthy enough to act on; governance itself is a steward and IRB question, not a tool

Notes on the workhorses

Achilles is the one that answers the most feasibility questions before you build anything. It is a characterization run over a data source that produces counts by domain, concept, age, sex, and calendar time. If your steward has run it and can show you the results, checks 2 and 3 are often answered in a single browse.

Cohort Diagnostics is the bridge from a single-site feasibility check to a network study. It runs a battery of diagnostics on one or more cohort definitions (incidence, index-event breakdown, cohort characterization, concept overlap) and is the standard artifact reviewers expect before a network study is distributed. When you are ready to move from "feasible here" to "feasible across the network", this is the tool.

The Data Quality Dashboard (DQD) does not tell you whether your question is answerable; it tells you whether the answers can be trusted. A source can contain your population and still fail data-quality checks that would undermine the study. DQD sits behind check 6 for that reason: governance is partly about permission and partly about whether the data are fit for the purpose.

How this connects to the curriculum

The daily curriculum teaches each of these tools in depth. This module's contribution is the discipline of reaching for them in the right order, before a protocol exists, so a dead end costs minutes instead of months.