Research data module: missingness checks in 30 fictional follow-up cases
Real sources · Illustrative request · Sample version
Customer question
Illustrative situation: 30 fictional cases across 90 pages require baseline and three-month follow-up field checks.
Which fields have sources and confirmation? Which follow-up is undocumented rather than negative?
Deliver traceable fields and gaps first. Complete counts do not establish effectiveness or extraction accuracy.
Confirmed fields in 30 fictional cases
All counts are constructed demonstration data, not accuracy measures, a real cohort or clinical effects. 24 + 6 = 30.
Analysis excerpt
| Check | Finding / sample content | Use |
|---|---|---|
| Example field | DEMO-01 follow-up lacks a source page | Keep it undocumented rather than impute |
| Extraction uncertainty | Date or dose requires source rechecking | Clinician confirmation precedes basic counts |
| De-identification check | Structured fields and free text may both identify someone | Agree authorization and applicable rules; automated removal is not completed review |
Delivery manifest
| File | Format | Contents |
|---|---|---|
| Field draft and source register | CSV | Fictional ID, value, source location, uncertainty and confirmation |
| Follow-up gaps and versions | PDF / CSV | Denominators, confirmed counts, missingness and corrections |
This is an illustrative formal delivery manifest. This pack supplies an HTML report, CSV tables and a source register. Agree formal formats, quantities and scope in the quote.
Acceptance criteria
- Accept agreed data checks, methods and module outputs. Code reproduces analysis in the agreed environment; missing or unsuitable data are recorded. Finding direction and publication are not acceptance criteria.
Sources
- HHS: Methods for de-identification of protected health information
https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification/index.html