FIELD GUIDE / STRONG
CSV cleaning for clinical staffing agencies
Spreadsheet imports add whitespace, strip digits, display scientific notation, and mix missing values with malformed values. This guide applies the work to candidate and placement rosters and candidate imports.
CHECK YOUR ROSTER
Upload your candidate and placement roster and clean NPI values before NPPES lookup.
The same validator used on the homepage: local checksum checks, live public NPPES lookup, cautious differences, and clean export.
What the check needs to separate
Candidate imports commonly contain repeats, name variants, and wrong entity types before screening begins. For csv cleaning, Spreadsheet imports add whitespace, strip digits, display scientific notation, and mix missing values with malformed values.
In csv cleaning for clinical staffing agencies, candidate imports changes how a result should be interpreted. NPI validation does not replace license, credential, exclusion, sanction, or employment screening.
| Roster input to retain | Public NPPES evidence to append |
|---|---|
| source_agency | practice location |
| candidate_id | entity type |
| source row ID | lookup status |
| source name | normalized NPI |
FICTIONAL OPERATIONAL EXAMPLE
CSV cleaning in a fictional candidate and placement roster
During a fictional csv cleaning review, a candidate and placement roster contains 8,500 imported clinician profiles. One row for Cameron Price, RN / Fictional Northstar Staffing reaches review because middle initial causes hard rejection.
For this csv cleaning review, the source retains source_agency, candidate_id, source_agency for traceability.
Keep raw and normalized values side by side; every cleaning rule should be reversible. The reviewer also checks candidate imports.
Common errors in this workflow
- 01duplicate row deleted before context is retained
- 02scientific notation saved by spreadsheet software
- 03middle initial causes hard rejection
- 04same clinician imported twice
A defensible workflow
- 01
Preserve every original row and raw identifier.
- 02
Trim display separators without inventing digits.
- 03
Classify blanks, malformed values, and duplicates separately. Retain legal_name as operational context.
- 04
Run the checksum before NPPES requests.
- 05
Append results without overwriting source columns.
REVIEW GUIDANCE
Use the result as evidence, not a verdict.
Keep raw and normalized values side by side; every cleaning rule should be reversible. Preserve candidate imports as a separate consideration for clinical staffing agencies.
Formatting repair does not prove the identifier belongs to the input provider. NPI validation does not replace license, credential, exclusion, sanction, or employment screening.
QUESTIONS
What reviewers usually need to know
What should clinical staffing agencies do first?
Preserve the source row, normalize locally, and keep candidate_npi before comparing public fields.
Should a difference be corrected automatically?
Usually not. Keep raw and normalized values side by side; every cleaning rule should be reversible.
What does an NPPES match establish?
It confirms public fields returned at lookup time. NPI validation does not replace license, credential, exclusion, sanction, or employment screening.
Primary references: CMS National Provider Identifiers and the NPI Registry API documentation. Public provider-reported data should be read with its source date and limitations.