Repeatability
High
The structure is consistent across all 12 files with identical column schemas, and the transformation logic — deduplication, code mapping, merging — is the same every time. This is a textbook repeatable ETL pipeline.
Ambiguity Tolerance
Medium
The output format (encounter-level unified CSV) is clear, but the procedure code standardization introduces ambiguity: mapping 47 naming conventions to a master taxonomy requires either a provided crosswalk or confident fuzzy matching, and edge cases will exist. Patient deduplication across offices also needs a defined matching rule (name + DOB? ID only?).
Data & Tool Availability
High
All inputs are flat CSV files with known columns — no API access, live systems, or special permissions required. A Python-based data agent can ingest, process, and output everything locally with standard libraries.
Error Cost
Medium
Billing analysis downstream means errors in deduplication or code mapping could distort financial conclusions, but the source files are untouched and the output is reviewable before use. Mistakes are correctable, not catastrophic.
Human Judgment Required
Low
The task is almost entirely mechanical: merge, deduplicate, remap, and export. The only judgment call is resolving ambiguous procedure code matches, which can be flagged for human review rather than silently decided.