- Registered Health Information Technician Data Integrity and Quality Flashcards
6 cards from real RHIT practice questions. Tap to flip, then mark Knew It or Still Learning โ missed cards come back until you master them.
Read the first 6 - Registered Health Information Technician Data Integrity and Quality flashcards as text
During a quantitative analysis of health records, which type of deficiency would be identified?
Answer: Whether all required reports, signatures, dates, and forms are present in the record
Quantitative analysis checks for the physical presence of required elements rather than evaluating clinical accuracy.
A physician copied and pasted a previous visit's note into the current encounter without updating it. What data quality risk does this create?
Answer: Inaccurate and potentially misleading clinical documentation that does not reflect the current encounter
Copy-and-paste without editing creates documentation that may be inaccurate and not reflective of the current encounter.
What is the purpose of a record reconciliation process after a patient's discharge?
Answer: To ensure all records generated during the stay are accounted for and properly filed
Record reconciliation ensures that all documents created during the stay are collected and assembled in the patient's health record.
Which validation technique checks that data values fall within an acceptable predefined range?
Answer: Range check
A range check validates that a data value falls within a predefined acceptable range.
What is the significance of data provenance in health information management?
Answer: It documents the origin, history, and chain of custody of data from creation through all transformations
Data provenance tracks the complete lineage of data from its point of origin through all modifications.
An organization is implementing a new EHR and needs to migrate data from the legacy system. Which step is most critical before migration?
Answer: Conducting thorough data cleansing and mapping to ensure legacy data meets the new system's standards
Data cleansing and mapping before migration ensures legacy data is cleaned, standardized, and properly mapped.