CHDA® Exam 2026/2027 | AHIMA-Aligned Practice
Test
100 Certified Questions with Verified Answers &
Expert Explanations
Domain I: Data Management (Questions 1-25)
Q1: A hospital's HIM department is implementing a new EHR system and must map
legacy ICD-9-CM codes to ICD-10-CM. During the mapping process, the data governance
team discovers that 12% of legacy codes have no equivalent in ICD-10-CM (one-to-null
mapping). What is the most appropriate next step according to AHIMA's Data Quality
Management Model?
A. Automatically map these codes to the nearest clinical approximation in ICD-10-CM to
maintain data continuity.
B. Flag these records for manual clinical review and document the mapping decision in
the metadata repository. [CORRECT]
C. Exclude these records from the migration to prevent data quality degradation in the
new system.
D. Retain the ICD-9-CM codes in the new system and create a separate lookup table for
legacy reporting only.
Correct Answer: B
Rationale: Per AHIMA's Data Quality Management Model, specifically the "Analysis" and
"Improvement" phases, when automated mapping yields one-to-null results, the
,appropriate action is clinical validation with documented decision-making. Option A
violates data integrity principles by introducing approximation errors. Option C creates
unacceptable data loss and selection bias. Option D perpetuates non-standard coding,
complicating future analytics and regulatory reporting. The metadata documentation
ensures transparency and auditability, aligning with information governance principles
of accountability and integrity.
Q2: A health data analyst is designing a data dictionary for a multi-site research registry.
Which data element characteristic is MOST critical for ensuring semantic
interoperability across heterogeneous EHR systems?
A. Field length and data type constraints (e.g., VARCHAR(50))
B. Controlled vocabulary binding to standard terminologies (e.g., SNOMED CT, LOINC)
[CORRECT]
C. Encryption algorithms for data-at-rest protection
D. Primary key assignments for relational database normalization
Correct Answer: B
Rationale: Semantic interoperability requires that data elements convey consistent
meaning across systems. Per AHIMA's Information Governance Principles and HL7
FHIR standards, binding data elements to standard terminologies (SNOMED CT for
clinical concepts, LOINC for laboratory observations) ensures that "blood glucose"
means the same thing at Site A (Epic) and Site B (Cerner). Field length (A) addresses
syntactic interoperability; encryption (C) addresses security; primary keys (D) address
database integrity—none ensure shared clinical meaning.
,Q3: During a quarterly data quality audit, an analyst discovers that the "Admission Date"
field in the EDW contains 8% null values, while the "Discharge Date" field has 3% null
values. The hospital's KPI for data completeness is 95%. Which action aligns with the
DMAIC (Define, Measure, Analyze, Improve, Control) methodology for data quality
improvement?
A. Immediately implement a hard-stop validation rule in the EHR to prevent future null
admission dates
B. Calculate the impact on length-of-stay analytics and proceed to root cause analysis in
the "Analyze" phase [CORRECT]
C. Report the finding as compliant since the average completeness across both fields
exceeds 95%
D. Delete all records with null admission dates to achieve 100% completeness for
reporting purposes
Correct Answer: B
Rationale: The DMAIC methodology requires systematic progression through phases.
Having measured the defect rates (8% and 3%), the analyst must now analyze root
causes—whether nulls stem from system errors, workflow issues, or data extraction
problems—before implementing improvements. Option A skips analysis and may
disrupt clinical workflows. Option C incorrectly averages non-equivalent fields
(admission date is more critical for LOS calculation). Option D introduces selection bias
and violates data retention principles.
, Q4: A healthcare organization is integrating claims data from multiple payers with
clinical data from the EHR for population health analytics. Which data integration
challenge poses the GREATEST risk to analytic validity due to structural heterogeneity?
A. Differences in patient consent forms across data sources
B. Variations in date formats (MM/DD/YYYY vs. YYYY-MM-DD) between systems
C. Inconsistent patient identifier schemas preventing accurate record linkage
[CORRECT]
D. Varying levels of encryption strength applied to different datasets
Correct Answer: C
Rationale: Per AHIMA's data integration best practices, patient identity management is
foundational to analytic validity. Inconsistent identifier schemas (Member ID vs. Medical
Record Number vs. SSN fragments) create the risk of false positives/negatives in record
linkage, directly compromising cohort definitions, outcome attribution, and risk
stratification accuracy. While date formats (B) require transformation and consent (A)
affects legal use, identifier misalignment fundamentally corrupts the analytic dataset's
integrity. Encryption (D) affects security, not analytic structure.
Q5: An analyst is evaluating data quality dimensions for a surgical registry. The registry
shows 99.2% agreement between "Procedure Code" entries and the corresponding
operative report text in a random sample. Which data quality dimension is being
measured?
A. Completeness
B. Consistency
Test
100 Certified Questions with Verified Answers &
Expert Explanations
Domain I: Data Management (Questions 1-25)
Q1: A hospital's HIM department is implementing a new EHR system and must map
legacy ICD-9-CM codes to ICD-10-CM. During the mapping process, the data governance
team discovers that 12% of legacy codes have no equivalent in ICD-10-CM (one-to-null
mapping). What is the most appropriate next step according to AHIMA's Data Quality
Management Model?
A. Automatically map these codes to the nearest clinical approximation in ICD-10-CM to
maintain data continuity.
B. Flag these records for manual clinical review and document the mapping decision in
the metadata repository. [CORRECT]
C. Exclude these records from the migration to prevent data quality degradation in the
new system.
D. Retain the ICD-9-CM codes in the new system and create a separate lookup table for
legacy reporting only.
Correct Answer: B
Rationale: Per AHIMA's Data Quality Management Model, specifically the "Analysis" and
"Improvement" phases, when automated mapping yields one-to-null results, the
,appropriate action is clinical validation with documented decision-making. Option A
violates data integrity principles by introducing approximation errors. Option C creates
unacceptable data loss and selection bias. Option D perpetuates non-standard coding,
complicating future analytics and regulatory reporting. The metadata documentation
ensures transparency and auditability, aligning with information governance principles
of accountability and integrity.
Q2: A health data analyst is designing a data dictionary for a multi-site research registry.
Which data element characteristic is MOST critical for ensuring semantic
interoperability across heterogeneous EHR systems?
A. Field length and data type constraints (e.g., VARCHAR(50))
B. Controlled vocabulary binding to standard terminologies (e.g., SNOMED CT, LOINC)
[CORRECT]
C. Encryption algorithms for data-at-rest protection
D. Primary key assignments for relational database normalization
Correct Answer: B
Rationale: Semantic interoperability requires that data elements convey consistent
meaning across systems. Per AHIMA's Information Governance Principles and HL7
FHIR standards, binding data elements to standard terminologies (SNOMED CT for
clinical concepts, LOINC for laboratory observations) ensures that "blood glucose"
means the same thing at Site A (Epic) and Site B (Cerner). Field length (A) addresses
syntactic interoperability; encryption (C) addresses security; primary keys (D) address
database integrity—none ensure shared clinical meaning.
,Q3: During a quarterly data quality audit, an analyst discovers that the "Admission Date"
field in the EDW contains 8% null values, while the "Discharge Date" field has 3% null
values. The hospital's KPI for data completeness is 95%. Which action aligns with the
DMAIC (Define, Measure, Analyze, Improve, Control) methodology for data quality
improvement?
A. Immediately implement a hard-stop validation rule in the EHR to prevent future null
admission dates
B. Calculate the impact on length-of-stay analytics and proceed to root cause analysis in
the "Analyze" phase [CORRECT]
C. Report the finding as compliant since the average completeness across both fields
exceeds 95%
D. Delete all records with null admission dates to achieve 100% completeness for
reporting purposes
Correct Answer: B
Rationale: The DMAIC methodology requires systematic progression through phases.
Having measured the defect rates (8% and 3%), the analyst must now analyze root
causes—whether nulls stem from system errors, workflow issues, or data extraction
problems—before implementing improvements. Option A skips analysis and may
disrupt clinical workflows. Option C incorrectly averages non-equivalent fields
(admission date is more critical for LOS calculation). Option D introduces selection bias
and violates data retention principles.
, Q4: A healthcare organization is integrating claims data from multiple payers with
clinical data from the EHR for population health analytics. Which data integration
challenge poses the GREATEST risk to analytic validity due to structural heterogeneity?
A. Differences in patient consent forms across data sources
B. Variations in date formats (MM/DD/YYYY vs. YYYY-MM-DD) between systems
C. Inconsistent patient identifier schemas preventing accurate record linkage
[CORRECT]
D. Varying levels of encryption strength applied to different datasets
Correct Answer: C
Rationale: Per AHIMA's data integration best practices, patient identity management is
foundational to analytic validity. Inconsistent identifier schemas (Member ID vs. Medical
Record Number vs. SSN fragments) create the risk of false positives/negatives in record
linkage, directly compromising cohort definitions, outcome attribution, and risk
stratification accuracy. While date formats (B) require transformation and consent (A)
affects legal use, identifier misalignment fundamentally corrupts the analytic dataset's
integrity. Encryption (D) affects security, not analytic structure.
Q5: An analyst is evaluating data quality dimensions for a surgical registry. The registry
shows 99.2% agreement between "Procedure Code" entries and the corresponding
operative report text in a random sample. Which data quality dimension is being
measured?
A. Completeness
B. Consistency
