WGU D220 | DATA ANALYTICS & INTEROPERABILITY
WGU D220
Data Analytics and Interoperability Review
2026–2027 | Informatics Study Guide
Focused concept review • 35 original practice questions • detailed rationales
How to Use This Study Guide
• Review the concepts first, then answer the practice questions without notes.
• For each missed question, identify the data, standard, interoperability process, or analytic method
being tested.
• Suggested time: 45–60 minutes for the questions, followed by focused review.
• These questions are original educational practice items, not official WGU assessment questions.
Part I — Data Analytics Essentials
DIKW framework: Data are raw observations; information is organized data with context; knowledge is
interpreted meaning or patterns; wisdom is applying knowledge and judgment to choose an appropriate
action.
Descriptive analytics: Summarizes what happened, such as monthly fall counts, average length of stay,
or medication-error rates.
Diagnostic analytics: Investigates why an outcome occurred, often by examining patterns, process
differences, contributing factors, and relationships. An association alone does not prove causation.
Predictive analytics: Uses historical data and statistical or machine-learning models to estimate the
likelihood of future events, such as readmission risk. Predictions require validation and should not be
treated as certainty.
Prescriptive analytics: Uses evidence, constraints, and optimization or decision rules to suggest
possible actions. Recommendations still require clinical judgment and governance.
Data quality dimensions: Accuracy, completeness, consistency, timeliness, validity, uniqueness, and
relevance. Poor-quality input can create misleading dashboards and model outputs.
Data governance: Defines accountability, ownership/stewardship, access, definitions, quality rules,
retention, privacy, security, and permitted uses across the data lifecycle.
Data visualization: Select a chart that fits the question: line charts for trends, bar charts for category
comparisons, and carefully labeled tables for precise values. Include clear definitions, units, dates, and
denominators.
Denominator matters: A rate requires a defined numerator and denominator. Compare like with like,
account for exposure or patient volume where appropriate, and avoid interpreting raw counts as risk
without context.
INFORMATICS STUDY GUIDE • Original educational resource | Page 1
, WGU D220 | DATA ANALYTICS & INTEROPERABILITY
Correlation vs. causation: Two measures can move together without one causing the other. Consider
confounding, selection effects, temporal order, plausible mechanisms, and study design before making
causal claims.
Benchmarking: Compares performance with a reference such as a prior period, peer group, or
recognized standard. Ensure definitions, populations, and measurement methods are comparable.
Process improvement: PDSA means Plan, Do, Study, Act: test a change on a small scale, study results,
and refine. Lean focuses on reducing waste; Six Sigma focuses on reducing process variation and defects.
Privacy in analytics: Use authorized data, limit access and disclosure to the applicable purpose, follow
approved de-identification and governance processes, and do not export PHI to unapproved tools.
AI and predictive models: Check data provenance, population fit, calibration, performance across
groups, explainability where relevant, privacy, drift, workflow impact, and human oversight.
Data lifecycle: Collection, validation, storage, access/use, exchange, retention, archival, and secure
disposal should be governed throughout the lifecycle.
Part II — Interoperability Essentials
Interoperability: The ability of systems to exchange information and use the information exchanged
meaningfully. It depends on technical connectivity, shared structure, and consistent interpretation.
Technical interoperability: The ability to connect and transport data between systems using compatible
interfaces, protocols, and exchange mechanisms.
Syntactic interoperability: Systems use a common structure or format so the receiving system can parse
the message. Structure alone does not guarantee the clinical meaning is preserved.
Semantic interoperability: Systems preserve the meaning of clinical concepts across exchange, often
through standardized terminologies, code systems, and value sets.
HL7 v2: A widely used messaging standard for exchanging operational and clinical data such as
admissions, orders, and results. Implementations may differ and require agreed profiles/mappings.
FHIR: Fast Healthcare Interoperability Resources is an HL7 standard that represents healthcare
information as resources and supports exchange through modern APIs and other mechanisms.
CDA/C-CDA: Clinical Document Architecture and Consolidated CDA support structured clinical
documents such as summaries and continuity-of-care documents.
DICOM: A standard for handling, storing, transmitting, and managing medical imaging information and
related data.
LOINC: A terminology commonly used to identify laboratory tests, clinical measurements, and
observation concepts.
SNOMED CT: A comprehensive clinical terminology used to represent clinical findings, disorders,
procedures, and other healthcare concepts.
ICD-10-CM: A classification system commonly used in the United States to code diagnoses for reporting
and administrative purposes. It is not a substitute for a rich clinical terminology.
RxNorm: A standardized nomenclature for clinical drugs that supports consistent identification of
medications.
INFORMATICS STUDY GUIDE • Original educational resource | Page 2
, WGU D220 | DATA ANALYTICS & INTEROPERABILITY
NANDA-I, NIC, NOC: NANDA-I classifies nursing diagnoses; NIC classifies nursing interventions; NOC
classifies nursing-sensitive outcomes.
HIE: Health information exchange supports appropriate electronic sharing of health information among
organizations or care settings. Exchange models may include directed, query-based, or consumer-
mediated approaches.
Master patient index (MPI): Helps match and link records to the correct person across systems.
Matching errors can create duplicate records or wrong-patient risks.
APIs and mapping: APIs allow software components to communicate. Mapping translates local codes or
data fields into shared standards; mappings should be validated and maintained.
Information blocking and governance: Data sharing should follow applicable law, permitted exceptions,
consent/authorization requirements, security controls, and organizational policy. Do not assume every
restriction is justified or every disclosure is automatically allowed.
Interoperability safety risks: Incorrect patient matching, missing context, unit mismatches, delayed
updates, terminology mapping errors, and duplicate data can cause harm. Verify source, time, units, and
clinical relevance.
Part III — Quick Comparison Table
Concept Main question Example
Descriptive analytics What happened? Falls per month
Diagnostic analytics Why might it have happened? Reviewing contributing workflow
factors
Predictive analytics What may happen? Estimating readmission risk
Prescriptive analytics What action may help? Comparing interventions under
constraints
Technical interoperability Can systems Interface or API transmits a
connect/exchange? message
Syntactic interoperability Can the receiver parse the Agreed message format
structure?
Semantic interoperability Does the receiver interpret the Shared clinical terminology and
same meaning? code
HL7 v2 How are operational messages Admission or lab-result
exchanged? messages
FHIR How can health data be API retrieving an Observation
represented/exchanged as resource
resources?
DICOM How are imaging data Radiology image and metadata
handled/exchanged?
LOINC Which test/observation is A laboratory test identifier
represented?
SNOMED CT Which clinical concept is Clinical finding or disorder
represented?
INFORMATICS STUDY GUIDE • Original educational resource | Page 3
WGU D220
Data Analytics and Interoperability Review
2026–2027 | Informatics Study Guide
Focused concept review • 35 original practice questions • detailed rationales
How to Use This Study Guide
• Review the concepts first, then answer the practice questions without notes.
• For each missed question, identify the data, standard, interoperability process, or analytic method
being tested.
• Suggested time: 45–60 minutes for the questions, followed by focused review.
• These questions are original educational practice items, not official WGU assessment questions.
Part I — Data Analytics Essentials
DIKW framework: Data are raw observations; information is organized data with context; knowledge is
interpreted meaning or patterns; wisdom is applying knowledge and judgment to choose an appropriate
action.
Descriptive analytics: Summarizes what happened, such as monthly fall counts, average length of stay,
or medication-error rates.
Diagnostic analytics: Investigates why an outcome occurred, often by examining patterns, process
differences, contributing factors, and relationships. An association alone does not prove causation.
Predictive analytics: Uses historical data and statistical or machine-learning models to estimate the
likelihood of future events, such as readmission risk. Predictions require validation and should not be
treated as certainty.
Prescriptive analytics: Uses evidence, constraints, and optimization or decision rules to suggest
possible actions. Recommendations still require clinical judgment and governance.
Data quality dimensions: Accuracy, completeness, consistency, timeliness, validity, uniqueness, and
relevance. Poor-quality input can create misleading dashboards and model outputs.
Data governance: Defines accountability, ownership/stewardship, access, definitions, quality rules,
retention, privacy, security, and permitted uses across the data lifecycle.
Data visualization: Select a chart that fits the question: line charts for trends, bar charts for category
comparisons, and carefully labeled tables for precise values. Include clear definitions, units, dates, and
denominators.
Denominator matters: A rate requires a defined numerator and denominator. Compare like with like,
account for exposure or patient volume where appropriate, and avoid interpreting raw counts as risk
without context.
INFORMATICS STUDY GUIDE • Original educational resource | Page 1
, WGU D220 | DATA ANALYTICS & INTEROPERABILITY
Correlation vs. causation: Two measures can move together without one causing the other. Consider
confounding, selection effects, temporal order, plausible mechanisms, and study design before making
causal claims.
Benchmarking: Compares performance with a reference such as a prior period, peer group, or
recognized standard. Ensure definitions, populations, and measurement methods are comparable.
Process improvement: PDSA means Plan, Do, Study, Act: test a change on a small scale, study results,
and refine. Lean focuses on reducing waste; Six Sigma focuses on reducing process variation and defects.
Privacy in analytics: Use authorized data, limit access and disclosure to the applicable purpose, follow
approved de-identification and governance processes, and do not export PHI to unapproved tools.
AI and predictive models: Check data provenance, population fit, calibration, performance across
groups, explainability where relevant, privacy, drift, workflow impact, and human oversight.
Data lifecycle: Collection, validation, storage, access/use, exchange, retention, archival, and secure
disposal should be governed throughout the lifecycle.
Part II — Interoperability Essentials
Interoperability: The ability of systems to exchange information and use the information exchanged
meaningfully. It depends on technical connectivity, shared structure, and consistent interpretation.
Technical interoperability: The ability to connect and transport data between systems using compatible
interfaces, protocols, and exchange mechanisms.
Syntactic interoperability: Systems use a common structure or format so the receiving system can parse
the message. Structure alone does not guarantee the clinical meaning is preserved.
Semantic interoperability: Systems preserve the meaning of clinical concepts across exchange, often
through standardized terminologies, code systems, and value sets.
HL7 v2: A widely used messaging standard for exchanging operational and clinical data such as
admissions, orders, and results. Implementations may differ and require agreed profiles/mappings.
FHIR: Fast Healthcare Interoperability Resources is an HL7 standard that represents healthcare
information as resources and supports exchange through modern APIs and other mechanisms.
CDA/C-CDA: Clinical Document Architecture and Consolidated CDA support structured clinical
documents such as summaries and continuity-of-care documents.
DICOM: A standard for handling, storing, transmitting, and managing medical imaging information and
related data.
LOINC: A terminology commonly used to identify laboratory tests, clinical measurements, and
observation concepts.
SNOMED CT: A comprehensive clinical terminology used to represent clinical findings, disorders,
procedures, and other healthcare concepts.
ICD-10-CM: A classification system commonly used in the United States to code diagnoses for reporting
and administrative purposes. It is not a substitute for a rich clinical terminology.
RxNorm: A standardized nomenclature for clinical drugs that supports consistent identification of
medications.
INFORMATICS STUDY GUIDE • Original educational resource | Page 2
, WGU D220 | DATA ANALYTICS & INTEROPERABILITY
NANDA-I, NIC, NOC: NANDA-I classifies nursing diagnoses; NIC classifies nursing interventions; NOC
classifies nursing-sensitive outcomes.
HIE: Health information exchange supports appropriate electronic sharing of health information among
organizations or care settings. Exchange models may include directed, query-based, or consumer-
mediated approaches.
Master patient index (MPI): Helps match and link records to the correct person across systems.
Matching errors can create duplicate records or wrong-patient risks.
APIs and mapping: APIs allow software components to communicate. Mapping translates local codes or
data fields into shared standards; mappings should be validated and maintained.
Information blocking and governance: Data sharing should follow applicable law, permitted exceptions,
consent/authorization requirements, security controls, and organizational policy. Do not assume every
restriction is justified or every disclosure is automatically allowed.
Interoperability safety risks: Incorrect patient matching, missing context, unit mismatches, delayed
updates, terminology mapping errors, and duplicate data can cause harm. Verify source, time, units, and
clinical relevance.
Part III — Quick Comparison Table
Concept Main question Example
Descriptive analytics What happened? Falls per month
Diagnostic analytics Why might it have happened? Reviewing contributing workflow
factors
Predictive analytics What may happen? Estimating readmission risk
Prescriptive analytics What action may help? Comparing interventions under
constraints
Technical interoperability Can systems Interface or API transmits a
connect/exchange? message
Syntactic interoperability Can the receiver parse the Agreed message format
structure?
Semantic interoperability Does the receiver interpret the Shared clinical terminology and
same meaning? code
HL7 v2 How are operational messages Admission or lab-result
exchanged? messages
FHIR How can health data be API retrieving an Observation
represented/exchanged as resource
resources?
DICOM How are imaging data Radiology image and metadata
handled/exchanged?
LOINC Which test/observation is A laboratory test identifier
represented?
SNOMED CT Which clinical concept is Clinical finding or disorder
represented?
INFORMATICS STUDY GUIDE • Original educational resource | Page 3
