| Domain | What candidate must know / tasks |
1. Foundational Knowledge of Analytics in Healthcare (≈ 14–16 % of exam) | - Understand healthcare delivery systems
- Know commonly used healthcare datasets, quality measures, classification & terminology systems (ICD, CPT, SNOMED, LOINC, RxNorm etc.)
- Understand revenue-cycle and healthcare billing fundamentals
- Be aware of regulatory/accreditation agencies and reporting requirements
- Basic understanding of epidemiology, health equity, and how EHR/informatics relate to data
- Know the levels of data analysis (descriptive, predictive, diagnostic, prescriptive)
- Basics of AI/machine-learning applications in healthcare data analytics (as per outline)
|
2. Business Needs Assessment (≈ 11–15 %) | - Understand project management principles related to analytics
- Identify stakeholders and their requirements
- Translate business questions (needs) into analyzable metrics
- Define objectives/goals of data requests/analytic tasks
- Develop an analysis plan: data parameters, sources, metrics definitions, documentation of specifications
- Evaluate external requirements or regulatory mandates where relevant
|
3. Data Acquisition (≈ 14–18 %) | - Identify data sources & trace data lineage
- Understand different data collection/extraction methods
- Extract data correctly
- Perform data quality assessment, cleansing, transformation, mapping
- Validate transformed data
- Ensure data integrity and readiness for analysis
|
4. Data Analysis (≈ 22–25 %) | - Query data effectively
- Choose and apply appropriate analytical / statistical methods
- Analyze data to identify trends, patterns, anomalies
- Understand benchmarking and risk-adjustment methods where applicable
- Use analytical tools / methods aligned with healthcare data context
|
5. Data Interpretation & Reporting (≈ 18–22 %) | - Interpret analysis results & identify key findings
- Recognize limitations, assumptions in analysis
- Create effective data visualizations (charts, graphs, dashboards)
- Communicate findings to stakeholders (clinicians, management, payers)
- Make recommendations based on data insights
|
6. Data Governance (≈ 8–10 %) | - Understand data governance principles: access, ownership, integrity, usage policies
- Know database designs, data stewardship, data dictionaries
- Be aware of health-data laws/regulations (privacy, security, compliance)
- Implement controls, audit logs, and controls over data submission and access
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