HRIP study plan

Define data governance, stewardship, and ownership, and distinguish the accountability of a data owner from the operational duties of a data steward in a healthcare organization.

Map a sample clinical data asset's lifecycle from point of capture through archival, naming the steward accountable at each stage.

Draft a one-page data governance charter covering decision rights, escalation paths, and committee structure for a hospital analytics program.

Differentiate data governance from data management and from IT operations, and give one concrete example of each boundary being crossed.

Explain the purpose and structure of core clinical terminology systems (SNOMED CT, LOINC, ICD-10-CM/PCS, RxNorm) and state which question each is designed to answer.

Contrast a reference terminology with a classification and give a clinical scenario where mapping between them creates information loss.

Describe the structure of an electronic health record data model, including encounter, order, result, and problem-list entities and their relationships.

Apply data-standard selection criteria to choose the appropriate terminology for a lab-result interoperability use case.

Explain the roles of HL7 v2, HL7 FHIR, CDA, and IHE profiles in health information exchange, and identify the message type appropriate for a given exchange scenario.

Trace a patient admission message from source system through interface engine to receiving system, naming each transformation and validation checkpoint.

Distinguish syntactic from semantic interoperability and propose a test that detects a semantic mismatch in a live interface.

Describe how a health information exchange, master patient index, and consent registry interact during a query.

Compare observational and experimental designs and select a defensible design for a stated comparative-effectiveness question.

Identify sources of bias (selection, information, confounding) in a described study and propose one mitigation for each.

Write a research question in PICO form and derive the corresponding primary outcome and analysis population.

Explain randomization, blinding, and intention-to-treat analysis and why each matters to internal validity.

Compute and interpret measures of association (risk ratio, odds ratio, hazard ratio) from a provided 2x2 table and state which design each suits.

Distinguish statistical significance from clinical significance and interpret a confidence interval in plain language.

Identify the appropriate hypothesis test for a described comparison of continuous or categorical data and justify the choice.

Explain Type I and Type II error, power, and sample size in the context of a study you designed in week 4.

Apply the HIPAA Privacy Rule minimum-necessary standard and identify when a de-identification approach (Safe Harbor versus expert determination) is required.

Distinguish the roles of covered entity, business associate, and subcontractor, and state the obligations triggered by each relationship.

Describe the HIPAA Security Rule's administrative, physical, and technical safeguards and give one control example per category.

Outline the breach-notification decision path, including risk assessment and timing requirements, for a lost unencrypted laptop.

Define the core data-quality dimensions (completeness, accuracy, timeliness, consistency, validity, uniqueness) and design one measurable check per dimension.

Construct a data-quality dashboard specification with thresholds, owners, and escalation rules for a clinical registry.

Describe a data-validation approach at point of entry versus in the warehouse and state the trade-offs.

Explain how data provenance and audit trails support integrity investigations and regulatory defensibility.

Select an appropriate chart type for a stated comparison and justify the choice against at least one misleading alternative.

Apply plain-language and accessibility principles to rewrite a technical finding for an executive audience.

Assemble a results narrative that reports effect size, uncertainty, and limitations without overstating causality.

Create a final review checklist covering all eight domains and identify your two weakest areas for post-exam study.