Data Management, Standards, and Interoperability
Data management, standards, and interoperability tests governance, data quality, terminology, and exchange standards. Expect questions that ask you to select the right standard for a use case, distinguish messaging from documents and APIs, and explain why semantic interoperability is harder than transport.
Governance and quality
- Data governance defines ownership, stewardship, policies, metadata, master data, and the data lifecycle.
- Common quality dimensions are completeness, accuracy, consistency, timeliness, validity, and uniqueness. Measure them against explicit rules.
Standards
- HL7 v2 is a messaging standard; CDA is a document standard; FHIR uses RESTful APIs and resources.
- DICOM covers imaging. LOINC covers laboratory and other observations. SNOMED CT covers clinical findings and concepts. ICD-10-CM/PCS supports morbidity and billing. CPT covers procedures. RxNorm normalizes drugs; NDC identifies packages. UMLS is a metathesaurus.
- Interoperability levels are foundational, structural, semantic, and organizational. USCDI defines a baseline data set; HIEs and TEFCA support exchange.
Worked example
A local test called 'Hgb A1c' must map to LOINC. Reasoning: LOINC distinguishes specimen and method, so one local label can map to different LOINC codes depending on method. The team validates with the laboratory, versions the map, and tests reports. Sending a code is structural interoperability; agreeing on its meaning is semantic interoperability.
Traps
Using HL7 v2 and FHIR interchangeably; assuming one-to-one terminology maps; ignoring license terms; calling an interface 'semantic' when only transport works.