SpeedsPath Glossary of Terms

Structured Data

Description

Structured Data refers to highly organized, predefined, and machine-readable information that follows a consistent format—such as fields, tables, codes, and standardized data types—used within pathology laboratory information systems (LIS/LIMS). It allows lab processes to be automated, searchable, interoperable, and analyzable, improving accuracy, reporting, and compliance.

Key Points

• Predefined Format: Stored in fixed fields such as patient ID, test code, specimen type, and results.
• Standardized Values: Uses controlled vocabularies (e.g., LOINC, SNOMED, ICD).
• Easily Searchable: Enables fast filtering, queries, and retrieval of patient, specimen, and test data.
• Interoperable: Supports clean data exchange with EMRs, billing systems, analyzers, and middleware.
• Automation-Friendly: Reduces manual work by supporting rule engines, autovalidation, and workflow triggers.
• Improved Analytics: Enables dashboards, performance metrics, QC tracking, and audit logs.
• Error Reduction: Minimizes human entry errors through structured fields and dropdown options.
• Regulatory Compliance: Helps meet CLIA, CAP, and NABL requirements for traceability and auditability.

SpeedsPath Makes Structured Data Accurate & Effortless

Structured Data is central to managing information in pathology labs. Accurate organization, standardization, and machine-readability make processes reliable. Automating this with an LIS adds speed, precision, and accountability.

See how SpeedsPath automates structured data handling, supports interoperability, and streamlines workflows.

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Related Terms

• Data Migration
• Interoperability
• LOINC
• SNOMED
• Unstructured Data

FAQs

1. What is structured data in a pathology LIS?
   Structured data is information stored in predefined, fixed formats—such as fields, dropdowns, and codes—making it consistent, searchable, and machine-readable.
2. Why is structured data important in lab management software?
   It improves accuracy, workflow efficiency, interoperability, analytics, and compliance.
3. What are examples of structured data in pathology labs?
   Test codes, patient demographics, specimen barcodes, instrument results, diagnosis codes, and QC values.
4. How does structured data support interoperability?
   Standardized formats (HL7, LOINC, SNOMED) allow clean data exchange between LIS, EMR, billing, RIS, analyzers, and other systems.
5. Does structured data reduce errors in lab workflows?
   Yes. Drop-down lists, predefined options, and validation rules reduce manual entry mistakes.
6. How does structured data help in generating pathology reports?
   It automatically populates fields, ensures consistency, and supports template-driven reporting.
7. Can structured data help with regulatory compliance?
   Absolutely. It maintains traceable audit trails and standardized formats required by CAP, NABL, CLIA, and HIPAA.
8. Is structured data necessary for automation?
   Yes. Automation engines (rules, autovalidation, routing) rely on structured values to trigger actions.
9. How does structured data improve analytics and dashboards?
   Because the data is consistent and coded, it allows performance metrics, TAT reports, QC trends, and utilization tracking.
10. How is structured data different from unstructured data in pathology?
    Structured data uses fixed fields and codes (e.g., "Hemoglobin: 12.5 g/dL"), whereas unstructured data includes free-text notes, gross descriptions, or narrative comments.

Want to learn more? Explore our LIS Glossary or check out our expert blogs on Anatomic Pathology, Molecular Diagnostics, Cytology, and Lab Information Systems.