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Formats for Nursing Documentation

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Nursing documentation encompasses various formats designed to capture precise patient data, facilitate communication among healthcare team members, and ensure comprehensive and accurate patient records. Let's explore each of these formats in detail:
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Ogive Graph01:07

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
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Nursing Clinical Information System (NCIS)
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FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.

Guohui Xiao1, Emily Pfaff2, Eric Prud'hommeaux3

  • 1University of Bergen, Norway; University of Oslo, Norway; Ontopic S.r.l., Italy.

Journal of Biomedical Informatics
|September 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces FHIR-Ontop-OMOP, a system that converts Observational Medical Outcomes Partnership (OMOP) Common Data Model data into FHIR-compliant knowledge graphs. This enables explainable AI in healthcare by standardizing electronic health records for better data analysis.

Keywords:
Clinical Knowledge GraphsData StandardsFast Healthcare Interoperability Resources (FHIR)Semantic WebShape Expressions (ShEx)Virtual Knowledge Graphs

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Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Data Standardization

Background:

  • Knowledge graphs (KGs) are crucial for explainable AI in healthcare.
  • Standardizing electronic health records (EHRs) into clinical KGs (CKGs) from formats like Fast Healthcare Interoperability Resources (FHIR) and Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is a research priority.
  • The potential of OMOP CDM for building CKGs has been underexplored.

Purpose of the Study:

  • To develop and assess methods for transforming OMOP CDM data repositories into virtual CKGs.
  • Ensure these virtual CKGs comply with the FHIR Resource Description Framework (RDF) specification.

Main Methods:

  • Developed the FHIR-Ontop-OMOP system to generate virtual CKGs from OMOP relational databases.
  • Utilized the Medical Information Mart for Intensive Care (MIMIC-III) OMOP CDM data repository for evaluation.
  • Assessed data transformation faithfulness and CKG conformance to FHIR RDF standards.

Main Results:

  • The FHIR-Ontop-OMOP system successfully generated FHIR-compliant RDF graphs from OMOP data.
  • Data transformation faithfulness was confirmed by identical patient counts from SQL and SPARQL queries.
  • Generated CKGs for 100 patients were fully conformant with the FHIR RDF specification.

Conclusions:

  • The FHIR-Ontop-OMOP system effectively exposes OMOP databases as FHIR-compliant RDF graphs.
  • This demonstrates the value of interoperability between FHIR and OMOP CDM for healthcare AI.
  • The generated CKGs provide a semantic foundation for advancing explainable AI in healthcare.