Building a Hospital Incident Reporting Ontology (HIRO) in the Web Ontology Language (OWL) using the JCAHO Patient Safety Event Taxonomy (PSET) presented by Liju Fan, PhD Senior Scientist

Core Competencies Health and Science Informatics Environmental Services Public Health Outreach The KEVRIC Company Founded in 1981 Headquarters in Silver Spring, Maryland Acquired by Information Management Consultants (IMC) in February 2003

Health and Science Informatics Capabilities Enterprise Vocabulary System Ontology Development Knowledge Management Patient Safety Clinical Trial Management Public Health Statistical Analysis

What is an Ontology? Tom Gruber - “An ontology is a specification of a conceptualization” Stanford Knowledge Systems Lab - “An ontology is an explicit specification of some topic. Ontologies therefore provide a vocabulary for representing and communicating knowledge about some topic and a set of relationships that hold among the terms in that vocabulary.” World Wide Web Consortium (W3C) - “Ontology is about the exact description of things and their relationships.”

What is an Ontology? Ontologies (from simple taxonomies to metadata schemes to logical theories) –defines the terms used to describe and represent an area of knowledge –is used by people, databases, and applications that need to share domain information –includes computer-usable definitions of basic concepts in the domain and the relationships among them –encodes knowledge in a domain and also knowledge that spans domains What is an Ontology? Ontologies (from simple taxonomies to metadata schemes to logical theories) defines the terms used to describe and represent an area of knowledge is used by people, databases, and applications that need to share domain information includes computer-usable definitions of basic concepts in the domain and the relationships among them encodes knowledge in a domain and also knowledge that spans domains

What is an Ontology? Ontologies figure prominently in the emerging Semantic Web –representing the semantics of documents –enabling the semantics to be used by web applications and intelligent agents. But the Semantic Web needs HIGHLY STURCTURED ontologies with –Classes (general things) in the many domains of interest –The relationships that can exist among things –The properties (or attributes) those things may have What is an Ontology? Ontologies figure prominently in the emerging Semantic Web –represent the semantics of documents –enable the semantics to be used by web applications and intelligent agents But the Semantic Web needs HIGHLY STRUCTURED ontologies with –Classes (general things) in the many domains of interest –The relationships that can exist among things –The properties (or attributes) those things may have

Uses of Ontologies Ontologies provide controlled vocabularies for a specific domain Separate ontologies mapped to one another to form a system-wide ontology Accessed through API, ontologies facilitate the integration of archived and external data to the warehouse

What is an Ontology? Ontologies figure prominently in the emerging Semantic Web –representing the semantics of documents –enabling the semantics to be used by web applications and intelligent agents. But the Semantic Web needs HIGHLY STURCTURED ontologies with –Classes (general things) in the many domains of interest –The relationships that can exist among things –The properties (or attributes) those things may have Identify Scope Review Existing Taxonomy/Ontology Knowledge Acquisition Encode ConceptualizeTest Iterative Development of Ontologies

Highlighted Project: Web Redesign Controlled Health Thesaurus for CDC-IRMO Highlighted Project: Hospital Incident Reporting Ontology for Joint Commission on Accreditation of Healthcare Organizations (JCAHO-HIRO) The KEVRIC team develops the Hospital Incident Reporting Ontology (HIRO) for adverse events and near misses Based on the Patient Safety Event Taxonomy (PSET), a common standardized terminology created by JCAHO for different event data collected from dissimilar reporting systems Incorporating a sample of de-identified hospital incident reports representative of systems currently in use To facilitate data-mining, knowledge sharing, and cooperative problem-solving respecting patient safety in hospitals Using Protégé-OWL (open-source ontology editing tool developed by Stanford U.)

Design Specifics of HIRO Approach: Integrative Portability to the web: OWL format Development environment: Protégé 3.0 release Hardware: Pentium Database: MySQL Consistency and inferencing: Classifiable by Racer Naming conventions: OWL-compatible and human friendly

Highlighted Project: Web Redesign Controlled Health Thesaurus for CDC-IRMO Evaluate and re-organize PSET in Protégé-OWL for ► Concept coverage ► Concept properties ► Concepts relationships Incorporate additional concepts and relationships from de- identified individual incident reports using ► Natural Language Processing (NLP) ► Manual editing and modeling by clinical staff who are subject matter experts in patient safety Development of HIRO

PSET Terms (xls) AHRQ Cases (html) Peer-Reviewed Literature (pdf) Johns Hopkins ICUSRS Database (mdb) HIRO Prototype Consistency Check Modeling Natural Language Processing Building Methodology Protégé-OWL Manual Curating Evaluation Re-organization

Five Top Nodes of Taxonomic Tree from PSET Cause of Occurrence - The factors and agents that bring about a health care error or systems failure Domain - Where a health care error or systems failure occurred and the type of individual involved Impact - The outcome or effect of a health care error or systems failure, commonly referred to as harm to the recipient of care Mitigation Activity - Those activities that an organization undertakes in attempting to lessen the severity and impact of a potential emergency Type of Occurrence - The perceptible, outward, or visible process that was in error or failed

PSET in Protégé True is_a relationship down through each hierarchy where every X at level n “is a” Y at level n-1 Example: Death is a Physical_Impact; Physical_Impact is a Medical_Impact; Medical_Impact is an Impact Non-hierarchical relationships linking concepts (classes) of two trees or within the same tree Examples: leads to, affects, prevents Properties provide additional information Examples: PSET_ID, Synonym, PSET_Definition Mapping to major medical vocabularies or classifications (DSM-IV_ID, ICD-9_Code)

Use Cases Uses of an ontology for patient safety information systems Controlled vocabulary for semantic interoperability Corporate web site management for patient safety Web portal for patient safety Design documentation, e.g., incident reports Data aggregation and retrieval Query patient safety databases via API

Acknowledgements KEVRIC / IMC Annette Smith, MS Douglas Boenning, MD Paul Koch, PhD Todd Grimmett Peg Silloway JCAHO Andrew Chang, JD Gerard Castro, MPH Stacey Champagne, MA Jerod M. Loeb, PhD AHRQ James Battles, PhD