Experiences from a Large- scale Implementation of SNOMED CT in Argentina.
- Dr. Alejandro Lopez Osornio
- Dr. Guillermo Reynoso
termMed IT
Buenos Aires, Argentina
Experiences from a Large- scale Implementation of SNOMED CT in - - PowerPoint PPT Presentation
Experiences from a Large- scale Implementation of SNOMED CT in Argentina. Dr. Alejandro Lopez Osornio Dr. Guillermo Reynoso termMed IT Buenos Aires, Argentina Hospital Italiano de Buenos Aires Located in Buenos Aires, Argentina Founded on
Experiences from a Large- scale Implementation of SNOMED CT in Argentina.
termMed IT
Buenos Aires, Argentina
Hospital Italiano de Buenos Aires
Located in Buenos Aires, Argentina Founded on 1853 500 bed hospital 10,000 visits a day in the ambulatory system 140,000 patients private health insurance system
Argentina, December 2001
Protests on the streets demanding the resignation of the president President De la Rua resigned Argentine Peso devalues from 1 U$S to 0.30 U$S Restrictions to cash withdrawal Credit crisis
Hospital Italiano, December 2001
“Management based on accurate clinical information will let us survive this crisis” (Dr. FGB Quiros) Great boost to the IT program Extension of the Hospital Information System to all areas:
Ambulatory visits Inpatient episodes Emergency Department Diagnostic procedures Pharmacy and Medical Devices Home care
Terminology use cases
Health insurance services management board
Chronic disease management program Epidemiology department Clinical trials Primary care Physicians Incentives Program Research and teaching (University Hospital) Clinical decision support systems
Available vocabularies on 2001
Local Pharmacy terminology Local Devices terminology Local Procedures terminology Argentine Reimbursement vocabulary Diagnosis:
Free text secondary coded with:
ICPC and ICD-10 (outpatient) ICD-9CM and DRG (inpatient)
Requirements
A new terminology System:
Unobtrusive: clinical users do not like things that step in the middle of their usual workflow Immediate: instant codification is the essential to use any kind of CDSS Precise: allow the user to record exactly what he wants, appropriate detail level
Design Principles
HIBA will use a local list of terms as user interface, constructed from the free- text entries repository The interface vocabulary will be concept
language” expressiveness (lot of qualifiers) This Interface Vocabulary will be mapped to a Reference Terminology and the to different classifications
Extension Mechanism
Terminologies levels and interactions
Aggregate Terminology Reference Terminology Interface Terminology Natural Language CIE-9-CM CIE-10 CIAP DRG ATC CIE-O SNOMED CT Local Vocabulary SCT Core CrossMaps Mechanism
SNOMED CT content
Concepts: great coverage, but detail is limited to avoid the ‘combinatorial explosion’. There is some regional bias. Descriptions: acceptable, valid and appropriate descriptions, but do not fully covers local jargon and acronyms (Spanish edition)
SNOMED CT is extendable
– Standard SNOMED CT
– Standard SCT + New descriptions
– Standard SCT + New descriptions + New concepts
Plan
Extend SNOMED CT to cover our needs Hide the complexity of the system implementing simple user interfaces:
Templates, pick-lists, checkboxes Search and refine
Terminology Team
1 Clinical terminologist (MD) (80% time) 1 Full time administrative supervisor 12 part-time medical students 1 Lead Software Analyst and Developer (75% time) and a second for 20% of his time Medical Informatics Department Advisory Board
SNOMED CT Mechanisms
Extensions Cross-Maps Subsets
Extensions
Namespace:
SNOMED CT and Local Extension
SNOMED CT Concepts Relationships Descriptions
SNOMED CT and Local Extension
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions
SNOMED CT and Local Extension
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions
New concepts definitions New Descriptions
SNOMED CT and Local Extension
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions
Left knee
Left knee osteoarthritis Osteoarthritis of left knee Osteoarthritis <- Is a Left knee <- Finding site
SNOMED CT and Local Extension
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions
Reference Terminology
SNOMED CT and Local Extension
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions
Interface Terminology
SNOMED CT and Local Extension
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions
Interface Terminology
'equivalent to'
SNOMED CT and Local Extension
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions
Interface Terminology Asthma <- Equivalent to Asthma Asthma Asthmatic
Dealing with changes on SNOMED CT, new releases every 6 months
SNOMED CT HI Extension New Concepts Relationships Descriptions Concepts Relationships Descriptions Unchanged Concepts Retired Concepts
X
Update time: One person, half day work, during one week
Local Interface Vocabulary for problems and procedures
2 million free text entries 110,000 selected descriptions
Normalization Frequency count > 10
25,000 concepts 9,000 equivalencies 16,000 new definitions
Subsets
Simple Subsets:
Diagnosis Signs & Symptoms Surgical Procedures Diagnostic Procedures Drugs & substances Medical Devices
HI Terminology
ID: 328423100001131 ID: 22223100001137 ID: 45565100001137 ID: 4433100001131 ID: 9843100001131 ID: 6743100001131
OB-Gyn Subset
ID: 76656100001137
Subsets
Language Subsets: Navigation Subsets:
Concept A: Description X Description Y Concept A: Description X (Preferred) Description Y Concept A: Description X Description Y (Preferred)
Medical department 1 Medical department 2 Context:
SNOMED CT Cross-maps
SNOMED CT HI Extension Concepts Relationships Descriptions Concepts Relationships Descriptions ICD-9CM ICD-10 ICPC
Tools
Apelon TDE - DTS Protege In house development
Terminology tools in Hospital Italiano
Terminology Administra1on tool
(Java 2EE Web based Applica-on + CLUE Browser)
Terminology Server
(Web Services)
SNOMED CT Release Domain Experts Clinical Informa-on Retrieval Clinical So3ware Applica-ons
Requirements for the Terminology Administration tool
Terminology Administra1on tool
(Java 2EE Web based Applica-on + CLUE Browser)
Add, delete concepts and descrip-ons SNOMED CT modeling Subset Administra-on Cross‐maps Administra-on Workflow Administra-on Quality Assurance SNOMED CT Release Terminology Server Domain Experts SNOMED CT periodic update
Requirements for the Terminology Server
Terminology Server
(Web Services)
Search and Refine Subset retrieval Users Sugges-ons Structured Post‐coordina-on Specific Knowledge Inference Cross map conversion Interface Vocabulary Clinical Informa-on Retrieval Clinical So3ware Applica-on
Data retrieval
Members of the terminology team analyze “Data retrieval support requests” and define appropriate subsets using an interactive Subset Definition tool: List of membership clauses: Include / Exclude descendants of concept... Include / Exclude concepts with role xx Include / Exclude concepts with role/target xx/yy The “Epidemiology department” uses the subsets on simple SQL sentences More than Data retrieval 1,200 subsets had been created
Implementation example:
Implementation example:
Results
Concept Oriented Thesaurus compilation 2002-2004 Software development 2003-2006 Full implementation in mid-2006 Free submission of new terms on problems list interface Submission rate has stabilized for the problems list but not for procedures Very good acceptance by the doctors = No complaints
Other standards
HL7 for messaging between different applications Map with ICD-9CM and DRG grouper DSM-IV Map with local specific reimbursement terminologies Participating on a pilot implementation of the draft version of the HL7 decision support services specification (Kawamoto - Lobach, Duke Univ.)
Information Model
No standardization of the information
model yet
Critical success factors
Full commitment from the to organization levels Respect for local vocabulary Successfully hiding the terminology complexity from the users
Which were the biggest hurdles to be
Initial processing of back history of free text Training modelers for the extension Designing a QA process for the adherence to Concept Model guidelines Train clinical users to avoid ‘non-clinical’ statements Manage variability on Procedures domain to maintain expressiveness
Lessons learned
There’s no such thing as too much effort to make things easier to you clinical users