Anne Casey RN MSc FRCN
Editor, Paediatric Nursing
Royal College of Nursing Adviser on Information Standards
Clinical Domain Lead, NHS Information Standards Board for Health and Social Care
(15/10/08, SNOMED Workshop)
A brief introduction to SNOMED CT - the ontology based medical terminology. This covers the basic definitions, the difference between SNOMED CT and ICD9, Post co-ordination use-cases and some general information.
This is not an extensive guide for SNOMED CT adoption in a system
This presentation deals with the basics of SNOMED CT with respect to it being a code for computer systems to interpret medical knowledge and initiate action. This is explained specifically with the medical professionals in mind.
It begins by discussing what SNOMED CT actually is and then moving on to demonstrate how the code system can be used to merge clinical documents written in different languages into one as well as how it can help in automating repetitive tasks using an if-then-else rules engine.
Provides an overview of SNOMED CT concentrating on its fundamentals, advantages and disadvantages of use, how its logical model is designed, the relationships and attribute name-value pairing, and pre- & post-coordinated expressions
A brief introduction to SNOMED CT - the ontology based medical terminology. This covers the basic definitions, the difference between SNOMED CT and ICD9, Post co-ordination use-cases and some general information.
This is not an extensive guide for SNOMED CT adoption in a system
This presentation deals with the basics of SNOMED CT with respect to it being a code for computer systems to interpret medical knowledge and initiate action. This is explained specifically with the medical professionals in mind.
It begins by discussing what SNOMED CT actually is and then moving on to demonstrate how the code system can be used to merge clinical documents written in different languages into one as well as how it can help in automating repetitive tasks using an if-then-else rules engine.
Provides an overview of SNOMED CT concentrating on its fundamentals, advantages and disadvantages of use, how its logical model is designed, the relationships and attribute name-value pairing, and pre- & post-coordinated expressions
ICD-10 is an unknown terrain that the country is going toward. No one knows what to expect. Some expect productivity to decrease by as much as 50% due to its implementation. Some predict this new system will result in a shortage of coders. Is any of this true? This presentation will investigate the impacts – both foreseen and unforeseen – that ICD-10 implementation will have on radiology billing companies and radiology groups.
ICD-10-CM is the United States’ clinical modification of the World Health Organization’s (WHO) International Classification of Diseases (ICD) Tenth Revision. It is used to classify diseases and causes of illness recorded on health records, claims, and other vital information.
The U.S. Department of Health and Human Services (HHS) will require covered entities (i.e., health plans, health care providers, and health care clearinghouses) that conduct electronic HIPAA standard transactions to move from ICD-9 to the next generation ICD-10 code sets by October 1, 2015.
How can ICD-11 possibly help you enhancing your casemix ?
What Can ICD11 offer systematically?
A systematic meaningful integrated system of clinical conditions - (not only for DRGs … )
Better clinical description
Better severity grading
Better coding of co-morbidity
Inherent functional information (key ICF classes)
Integrated information system between ICD, ICF, ICHI
Deconstructing Diagnosis into subgroups
Computerized information processing
http://cpc.certifiedcodertraining.com/index.php/what-is-medical-coding | Curious about the field of Medical Coding? Certified Coder presents a brief overview of Medical Coding and why it is important.
The presentation speaks about the history and the purpose of why Medical Coding was brought into practice. It also speaks about how it came into picture and what are the recent advancements in this field.
What is Health Informatics?
HI Goals
HI stakeholders
HI subfields / subspecialties
Healthcare trends & HI
HI professional environments
HI education / training opportunities & degrees
HI organizations / journals / meetings / events
HI professional certificates
HI books
Public Health informatics, Consumer health informatics, mHealth & PHRs (Novem...Nawanan Theera-Ampornpunt
Presented at the M.S. and Ph.D. Programs in Data Science for Health Care, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 11, 2019
Clinical Documentation Guidelines for ICD-10-CMPamela Marasco
How Do You Rate Yourself as an Adopter of Change? Assess your willingness to implement new clinical documentation standards for ICD-10-CM. Improve your practice for clinical documentation to ensure proper selection of ICD-10-CM Coding Guidelines. Because EVERYTHING IS CHANGING!
The demand is growing for Medical Scribes. If you are looking for a new career and love the mix of medical language and technology, and want a challenging career with a bright future, this is for you!
An electronic health record is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings.
ICD-10 is an unknown terrain that the country is going toward. No one knows what to expect. Some expect productivity to decrease by as much as 50% due to its implementation. Some predict this new system will result in a shortage of coders. Is any of this true? This presentation will investigate the impacts – both foreseen and unforeseen – that ICD-10 implementation will have on radiology billing companies and radiology groups.
ICD-10-CM is the United States’ clinical modification of the World Health Organization’s (WHO) International Classification of Diseases (ICD) Tenth Revision. It is used to classify diseases and causes of illness recorded on health records, claims, and other vital information.
The U.S. Department of Health and Human Services (HHS) will require covered entities (i.e., health plans, health care providers, and health care clearinghouses) that conduct electronic HIPAA standard transactions to move from ICD-9 to the next generation ICD-10 code sets by October 1, 2015.
How can ICD-11 possibly help you enhancing your casemix ?
What Can ICD11 offer systematically?
A systematic meaningful integrated system of clinical conditions - (not only for DRGs … )
Better clinical description
Better severity grading
Better coding of co-morbidity
Inherent functional information (key ICF classes)
Integrated information system between ICD, ICF, ICHI
Deconstructing Diagnosis into subgroups
Computerized information processing
http://cpc.certifiedcodertraining.com/index.php/what-is-medical-coding | Curious about the field of Medical Coding? Certified Coder presents a brief overview of Medical Coding and why it is important.
The presentation speaks about the history and the purpose of why Medical Coding was brought into practice. It also speaks about how it came into picture and what are the recent advancements in this field.
What is Health Informatics?
HI Goals
HI stakeholders
HI subfields / subspecialties
Healthcare trends & HI
HI professional environments
HI education / training opportunities & degrees
HI organizations / journals / meetings / events
HI professional certificates
HI books
Public Health informatics, Consumer health informatics, mHealth & PHRs (Novem...Nawanan Theera-Ampornpunt
Presented at the M.S. and Ph.D. Programs in Data Science for Health Care, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 11, 2019
Clinical Documentation Guidelines for ICD-10-CMPamela Marasco
How Do You Rate Yourself as an Adopter of Change? Assess your willingness to implement new clinical documentation standards for ICD-10-CM. Improve your practice for clinical documentation to ensure proper selection of ICD-10-CM Coding Guidelines. Because EVERYTHING IS CHANGING!
The demand is growing for Medical Scribes. If you are looking for a new career and love the mix of medical language and technology, and want a challenging career with a bright future, this is for you!
An electronic health record is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings.
SNOMED CT and other healthcare terminology standards: competition or cooperat...THL
SNOMED CT and other healthcare terminology standards: competition or cooperation? SNOMED CT in relation to LOINC, ICD, ICPC and other terminologies.
Robert Hausam, Hausam Consulting LLC
SNOMED CT 2019 -seminaari (29.3.2019
OpenMRS Concept Management Tutorial presented on 9 Dec 2015 at the OpenMRS Worldwide Summit in Singapore. Presented by Andy Kanter and Ellen Ball. 4 hour presentation.
Dynamic Context Adaptation for Diagnosing the Heart Disease in Healthcare Env...ijsc
In a smart medical environment, the ability to adapt context is critical. Context adaptation (CA) requires applications to delegate adaptation control to an entity that can receive state information and trigger adaptation in multiple applications. Thus, the idea of CA is to extract general knowledge from each contextualized system or medical dataset and to use it, in adapted form, when facing a similar problem in a different environment. In this paper, we describe a model for providing smart intelligent diagnosing services to the healthcare Environment and present work on our own architecture that has been designed to meet the key requirements of healthcare in a context-aware adaptive applications. The proposed system is capable of providing context intelligence, Decision Making, adaptability and extensible application framework for dynamic remote healthcare environments. The objective of the research is to revolutionize daily human life by making people’s surroundings flexible and adaptive.
DYNAMIC CONTEXT ADAPTATION FOR DIAGNOSING THE HEART DISEASE IN HEALTHCARE ENV...ijsc
In a smart medical environment, the ability to adapt context is critical. Context adaptation (CA) requires
applications to delegate adaptation control to an entity that can receive state information and trigger
adaptation in multiple applications. Thus, the idea of CA is to extract general knowledge from each
contextualized system or medical dataset and to use it, in adapted form, when facing a similar problem in a
different environment. In this paper, we describe a model for providing smart intelligent diagnosing
services to the healthcare Environment and present work on our own architecture that has been
designed to meet the key requirements of healthcare in a context-aware adaptive applications. The
proposed system is capable of providing context intelligence, Decision Making, adaptability and extensible
application framework for dynamic remote healthcare environments. The objective of the research is to
revolutionize daily human life by making people’s surroundings flexible and adaptive.
SNOMED CT in the clinical data standards landscape THL
SNOMED CT in the clinical data standards landscape: Where does it fit and how should we use it?
Robert Hausam, Hausam Consulting LLC
SNOMED CT 2019 -seminaari (28.3.2019)
Reference Domain Ontologies and Large Medical Language Models.pptxChimezie Ogbuji
Large Language Models (LLMs) have exploded into the modern research and development consciousness and triggered an artificial intelligence revolution. They are well-positioned to have a major impact on Medical Informatics. However, much of the data used to train these revolutionary models are general-purpose and, in some cases, synthetically generated from LLMs. Ontologies are a shared and agreed-upon conceptualization of a domain and facilitate computational reasoning. They have become important tools in biomedicine, supporting critical aspects of healthcare and biomedical research, and are integral to science. In this talk, we will delve into ontologies, their representational and reasoning power, and how terminology systems such as SNOMED-CT, an international master terminology providing comprehensive coverage of the entire domain of medicine, can be used with Controlled Natural Languages (CNL) to advance how LLMs are used and trained.
Health research, clinical registries, electronic health records – how do they...Koray Atalag
This is a talk I gave at my own organisation - National Institute for Health Innovation (NIHI) of the University of Auckland on 6 Aug 2014. Abstract as follows:
In this talk I’ll first cover the topic of clinical registry – an invaluable tool for supporting clinical practice but also gaining momentum in research and quality improvement. NIHI has been very active in this space: we have delivered the prestigious and highly successful National Cardiac Registry (ANZACS-QI) together with VIEW research team and also very recently launched the Gestational Diabetes Registry with Counties Manukau DHB & Diabetes Projects Trust. A few others are in likely to come down the line. This is a huge opportunity for health data driven research and NIHI to position itself as ‘the health data steward’ in the country given our independent status and existing IT infrastructure and “good culture” of working with health data . NIHI’s ‘health informatics’ twist in delivering these projects is how we go about defining ‘information’ – using a scientifically credible and robust methodology: openEHR. This is an international (and now national too) standard to non-ambiguously define health information so that they are easy to understand and also are computable. We build software (even automatically in some cases!) using models created by this formalism. I’ll give basics of openEHR approach and then walk you through how to make sense out of all these. Hopefully you may have an idea about its ‘value proposition’ (as business people call) or Science merit as I like to call it ;)
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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1. SNOMED Clinical Terms -
Introduction
Anne Casey RN, MSc FRCN
Editor Paediatric Nursing
Adviser in Informatics Standards to the Royal College of Nursing
Clinical Domain Lead, NHS (England) Information Standards Board
for Health and Social Care
2. Overview
Requirements summary
SNOMED CT
– Basics
– Relationships to other terminologies
Getting involved
– IHTSDO governance
– Working groups
‘Local work towards a national / international standard’
Implementation considerations
Discussion
4. eHealth opportunities
– Multi-disciplinary, cross sector records and
communications
– Ways of recording /displaying information not
possible in paper records (helpful record
structures)
– Ways of aggregating individual level data to
support population analyses
– Content standards for clinical elements of
electronic records (that support good standards of
treatment and care)
5. Context
ICT that supports clinical decision making and the
work of clinicians
Future of cross sector working, team based care,
single shared record, (consumer-owned)
Current ‘mixed economy’ for healthcare records
– separate records for each profession
– single patient record with different sections for each
profession
– single patient record
– Patient/client held record
6. Content Requirement
patient terminology?
nursing terminology
medical terminology
laboratory terminology
physiotherapy terminology, dietetics, ….
drugs, equipment terminologies
social care terminology
…….
7. Requirements to support interoperability
in an eHealth world
Standard, multi professional terminology
with characteristics that support data entry, retrieval,
links to decision support, messaging, maps to other
terminologies / classifications, translation etc.
8. SNOMED Clinical Terms…..
…a terminological resource that can be implemented
in software applications to represent clinically
relevant information
comprehensive (>350,000 concepts)
multi-disciplinary coverage but discipline neutral
structured to support data entry, retrieval, maps etc
Maintained and updated based on user input and
quality review
9. Requirements drive the design,
development and maintenance of SCT
Requirements related to electronic patient records
– Support for effective delivery of high quality healthcare to individuals and to
populations
General requirements for a terminology
Implementation and migration requirements
Requirements related to the intended user communities
– International, multilingual applicability
– Support for particular local requirements
National and strategic priorities
10. General Properties
Interface - supporting data entry
– broad coverage, granular, synonyms, pre-coordinated terms,
‘natural language’, translations
Reference - supporting data retrieval
– Subtype hierarchies that allow items to be aggregated
– Defining relationships that allow computation of equivalence
and subsumption
– … and aspects of the terminology that manage context
11. Relationship between SNOMED CT
and other terminological systems
Different terminologies have features suited to
their different purposes, such as...
clinical definitions
– e.g. pyrexia in an immunocompromised patient = 37.5°C
‘knowledge’ relationships
– e.g. contraindications for use of aspirin
classification concepts
– e.g. other procedure
simple coding scheme for use on paper
grouping / organisation of terms for particular purposes
– e.g. a list of investigations to be done for pyrexial patient
12. SNOMED CT has a specific set of
purposes – it doesn’t do what some other
‘terminologies’ do..
Example 1. ICF
..describes body functions and structures, activities
and participation - used to understand and measure
health outcomes
Example 2. Nursing terminologies such as NANDA
diagnoses
..support the description of nursing for practice,
education and research; may include knowledge that
supports clinical reasoning (e.g. defining
characteristics of NANDA diagnoses)
13.
14. Basic Elements of SNOMED CT
Concepts
• The basic units of SNOMED CT
Descriptions
• These relate terms that name the concepts to the concepts
themselves. Each concept has at least two Descriptions.
Relationships
• Relationships are the connections between concepts in SNOMED
CT.
Attributes / qualifiers - properties of concepts
Formal concept definitions - relationships that define the meaning of a
concept relative to other concepts
Tables – SCT distributed as flat files for incorporation into software
(terminology server or direct to EPR ‘catalogue’)
15. Concepts
Represent distinct clinical meanings
Are identified by a unique numeric identifier (Concept ID)
that never changes and a unique human readable
name (Fully Specified Name)
Are associated with a set of relationships (the “logical
definition”) and two or more descriptions
16. Descriptions
Concept descriptions relate the terms or names of a SNOMED CT
concept to the concept itself.
“Term” in this context means a phrase used to name a concept. A single
description associates a single term with a single Concept ID.
Descriptions are an important interface property because they give end
users the flexibility to use terms that they are familiar with
The Concept ID ties terms with the same meaning together to aid
consistent interpretation and retrieval.
17. Description types
Preferred Term
• The most common word or phrase used by
clinicians to name a concept
The Fully Specified Name
• Provides an unambiguous way to name a concept
Synonyms
• The rest of the names that may be used for a
concept
18. A SNOMED CT Concept
Some of the descriptions associated with ConceptID
22298006:
• Fully Specified Name: Myocardial infarction (disorder)
DescriptionID 751689013
• Preferred term: Myocardial infarction
DescriptionID 37436014
• Synonym: Cardiac infarction
DescriptionID 37442013
• Synonym: Heart attack
DescriptionID 37443015
• Synonym: Infarction of heart
DescriptionID 37441018
19. FSNs reduce ambiguity
Dressing (oneself) Dressing (observable entity)
parent – personal care
activity
Dressing (e.g. a bandage) Dressing, device (physical
object)
Dressing (assisting the Dressing patient (procedure)
person to dress)
Dressing (of wound) Dressing of wound (procedure)
20. The problem with words and
meaning
What is a “pyogenic granuloma?”
• Pyogenic = pus forming
• Granuloma = a collection of inflammatory cells
of a particular type
But
• Pyogenic granuloma = a benign tumor of small
blood vessels of the skin
• It is neither pyogenic nor a granuloma.
Combinations are frequently very
different from the sum of their parts
21. SNOMED is not the “language
police”
SNOMED CT seeks to
• Establish how language is used by clinicians
• To represent meaning as faithfully as possible.
SNOMED CT declares what it thinks a phrase means
• To reflect current usage
• To minimise ambiguity
• Not to shape or control the way a phrase is used
If you want someone to demand that clinicians change what they
call “pyogenic granuloma”
• That may be someone else’s job
• It’s not something SNOMED CT is trying to do
22. Top-level hierarchies
1. Clinical finding 2. Procedure
3. Observable entity 4. Body structure
5. Organism 6. Substance
7. Pharmaceutical/biologic product 8. Specimen
9. Physical object 10. Physical force
11. Events 12. Environment or geographical
Location
13. Social context 14. Situation with explicit context
15. Staging and scales 16. Qualifier value
17. Special concept 18. Linkage concept
19. Record artifact 20. SNOMED-CT UK Administrative
Concepts
23. Relationships
The connections between concepts
• Every SNOMED CT concept has at least one
relationship to another concept
• Relationships characterize concepts and give them
their meaning
• The list of relationships for a particular concept makes
up the logical definition of that concept
2 types:
• IS-A (sub-type/super-type)
• Attribute
24. Relationships cont…
‘Is a’ relationship also known as supertype-subtype or parent
child relationships
A relationship is assigned only when that relationship is
always known to be true (i.e there are no ‘maybes’)
25. ‘Only necessarily true’
‘Pain in calf’ is included in SNOMED CT as it is a
concept agreed to be relevant in practice
It is also possible to say which calf, how severe,
how long etc but these are NOT definitional of
the specific phrase ‘pain in calf’
26. Attribute Relationships
Characterize and specify concepts
An example of an attribute is FINDING-SITE,
which is used to further specify Disease
concepts
- e.g. part of the logical definition of the concept
Pneumonia in SNOMED CT is:
- Concept = Pneumonia
- Attribute = FINDING-SITE
- Value of attribute = Lung structure
27. ‘Context’
Context refers to the effects of embedding a concept
code in a clinical statement
A code is embedded in a clinical statement when it is used in a
clinical record
Embedding a code in a clinical statement
Adds information
– Date of finding or action
– Author, performer, etc.
May also elaborate its meaning in one of several ways
– Subtype qualification
– Axis modification
– Affirmation or Negation
– Combination
28. Context: Representation
Context can be represented in various ways
Pre-coordination of SNOMED CT concepts
– Example
160303001 Family History of diabetes mellitus
Post-coordination of SNOMED CT concepts that together express a
more specific concept
– Example
57177007+(246090004=73211009 ) Family history + (Assoc. finding = Diabetes
mellitus)
Structures and attributes specific to a proprietary data model or a
standard reference model
– Examples from HL7 RIM
ActRelationshipclass
moodCodeattribute
29. Other things –
RefSets: Reference Sets [aka Subsets] including language
RefSets
Extensions: support Realm-specific content that is not required
in the international release
e.g.leave granted under the Mental Health Act 1983 (England and
Wales)
Cross Maps: linking SNOMED CT to other terminologies
mechanisms for developing and maintaining
shared resources
30. Reference Set mechanism
‘The effective usage of SNOMED CT requires a way to
refer to sets of components that are appropriate for a
specific use case’
This ‘referring to’ is achieved by the SNOMED CT
Reference Set mechanism
[SNOMED CT Reference Sets at www.ihtsdo.org]
NOTE: The preferred term is Reference Set (RefSet),
as Subset is potentially misleading (and the RefSet
mechanism does more than the original SNOMED
Subset mechanism).
31. Terminology Infrastructure
IHTSDO Board
SNOMED CT
Committees
international feedback
Working groups
SNOMED CT
National terminology Service + national subsets
National working groups & extensions
System suppliers
SNOMED CT
In Use
Other Terminologies
32.
33.
34.
35. Nursing Special Interest Group
Reports to IHTSDO Content Committee
Objectives:
– ensure adequate content to support nursing practice
– Identify and prioritize new content inclusion
– Develop partnerships and relationships with (nursing) system suppliers
& other specialty organization
– Provide guidance about the use of SNOMED CT in Nursing practice
Open membership: ICN, ANA, RCN, nurses from IT, clinical
practice, education
36. Examples of Nursing SIG projects
Working with SNOMED CT – basic guide
Education, advice and counselling
Representing assessment scales in
SNOMED CT
Lines, catheters, cannulae – with anaesthesia
37. Education Special Interest Group
Reports to Quality Committee
Open participation
Issues
– conflict of interest declaration
– IPR issues
– volunteer v funded work
– Consensus v evidence based
– use what’s out there; provide back to others;
evaluate and refine
38. Education SIG priorities
SNOMED Editors’ curriculum
– Specialist additions: mappers, translators
– Competency framework and certification
– Informs tools specification (safe place to practise!)
SNOMED Implementers’ curriculum
[educational needs of clinical and other communities]
NB curriculum v syllabus or standard course content
39. SNOMED CT Content development
Expert
committee
System content SNOMED CT
developer
End user
40. Contributions to content
development and quality review
Specialty lists - making implementation easier:
radiology, general practice, nursing
Encoding of national (international?) standards
– Assessment instruments: Glasgow coma scale, Barthel
index,
– Standard structured records / clinical datasets: diabetic
retinopathy screening record, national renal dataset
– Archetype repositories
41. Wouldn’t it be
good if..
Clinical guidelines and
accompanying audit
datasets used consistent,
SNOMED aligned
terminology
42. Local work…
Emergency Department diagnosis recording using SNOMED ED
Subset v national data dictionary term list
450 terms v 22 terms (including diagnosis not classifiable)
PRE: 28.9% of 18,457 records had no recorded diagnosis.
POST: 7.7% of 18,798 records had no recorded diagnosis
Lessons learnt: gaps in subset (770); anatomy post co-ordinated?
Tony Shannon, Consultant in Emergency Medicine, Leeds Teaching Hospital &
Clinical Lead, Clinical Content Service, NHS Connecting for Health
43. SNOMED CT –
a terminological resource
‘The benefit of recording information in a
standard terminology such as SNOMED CT is
linked to the benefits of the electronic care
record and the benefits of recording clinical
information in a structured form’
SNOMED CT - the language of the NHS Care Records Service.
www.connectingforhealth.nhs.uk/publications
44. Content standards
A ‘terminological resource’
= Many ways to say the
same thing
SNOMED CT
366,000 Coded Concepts
993,000 Terms For patient safety and
1.46M Relationships good communication we
need standards for record
and message content
including restricted sets of
SNOMED terms/codes
45.
46. Implementation requires evolution of….
SNOMED CT – usable components work
Systems
Users
In the context of ACTUAL
requirements for coded
data (also evolving)
47. Implementation of SNOMED CT
in clinical systems
‘Level 2 systems have internal support for SNOMED CT
using both pre and post co-ordinated content…..
most fully exploit the benefits of using SCT.
For the most part these systems do not exist and will
require the development of new user interfaces,
database information and system interfaces.’
[Implementing SNOMED CT within national electronic record solutions –
CHIRAD Health Informatics, www.chirad.org.uk – Big Issue]
48. Users?
1. Clinicians
Administrative and secretarial staff
Secondary users of data: researchers,
auditors, coders ..
2. System content developers including
expert clinicians
3. Terminology developers
49. Migration of clinician users…
…from unstructured, non …to structured, standard
standard, narrative records records and messages
…from vague, ambiguous, ...to standard, defined,
local terminology and locally evidence based
adapted clinical tools terminologies and tools
...from paper records …to ICT that supports
clinical workflow, decision
making, recording and
communication (and has
standard terminology for
interoperability)