1. Networking and Health Information Exchange
Basic Health Data Standards
Lecture c
Welcome to Networking and Health Information Exchange, Basic Health Data Standards. This is Lecture c.
This component, Networking and Health Information Exchange, addresses what is required to accomplish networking across and among disparate organizations who have heterogeneous systems. As one might imagine, this topic covers a lot of territory fraught with new topics and a lot of acronyms. My apologies, but that is what it is. I suggest you keep your glossary beside you as you study this material.
Unit 4 covers Basic Health Data Standards and consists of six lectures. Over these six lectures, we will identify the set of standards necessary to establish semantic interoperability.
In lecture c, we continue to introduce common, controlled vocabularies.
The fact that it takes two lectures to cover just the top few controlled terminology sets gives an indication of how complex and confusing the issue of “what terminology to use” is. It is important to remember that much of what we hope to accomplish in terms of using HIT to enable a network of seamless care depends on resolving these issues. No one set is enough; mapping between sets results in a loss of information.
This material (Comp 9 Unit 4) was developed by Duke University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC This material was updated by Normandale Community College, funded under Award Number 90WT0003.
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit

2. Basic Health Data Standards Learning Objectives
Understand why it is necessary to use a common set of data elements with common names to be able to exchange and understand data from other places
Understand what is meant by semantic interoperability
Understand many of the sets of controlled vocabularies in use today – how they are used and who requires their use
The Objectives for this unit, Basic Health Data Standards, are to:
Understand why it is necessary to use a common set of data elements with common names to be able to exchange and understand data from other places,
Understand what is meant by semantic interoperability,
Understand many of the sets of controlled vocabularies in use today – how they are used and who requires their use,

3. Basic Health Data Standards Learning Objectives
Understand the use, purpose and interrelation among sets of controlled vocabularies in use today
Identify the more common controlled vocabularies in use today: ICD, CPT, DRG, NDC, RxNorm, and LOINC,
identify the more common controlled vocabularies in use today: SNOMED, MEDCIN, MedDRA, Nursing terminologies, MeSH and UMLS,
Understand data elements; attributes of data elements
Additional Objectives for this unit, Basic Health Data Standards, are to:
Understand the use, purpose and interrelation among sets of controlled vocabularies in use today,
Identify the more common controlled vocabularies in use today: ICD, CPT, DRG, NDC, RxNorm, and LOINC,
Identify the more common controlled vocabularies in use today: SNOMED, MEDCIN, MedDRA, Nursing terminologies, MeSH and UMLS,
Understand data elements; attributes of data elements,

4. Basic Health Data Standards Learning Objectives
Understand contribution of master meta-dictionary of data elements to semantic interoperability
Explain how data structures can be built from basic data components
Explain how templates and archetypes facilitate networking and information interchange and
Discuss Clinical Data Architecture (CDA), Continuity of Care Document (CCD), and Continuity of Care Record (CCR) Standards
Additional Objectives for this unit, Basic Health Data Standards, are to:
Understand contribution of master meta-dictionary of data elements to semantic interoperability,
Explain how data structures can be built from basic data components,
Explain how templates and archetypes facilitate networking and information interchange, and
Discuss Clinical Data Architecture (CDA), Continuity of Care Document (CCD), and Continuity of Care Record (CCR) Standards.

5. Common Controlled Vocabularies
SNOMED -CT
MEDCIN
MedDRA
Nursing terminologies
MeSH
UMLS
In lecture c, we continue a discussion on the use, purpose and interrelation among sets of controlled vocabularies in use today; and identify the more common controlled vocabularies in use today: SNOMED, MEDCIN, MedDRA, Nursing terminologies, MeSH and UMLS.

6. Evolution of SNOMED
Systematized Nomenclature of Diseases and Organisms (SNDO) NY Academy of Medicine
Systematized Nomenclature of Pathology (SNOP)
Systematized Nomenclature of Medicine (SNOMED)
SNOMED-RT (Reference Terminology)(1999)
SNOMED-CT (Merger with Read)(2002)
International Healthcare Terminology SDO (2007)
The roots of SNOMED go back to It was started by the NY Academy of Medicine and called the Systematized Nomenclature of Diseases and Organisms (SNDO). It evolved into SNOP in 1965, under the efforts of the College of American Pathology (CAP).
In 1979, the name was changed to the Systematized Nomenclature of Medicine (SNOMED).
In 1999, with a new release of the terminology set, the words Reference Terminology were added to SNOMED, and it became SNOMED-RT.
In 2002, SNOMED, under CAP, merged with the Reed Clinical Classification System, a coding system used primarily in the United Kingdom, mostly by primary care physicians. The version became known as SNOMED-CT (Clinical Terms).
In 2007, the International Healthcare Terminology SDO was formed. HTSDO acquires, owns and administers the rights to SNOMED-CT and other health terminologies and related standards. SNOMED-CT is the current version and is a semantic network with definitions. SNOMED-CT continues to evolve and is growing in use around the world.

7. SNOMED-CT
Created by the merger, expansion and restructuring of SNOMED-RT and Read Codes
344,000 concepts, 450,000 medical descriptions, 700,000 concept interrelations
Cross maps to ICD9-CM, ICD-10, LOINC
As noted, SNOMED–CT was created by the merger, expansion and restructuring of SNOMED-RT and Read Codes. SNOMED-CT includes 344,000 concepts, 450,000 medical descriptions, 700,000 concept interrelations and that number is increasing. It has been cross-mapped to ICD9-CM, ICD-10, and LOINC.
SNOMED-CT is considered to be possibly the most comprehensive, multilingual clinical healthcare terminology in the world. IHTSDO has issued affiliate licenses to 5,000 individuals and organizations in the world. It is the closest to a universal language there is. Yet it still falls short of adequate clinical representation.
The US originally paid $37.5 million to IHTSDO for SNOMED licenses. Today, SNOMED-CT is available without charge to any person or group in the US. Current dues for the US are still in the millions of dollars.

8. SNOMED-CT Components Concepts Descriptions Hierarchies Relationships
SNOMED CT components include:
Concepts : Basic units of meaning designated by unique code, unique name, and description. Including preferred term and synonyms;
Descriptions: Terms or names (synonyms) assigned to a concept;
Hierarchies: There are 19 higher level hierarchies; each has sub hierarchies;
and
Relationships: Link concepts either within a hierarchy or across hierarchies.

9. Example
|Burn of skin|: |Severity|= |severe |Finding Site|= ( |Structure of skin between fourth and fifth toes|: |Laterality|= |left) Such expressions are said to be post-coordinated as contrasted to pre-coordinated terms.
This example illustrates the hierarchical relationships of SNOMED-CT. The example shows a code for burn of skin that is modified by sub-codes for severity (severe); a finding site (structure of skin between the fourth and fifth toes). In this example, the SNOMED-CT code represents the coding of a severe burn between the fourth and fifth toes and a laterality of the left foot. This SNOMED-CT code is generated from the data by combining terms from the different SNOMED-CT axes.
A pre-coordinated term is one that exists as part of the coded set, i.e., defined in advance. A post-coordinated term is composed just-in-time. SNOMED-CT can be very powerful. However, one of the problems with SNOMED-CT is that a data element can be coded multiple ways, making queries a challenge.

10. Semantic Representation in SNOMED-CT
SNOMED CT is a semantic network. This figure illustrates semantic relationships possible with SNOMED. Semantic relationships are indicated by the is-a link. Other properties are represented by definitive links.
This slide shows semantic representation for the term Pulmonary Tularemia. It is a Tularemia and it is a Bacterial Pneumonia. Pulmonary Tularemia has finding site of Lung Structure; has an associated morphology of Inflammation; and has causative agent of Francisella Tularensis. Knowledge can be imbedded in SNOMED through these links. This representation provides significant power for queries by any of these relationships.
In this example, pulmonary tularemia:
Has-finding-site of lung structure;
Has associated-morphology of inflammation; and
Has-causative-agent of Francisella tularensis.
Source: Dr. James J. Cimino, NIH Clinical Center.

11. SNOMED Axes Findings [F] Social context Procedures [P] General
Body structures (anatomy) [T]
Other
Morphology [M]
Organisms [L]
Substances [C]
Physical agents [A]
Occupations
These are the SNOMED axes:
Findings represented by [F];
Procedures represented by [P];
Body structures represented by (anatomy) [T];
Morphology represented by [M];
Organisms represented by [L];
Substances represented by [C];
Physical agents represented by [A];
Occupations;
Social context;
General; and
Other.
These axes provide the vectors for the semantic network. So a composite code can be represented by zero or multiple codes across all of these axes. SNOMED can be very specific and detailed. Again, queries may be a problem with this structure.

12. Terminology Model Ruptured ovarian cyst (D7-75111)
Is a disease (D )
Has morphology rupture (M-14400)
Has morphology cyst (M-33400)
Has topography ovary (T-87000)
Here is an example showing the semantic relationship coding of SNOMED using some of the axes. The highest level code is structured across the multiple axes of SNOMED.
In this example:
Ruptured ovarian cyst SNOMED code (D )
is a disease SNOMED code (D ) and
has morphology rupture SNOMED code (M-14400) and
has morphology cyst SNOMED code (M-33400) and
has topography ovary SNOMED code (T-87000).

13. MEDCIN
System of standardized medical terminology developed by Medicomp Systems
Includes over 250,000 clinical data elements encompassing symptoms, history, physical exam, tests, diagnoses and therapy
MEDCIN is a system of standardized medical terminology developed by Medicomp Systems. It includes over 250,000 clinical data elements encompassing symptoms, history, physical exam, tests, diagnoses, and therapy.
MEDCIN is available at no cost. However, all of MEDCIN includes a knowledge base linkage for which there is a charge. MEDCIN is used by the Department of Defense. It is the most comprehensive vocabulary for signs and symptoms. Signs and symptoms (or history and physical findings) are usually the last data category to be structured and coded.

14. International Classification for Primary Care (ICPC)
Used by individual family practices and group practices
Use is to record primary care patient encounter
Includes reason for encounter (chief complaint)
Focuses on patient perspective
Includes fear of disease
Request for preventive services
Request for treatment
International Classification for Primary Care (ICPC) is very popular for use in primary care settings throughout the world – except it has little use in the US, probably because ICD 9 coding system is required for reimbursement. It is based on ICD.
ICPC is used by individual family practices and group practices and, is used to record primary care patient encounter. The coding system includes reason for encounter (chief complaint) and focuses on patient perspective. ICPC includes fear of disease, request for preventive services, and request for treatment.
ICPC has been translated into 19 languages; the terminology system is included in UMLS. One of the concerns with ICPC is that the code is based on the body system; the same disease would be coded differently, depending on the body system involved. For example, an ICPC code related to the circulatory system would start with K; for the respiratory system, the code would start with R. The specific item being coded is numeric. For example, heart pain is coded as K21; respiratory pain is coded as R21; and chest pain is coded as A21 (where A represents a general category).

15. Medical Dictionary for Regulatory Activities (MedDRA)
International medical terminology used by regulatory authorities and regulated biopharmaceutical industry
Terminology for coding all medical data obtained during all phrases of development and marketing:
Symptoms
Signs
Disease
Diagnoses
Indications
Investigations/Procedures
Medical/Social History
Medical Dictionary for Regulatory Activities (MedDRA) is an international medical terminology used by regulatory authorities and regulated biopharmaceutical industry.
MedDRA supports terminology for coding all medical data obtained during all phrases of development and marketing. The categories are:
Symptoms,
Signs,
Disease
Diagnoses,
Indications,
Investigations/Procedures, and
Medical/Social History.
MedDRA is developed by The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) and is owned by the International Federation of Pharmaceutical Manufacturers & Associations (IFPMA). It is free for regulators but costs for industry. MedDRA is one of the few coding systems used for adverse events, mostly medication errors. Unfortunately, it is not complete. It has been translated into 9 languages.

16. MedDRA codes Unique 8 digit number, starting with 10000001
As terms added, codes assigned sequentially
Example
Gastric hemorrhage (LLT) =
Gastric haemorrhage (LLT) =
Gastrointestinal disorders (SOC) =
MedDRA is a unique, eight-digit number, starting with , with new terms being coded sequentially.
Examples are:
Gastric hemorrhage (LLT) =
Gastric haemorrhage (LLT) =
Gastrointestinal disorders (SOC) =
Note in the example, the difference in spelling and the different code. While this accommodates the different spellings, the different codes create a challenge to queries. The LLT means lower level term and the HLT is a higher level term. There is an implied hierarchy.

17. Nursing Terminologies
NANDA Taxonomy
Clinical Care Classification
Patient Care Data Set
Omaha System
AORN Perioperative Data Set
International Classification of Nursing Practice
Nursing Interventions Classification
Nursing Outcomes Classification
One could truthfully say there are a lot of nursing terminologies.
The popular nursing terminologies are:
North American Nursing Diagnosis Association (NANDA) Taxonomy,
Clinical Care Classification,
Patient Care Data Set (PCDS),
Omaha System,
AORN Perioperative Data Set,
Nursing Interventions Classification (NIC),
Nursing Outcomes Classification (NOC), and
International Classification of Nursing Practice (ICNP).
The terminologies are all slightly different but, as shown in a later slide, the overlap is large. Many of these codes are proprietary and charge a license fee.

18. Coding Nursing Concepts
Diagnoses/Judgements
Interventions
Outcomes
Goals
Nursing codes are used to define nursing care and are broken into four categories.
There is a nurse terminology that has as its primary use each of the categories. The categories are:
Diagnoses/judgements – NANDA;
Interventions – Omaha;
Outcomes – NOC; and
Goals – PCDS.

19. Nursing Terminologies
Diagnoses
Interventions
Outcomes
Goals
NANDA X
NIC
NOC
CCC
PCDS
Omaha
AORN
ICNP
This table shows the overlaps across the various concepts among the different nursing terminologies. The International Classification of Nursing Practice covers all of the categories. You might ask, “why don’t these terminology sets merge into a single system?” The answer is the same for most terminologies: political and financial.
NANDA – diagnoses
NIC – interventions
NOC – Outcomes
CCC – diagnoses, interventions, and outcomes
PCDS - diagnoses, interventions, and goals
Omaha - diagnoses, interventions, and outcomes
AORN - diagnoses, interventions, and outcomes
ICNP - diagnoses, interventions, outcomes, and goals
Table 4.2: Nursing Terminologies.
Source: Dr. James J. Cimino, NIH Clinical Center.

20. Medical Subject Headings (MeSH)
Used to tag medical abstracts with concept-based information
Created to improve information retrieval
Introduces the notion of “context” for the same concept
Free for use in the US and in most countries
Medical Subject Headings (MeSH) is used by the NLM to index the medical literature. MeSH terms are used to tag medical abstracts with concept-based information. If you write an article that will be indexed by the NLM, the authors are asked to include the appropriate MeSH term. It was created to improve information retrieval.
The 2009 MeSH contains over 25,000 descriptors and over 160,000 entry terms – words that map to specific MeSH descriptors. The MeSH thesaurus has been used to map over 5200 biomedical journals worldwide. MeSH is free.
MeSH makes retrieving the medical literature much easier. Since the terms are selected by the authors, they reflect best the content of the articles.

21. MeSH Example
D011014: Pneumonia D018410: Pneumonia, Bacterial D007877: Legionnaires' Disease D011018: Pneumonia, Pneumococcal D011019: Pneumonia, Mycoplasma D009175: Mycoplasma Infections D011002: Pleuropneumonia, Contagious D011022: Pneumonia, Rickettsial D011023: Pneumonia, Staphylococcal D001996: Bronchopneumonia D009956: Ornithosis D011001: Pleuropneumonia D011015: Pneumonia, Aspiration D011017: Pneumonia, Lipid D011020: Pneumonia, Pneumocystis carinii D011024: Pneumonia, Viral
This slide shows an example of MeSH terms. The MeSH terms are arranged in a hierarchal structure to permit searching at various levels.
This MeSH example for pneumonia illustrates the hierarchical nature of MeSH. For example one level down from the base code of pneumonia is pneumonia, bacterial. Next level down has three branches: Legionnaires’ Disease, Pneumonia, Pneumococcal, and Pneumonia, Mycoplasma. And so forth. The complete structure is:
D011014: Pneumonia
D018410: Pneumonia, Bacterial
D007877: Legionnaires' Disease
D011018: Pneumonia, Pneumococcal
D011019: Pneumonia, Mycoplasma
D009175: Mycoplasma Infections
D011002: Pleuropneumonia, Contagious
D011022: Pneumonia, Rickettsial
D011023: Pneumonia, Staphylococcal
D001996: Bronchopneumonia
D009956: Ornithosis
D011001: Pleuropneumonia
D011015: Pneumonia, Aspiration
D011017: Pneumonia, Lipid
D011020: Pneumonia, Pneumocystis carinii
D011024: Pneumonia, Viral

22. Unified Medical Language System
"The purpose of the [Unified Medical Language System] is to improve the ability of computer programs to 'understand' the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users."
Donald A.B. Lindberg, 1993
D. A. B. Lindberg, B. L. Humphreys, and A. T. McCray, (1993).
Unified Medical Language System is a compendium of many controlled vocabularies in biomedicine and health. It is designed and maintained by the NLM. Anyone can sign up for UMLS and have access to any and all of the contents. UMLS contains many tables supporting various linkages among its contents.
Dr. Donald A.B. Lindberg, Director of the NLM, clearly defined the purpose of the UMLS in 1993.
“The purpose of the [Unified Medical Language System] is to improve the ability of computer programs to ‘understand' the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.”

23. UMLS Structure Three main components: Metathesaurus Semantic Network
Specialist Lexicon and tools
The UMLS has three knowledge sources:
Metathesaurus – a multi-purpose, multi-lingual vocabulary database that contains information about health-related concepts including names, codes and relationships. It is a dictionary of concepts in which different strings are linked to concepts. Two strings pointing to same concepts are synonyms. Strings come from the source vocabularies.
Semantic Network – a set of semantic types that provides a consistent categorization of concepts represented in the Metathesaurus and a useful set of meaningful relationships. Major groupings include anatomic structures, biologic function, chemicals, events, organisms, physical objects, and other.
The Specialist Lexicon – a general English lexicon that includes both commonly-occurring words as well as biomedical terms. The purpose of the Specialist Lexicon is to support Natural Language Processing. The Specialist Lexicon contains over 20,000 entries.

24. Choice of Terminologies
HPI
PMH
Exam
Labs
Diag Dx Rx Tx
ICD + ++
CPT
DRG
NDC
+++
RxNorm
LOINC
Nursing
SNOWMED
MeSH
UMLS
In summary, this table shows the main controlled vocabulary sets used for the different categories of data The number of +s indicates significant use in the US. ICD and CPT have a cost associated with them.
The table shows what terminologies are used for what purposes:
ICD – diagnostic tests, diagnoses, medications, and costs;
CPT – labs, diagnostic tests, diagnoses, medications, and costs;
DRG – diagnoses, reimbursement;
NDC – medications;
RxNorm – medications;
LOINC – labs, diagnostic tests;
Nursing – medications;
SNOMED – history of present illness, past medical history, physical exam, labs, diagnostic tests, diagnoses, medications, treatments;
MeSH - history of present illness, past medical history, physical exam, labs, diagnostic tests, diagnoses, medications, treatments; and
UMLS - history of present illness, past medical history, physical exam, labs, diagnostic tests, diagnoses, medications, and treatments.
Table Choice of terminologies
Source: Dr. James J. Cimino, NIH Clinical Center.

25. Current Situation
SNOMED-CT is gaining momentum as the terminology of choice
LOINC is used in the US for laboratory test names
RxNorm is likely to be drug code of choice in US
ICD x is required for reimbursement
Adverse events – maybe MedDRA
All of these terminologies are in use today. Most institutions will use a combination of these terminologies, as well as local terminologies. The current status of terminologies, at least in the US, at the present time includes:
SNOMED-CT, which is gaining momentum as the terminology of choice;
LOINC, which is used for laboratory test names;
RxNorm, which is likely to be drug code of choice;
ICD x, which is required for reimbursement (ICD-9 and moving into ICD-10); and
MedDRA, which is used for adverse events.
Today’s world maps among these controlled terminologies. If mapping between two controlled terminologies is perfect, then why do we need both sets; if not, then mapping must be accompanied with information loss. In any case, these codes are a distance from real clinical usage.
A common global master set of data elements does not exist. Until it does, true interoperability remains a goal.

26. Basic Health Data Standards Summary – Lecture c
The complex problems caused by use of local terminologies and too many controlled terminologies continues to be an obstacle to interoperability. Until this problem is solved, the sharing and aggregation of data among disparate sites will result in a loss of information and prone to error.
This concludes Lecture c of Basic Health Data Standards.
The complex problems caused by use of local terminologies and too many controlled terminologies continues to be an obstacle to interoperability. Until this problem is solved, the sharing and aggregation of data among disparate sites will result in a loss of information and be prone to error.

27. Basic Health Data Standards Summary – Lecture c
This Lecture has discussed a set of controlled terminologies that are used in healthcare and healthcare processes and has included characteristics and examples of each terminology set.
Comparison among the terminologies was also highlighted.
Additionally in lecture C we discussed a set of controlled terminologies that are used in healthcare and healthcare processes and included characteristics and examples of each terminology set. Comparison among terminologies was also highlighted.

28. Basic Health Data Standards References – Lecture c
Lindberg, D. A. B., Humphreys, B. L., & McCray, A. T. (1993). The Unified Medical Language System. Methods Inform. Medicine, 32, 281.
Charts, Tables, Figures
4.2 Table. Courtesy of Dr. James J. Cimino, NIH Clinical Center.
4.3 Table. Courtesy of Dr. James J. Cimino, NIH Clinical Center.
No audio.
Images
Slide 10: : Source: Courtesy of Dr. James J. Cimino, NIH Clinical Center.

29. Basic Health Data Standards Lecture c
This material was developed by Duke University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC This material was updated by Normandale Community College, funded under Award Number 90WT0003.
No audio.
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