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Query Health Concept-to-Codes (C2C) SWG Meeting #4

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Presentation on theme: "Query Health Concept-to-Codes (C2C) SWG Meeting #4"— Presentation transcript:

1 Query Health Concept-to-Codes (C2C) SWG Meeting #4
January 3, 2012

2 Today’s Agenda Topic Time Allotted
Quick Review of Updated Timeline and Future Meeting Times 2:30 – 2:35 Presentation by Subject Matter Experts Rick Biehl – DOQS 2:35 - 3:00 Jeff Brown - PopMedNet 3:00 – 3:30 Olivier Bodenreider – NLM 3:30 – 4:00

3 Starting Jan 3rd, meeting times extended from 2:30-4:00pm
Proposed Timeline Starting Jan 3rd, meeting times extended from 2:30-4:00pm TODAY Meeting 1 – Dec 6 Meeting 2 – Dec 13 Meeting 3 – Dec 20 Meeting 4 – Jan 03 Meeting 5 – Jan 10 Meeting 6 – Jan 17 Meeting 7 – Jan 24 Meeting 8 – Jan 31 Meeting 9 – Feb 7 Tasks Introductions Scope Proposed Approach Identify SME and presentation timeline for next few meetings Presentation hQuery i2b2 Presentation I2b2 (Cont.) Intermountain Health DOQS (Data Warehousing / Mapping) Presentation DOQS (Data Warehousing / Mapping) Cont. PopMedNet NLM Presentation Ibeza CDISC SHARE Presentation NQF LexEVS RELMA (LOINC) 3M Tasks Preliminary review of presentation summaries and Draft Deliverable Tasks Review of presented concept mapping frameworks to select a proposed approach Begin Consensus Voting process Tasks Consensus Voting Finalized Coordinate offline activities to summarize approaches and develop draft deliverable from presentations

4 Data Oriented Quality Solutions (DOQS)
Rick Biehl, Ph.D Data Oriented Quality Solutions (DOQS)

5 5

6 CLINICAL PHENOTYPE GENOTYPE
6

7 CLINICAL PHENOTYPE GENOTYPE
7

8 8

9 All of the dimensions are the same 6-table implementation.
9

10 Largely inspired by the work of Ralph Kimball
10

11 CATEGORY Hospital Physician Drug
The categories represent the LOGICAL subdimensions of the data warehouse. 11

12 Transferring Hospital Attending Physician Consulting Physician
ROLE Admitting Hospital Transferring Hospital Attending Physician Consulting Physician Admitting Physician Ordered Drug Administered Drug 12

13 Ontologies happen here!
TYPE : Network, Directed Acyclic Graph, or Hierarchy Ontologies happen here! PERSPECTIVE ICD decomposes ICD-9 384 Acetaminophen is an Analgesic Tylenol brands Acetaminophen Tylenol 350 Caps instantiates Tylenol Vesicle is an Organelle Lower jaw bone is synonym of Mandible 13

14 How many analgesics were administered?
CATEGORY PERSPECTIVE ROLE How many analgesics were administered? Query all facts where a drug (category) was administered (role) and Analgesic was available in any higher perspective. 14

15 Open Biomedical Ontology (OBO) Group
Data that complies with the meta-model defined by the BFO will be able to behave in an integrated way across widely varying federated data structures. Open Biomedical Ontology (OBO) Group 15

16 Who? What? Where? When? How? Why? QUERY 16

17 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Spatial Region 3D, 2D, 1D, 0D Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Dependent Continuant Quality Realizable Entity Function Role Disposition Processual Entity Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process SPAN Occurrent Spatiotemporal Region Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Temporal Region Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant 17

18 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) Spatial Region 3D, 2D, 1D, 0D Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Dependent Continuant Quality Realizable Entity Function Role Disposition Processual Entity Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process SPAN Occurrent Spatiotemporal Region Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Temporal Region (a) Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 18

19 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) (b) Spatial Region Geopolitics 3D, 2D, 1D, 0D Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Dependent Continuant Quality Realizable Entity Function Role Disposition Processual Entity Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process SPAN Occurrent Spatiotemporal Region Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Temporal Region (a) Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 19

20 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) (b) Spatial Region Geopolitics 3D, 2D, 1D, 0D (c) Organization Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Caregiver Patient Dependent Continuant Anatomy Quality Realizable Entity Function Role Disposition Diagnosis Procedure Processual Entity Material Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Facility SPAN Occurrent Accounting Spatiotemporal Region Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Temporal Region (a) Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 20

21 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) Spatial Region Geopolitics 3D, 2D, 1D, 0D Organization Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Caregiver Patient (d) Dependent Continuant Anatomy Quality Realizable Entity Function Role Disposition Diagnosis Procedure Processual Entity Material Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Facility SPAN Occurrent Accounting Spatiotemporal Region Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Temporal Region Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 21

22 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) Spatial Region Geopolitics 3D, 2D, 1D, 0D Organization Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Caregiver Patient (d) Dependent Continuant Anatomy Quality Realizable Entity Function Role Disposition Diagnosis Procedure Processual Entity (e) Material Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Facility SPAN Occurrent Accounting Spatiotemporal Region Encounter Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Operation Temporal Region Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 22

23 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) Spatial Region Geopolitics 3D, 2D, 1D, 0D Organization Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Caregiver Patient (d) Dependent Continuant Anatomy (f) Quality Realizable Entity Function Role Disposition Diagnosis Procedure Processual Entity (e) Material Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Facility SPAN Occurrent Accounting Spatiotemporal Region Encounter Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Operation Facts Temporal Region Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 23

24 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) Spatial Region Geopolitics 3D, 2D, 1D, 0D Organization Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Caregiver Patient (d) Dependent Continuant Anatomy (f) Quality Realizable Entity Function Role Disposition Diagnosis Procedure Processual Entity (e) Material Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Facility SPAN Occurrent Accounting (g) Spatiotemporal Region Encounter Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Operation Facts Temporal Region Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 24

25 Independent Continuant Spatiotemporal Region
Basic Formal Ontology (BFO) Clinical Data Warehouse (CDW) Spatial Region Geopolitics 3D, 2D, 1D, 0D Organization Independent Continuant SNAP Continuant Site Object Object Aggregate Fiat Part of Object Boundary of Object Caregiver Patient Dependent Continuant Anatomy Quality Realizable Entity Function Role Disposition Diagnosis Procedure Processual Entity Material Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Facility Queries happen here! SPAN Occurrent Accounting Spatiotemporal Region Encounter Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Operation Facts Temporal Region Calendar Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Clock 25

26 Thank You! You are welcome to contact me for additional information at any time: Richard E. Biehl, Ph.D. Data-Oriented Quality Solutions Coming in ! 26

27 Jeff Brown, Ph.D PopMedNet

28 Query Health Clinical Working Group Concept Mapping sub-working group
PopMedNet Distributed Research Network Technologies for Population Medicine Query Health Clinical Working Group Concept Mapping sub-working group January 3, 2012 PopMedNet™ was developed under contract from the Agency for Healthcare Research and Quality, US Department of Health and Human Services as part of the Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) program, awarded to the DEcIDE centers at the HMO Research Network Center for Education and Research on Therapeutics (HMORN CERT) and the University of Pennsylvania. The Food and Drug Administration’s Mini-Sentinel project provided additional support. PopMedNettm contact: 28

29 Lessons 29 contact:

30 Lessons Ministers will today say the ill-fated £11.4 billion National Programme for IT, set up in 2002, is to be “urgently dismantled” following criticism that it is not value for taxpayers’ money. After an official review, the “one size fits all” project will be replaced by cheaper regional schemes allowing local health trusts and GPs to develop or buy individual computer systems to suit their needs. 30 contact:

31 Lessons The project was initiated in 1998 at an estimated cost of $68 million….. The current estimated cost to complete is now placed at a whopping $722 million, with some $670 million of that going to the defense contractor SAIC for the system's development…. most of the remaining tens of millions have been spent on outside contract and project management to control the project's cost and quality… 31 contact:

32 Lessons 32 contact:

33 Lessons The main stated barriers to adoption are:
1. …is quite complex and requires resident experts in Java programming to support the data integration both across the center and for institution to institution communication. 2. There is no graphical user interface to simplify basic administrative or configurational tasks. 3. Constant changes in the grid architecture and individual tools (“software churn”) increased barriers to adoption and made commercial offerings more attractive, even if they did not offer the same promise of data sharing and common semantics. The following are representative comments: • …is of very limited use. Part of the problem is that not much data is currently being shared there, and part is the complexity and cumbersomeness of the system design. • …implemented but not used. Center doesn’t really want to share data anyways • Not rigorously security tested 33 contact:

34 Lessons “…..the strategic goals of the program were determined by technological advances rather than by key, pre-determined scientific and clinical requirements. …. ended up developing powerful and far-reaching technology,…without clear applications to demonstrate what these technologies could and would do… the WG struggled to find projects that could not have been implemented with alternative less expensive or existing technologies and software tools.” 34 contact:

35 Distributed Querying Approach
Standardize the data in a network common data model HMORN Virtual Data Warehouse (VDW) Mini-Sentinel CDM Electronic Support for Public Health (ESP) Summary Tables Others Distribute code based on the network’s CDM to partners for local execution Results securely returned to requester 35 contact:

36 Overview of Query Distribution /Response
Secure Network Portal (PopMedNet Portal) 2 1 5 Authorized Requestor/ Investigator 4 3 Review & Run Query Review & Return Results Data Partner N Data Partner 1 PopMedNet DataMart Client Desktop Application 1- Query created and submitted by authorized user on the secure network portal 2- Data partners notified of query and retrieve it from the secure network portal 3- Data partners review and run query against their local data 4- Data partners review results 5- Data partners securely return results to the secure network portal for review by requestor Standardized Data PopMedNet DataMart Client Desktop Application Standardized Data 36 contact:

37 3 Approaches to Querying Distributed Data
Distribute custom programs (SAS, SQL, etc) for in- depth analysis against encounter-level data in a standard format (network specific) Menu-driven querying against encounter-level data a in standard format (network specific) Menu-driven querying of pre-tabulated summary tables (network specific) 37 contact:

38 Architecture: Keep Power in Hands of the DPs
Networks exist at the pleasure of the data partners Keep the decision to participate, and how to participate, in DP control Approach: practical within our partners’ social, regulatory, and business environment Lowers barriers to acceptance and implementation Small IT footprint and limited risk Minimize need for extensive database expertise and ongoing maintenance/management of complex data structures Design allows automation of any step via role based access control 38 contact: 38

39 Uses of PopMedNet Mini-Sentinel (FDA)
Public health surveillance for medical product safety 17 health plan sites, encounter-level and summary-level data model Scalable PArtnering Network for CER (AHRQ) CER network focusing on ADHD and obesity 11 sites, HMORN VDW and summary tables as the data models HMO Research Network DEcIDE center (AHRQ) CER; 4 sites, HMORN VDW and summary tables as the data model Population-Based Effectiveness in Asthma and Lung Diseases (AHRQ) CER; 4 sites, HMORN VDW as the data model MDPHnet (ONC) Public health surveillance Multiple medical group practice networks using EHR-based data model Uses menu-driven querying and complex “stored queries” for specific measures 39

40 Mini-Sentinel Guiding Principles (selected)
Data Partners have the best understanding of their data and its uses Valid use and interpretation of findings requires input from the Data Partners. Distributed programs should be executed without site-specific modification after appropriate testing. The Mini-Sentinel Common Data Model accommodates all requirements of Mini-Sentinel data activities and may change to meet FDA objectives. The objectives of the network are paramount and should dictate all decisions

41 Uber-network versus Targeted Networks
Designing a single uber-network will likely fail to meet the needs of all (and will likely fail) An HIE dedicated to providing information about a patient at a single point in time has different needs than a network dedicated to comparative effectiveness research or a network for quality of care measures Just because the “data are the same” doesn’t make it possible to use the same system for different purposes Research versus public health surveillance versus operations Demands for sensitivity and specificity are very different across uses Mistake to pretend EHR data can be readily combined for any use: targeted networks can limit scope to appropriate use Although EHR information has important uses, it has important limitations that must be recognized 41 contact:

42 Overview and Current Status
How do you define concept mapping within your system (e.g. are you mapping in between standards, or are you mapping from standards to your local data dictionary)? PopMedNet facilitates creation, operation, and governance of networks, each network decides how to standardize data and queries PMN networks typically standardize formatting but avoid concept mapping, with some exceptions Demographics SEX: Force into values of M, F, and U although local codes could be more expansive, including transgender. Not known if self-reported or observed. RACE: Force into standard race terminology, local information could have hundreds of categories. Not known if self-reported, observed, or imputed. Enrollment Mini-Sentinel simplifies enrollment categories into Medical Coverage (Y/N) or Drug Coverage (Y/N), HMORN has may more enrollment categories (Medicare, Medicaid, PPO, POS, HDHP, HMO, self-pay, etc) Granularity is based on needs of the network 42 contact:

43 Overview and Current Status (2)
How do you define concept mapping within your system (e.g. are you mapping in between standards, or are you mapping from standards to your local data dictionary)? Encounters TYPE OF ENCOUNTER: Normalize into relevant categories based on network. EHRs can have thousands of encounter types, all with local codes. Mini-Sentinel uses 5 encounter types, HMORN uses about 10. Definition of an encounter is crucial for some uses Diagnoses/Procedures Coding system network based; use native standardized codes (ICD9, HCPCS) and map local to standard if possible. Data models maintain local code (original code in electronic system) Do not map standards to standard; map local codes to standard as possible Use central DX/PX look-up to classify and group as needed Outpatient Pharmacy Dispensings Use NDC to identify dispensings, local codes allowed. Use central NDC look-up to classify and group NDCs as needed Some standardization related to reversals and negative values 43 contact:

44 Overview and Current Status (3)
How do you define concept mapping within your system (e.g. are you mapping in between standards, or are you mapping from standards to your local data dictionary)? Laboratory Results Must use mapping of local codes to standards, and within standard mapping Mini-Sentinel and HMORN developed their own data model to facilitate Requires substantial work to get information ready for research Target selected high-priority labs for standardization Data model must specify exactly what is meant by each lab type, which standard codes are encompassed in that lab type, and sites have to handle local mapping Vital Signs Format standardization (inches, pounds, smoking status) 44 contact:

45 Overview and Current Status
Are there any internal mechanism? PopMedNet does not include any mapping capability Networks powered by PMN standardize the data and decide on querying approach, PMN facilitates A data model plug-in is possible to translate queries between models Do you use any external tools? PMN querying tools are network specific (SAS, SQL, etc) Mappings are limited to industry standard terminologies (NDC, ICD9, HCPCS, LOINC) Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)? Networks determine how to store data. Most data models maintain local codes even if the code is mapped 45 contact:

46 Integration and Infrastructure
How can you integrate with external tools for mapping? JavaScript library? Java? Web Services API? PopMedNet has a Web services API/ plug-in architecture How do you see your framework integrating with the QH Reference Implementation solution? PopMedNet is the transport mechanism and governance tool Networks will develop unique solutions for querying 46 contact:

47 Alignment to Query Health
Where does the mapping occur? Is it at the Data Source level? Or at the Information Requestor level? Or Both? All existing systems using PopMedNet following the same basic approach: Data are standardized into a CDM at each site Each query uses its own definition of all concepts based on the CDM No implementations allow site-by-site concept creation, it is always the requester that defines important clinical concepts Sites don’t define diabetes, investigators do Can it be easily implemented elsewhere? PopMedNet is agnostic to the implementation decisions Translate and map the data , translate and map the query, a mix of both Translate at run-time, translate nightly, weekly All decisions of the network 47 contact:

48 Maintenance Who maintains your concept mapping tool?
Who maintains the mappings and how often are they released? What is the associated cost with maintenance? All network implementations use a dedicated coordinating center to oversee the integrity of the data and use of the network Requires substantial resources to ensure appropriate use 48 contact:

49 Query Health Clinical Working Group Concept Mapping sub-working group
PopMedNet Distributed Research Network Technologies for Population Medicine Query Health Clinical Working Group Concept Mapping sub-working group January 3, 2012 PopMedNet™ was developed under contract from the Agency for Healthcare Research and Quality, US Department of Health and Human Services as part of the Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) program, awarded to the DEcIDE centers at the HMO Research Network Center for Education and Research on Therapeutics (HMORN CERT) and the University of Pennsylvania. The Food and Drug Administration’s Mini-Sentinel project provided additional support. PopMedNettm contact: 49

50 Software Features Secure, private multi-center research networks
Open source application Data partners maintain control of their data Flexible governance, access control, permissions, auditing Secure FISMA-compliant platform Mature documentation and set-up procedures Scalable: easy to add new data, new partners Interoperable with other networks using the same software (PopMedNet) 50 contact:

51 DataMart Administrator
PopMedNet Architecture Researchers Web Services Document Manager IRB Model Workflow Project Organization User DataMart Archive Search Audit Request Schema Results Meta Data Business Objects Security Roles Rights Access Control Data Source Data Access Portal Database Network Portal Presentation Layer Content Network Models Public Admin DataMart Application Business Objects Web Services Connection Manager Security Results Request Presentation Layer DataMart Administrator Data Manager Data Source Database Data Partner Data Partner Host Common Data Model EMR DataMart Administrator Internet Network Administrators Internet Archive Data Vault Repository Database

52 DataMart Administrator
PopMedNet Architecture DataMart Application Business Objects Web Services Connection Manager Security Results Request Presentation Layer DataMart Administrator Data Manager Data Source Database Data Partner Data Partner Host Common Data Model EMR DataMart Administrator

53 Olivier Bodenreider, M.D.
National Library of Medicine - NLM

54 NLM resources for Clinical Concept Mapping
Standards and Interoperability (S&I) Framework Clinical Concept Mapping (Sub-Work Group) December 20, 2011 Dr. Olivier Bodenreider U.S. National Library of Medicine, Bethesda, MD 54

55 Use cases text-to-reference code-to-reference reference-to-code query
translation database “Addison’s disease” umls:C snomedct: snomedct: fdb:019188 fdb:019188 ndc: rxnorm:854873 Zolpidem tartrate 10 MG Oral Tablet 55

56 Integrating vocabularies
MIE Geneva, Switzerland August 28, 2005 Integrating vocabularies Clinical repositories SNOMED CT Genetic knowledge bases OMIM Other subdomains UMLS Biomedical literature MeSH Genome annotations GO Anatomy FMA Model organisms NCBI Taxonomy 56 UMLS Tutorial - O. Bodenreider (NLM)

57 Integrating vocabularies
MIE Geneva, Switzerland August 28, 2005 Integrating vocabularies Clinical repositories Genetic knowledge bases Other subdomains Biomedical literature Model organisms Genome annotations Anatomy 57 UMLS Tutorial - O. Bodenreider (NLM)

58 Integrating vocabularies
MIE Geneva, Switzerland August 28, 2005 Integrating vocabularies Addison's disease ( ) Clinical repositories Genetic knowledge bases OMIM Other subdomains SNOMED CT UMLS UMLS Biomedical literature Genome annotations GO MeSH Anatomy FMA Model organisms NCBI Taxonomy C Addison Disease (D000224) 58 UMLS Tutorial - O. Bodenreider (NLM)

59 What does UMLS stand for?
O. Bodenreider - NLM 4/14/2017 What does UMLS stand for? Unified Medical Language System UMLS® Unified Medical Language System® UMLS Metathesaurus® 59 Unified Medical Language System (UMLS) Overview

60 Organize terms Synonymous terms clustered into a concept
O. Bodenreider - NLM 4/14/2017 Organize terms Synonymous terms clustered into a concept Preferred term Unique identifier (CUI) Addison Disease MeSH D000224 Primary hypoadrenalism MedDRA Primary adrenocortical insufficiency ICD-10 E27.1 Addison's disease (disorder) SNOMED CT C Addison's disease 60 Unified Medical Language System (UMLS) Overview

61 MIE 2005 - Geneva, Switzerland
August 28, 2005 Source Vocabularies (2011AB) 160 source vocabularies 21 languages Broad coverage of biomedicine 8M names (normalized) 2.6M concepts >10M relations Common presentation 61 UMLS Tutorial - O. Bodenreider (NLM)

62 Source Vocabularies in UMLS
MIE Geneva, Switzerland August 28, 2005 Source Vocabularies in UMLS General vocabularies anatomy (FMA, Neuronames) drugs (RxNorm, First DataBank, Micromedex) medical devices (UMD, SPN) Several perspectives clinical terms (SNOMED CT) information sciences (MeSH) administrative terminologies (ICD-9-CM, ICD-10-CM, CPT-4) data exchange terminologies (HL7, LOINC) 62 UMLS Tutorial - O. Bodenreider (NLM)

63 Source Vocabularies in UMLS
MIE Geneva, Switzerland August 28, 2005 Source Vocabularies in UMLS Specialized vocabularies nursing (NIC, NOC, NANDA, Omaha, ICNP) dentistry (CDT) oncology (PDQ) psychiatry (DSM, APA) adverse reactions (MedDRA, WHO ART) primary care (ICPC) Terminology of knowledge bases (AI/Rheum, DXplain, QMR) The UMLS serves as a vehicle for the regulatory standards (HIPAA, HITSP, Meaningful Use) 63 UMLS Tutorial - O. Bodenreider (NLM)

64 Source vocabularies in RxNorm
(terms in thousands, as of October 2011) Gold Standard Alchemy Master Drug Data Base (Medi-Span, Wolters Kluwer Health) Multum MediSource Lexicon Micromedex DRUGDEX Medical Subject Headings FDA National Drug Code Directory FDA Structured Product Labels Nat’l Drug Data File (First DataBank Inc.) VHA National Drug File – RT SNOMED Clinical Terms (drug information) VHA National Drug File 26 13 67 46 19 66 55 85 116* 88* 38 64

65 Application Programming Interfaces
UMLS SOAP-based Supports term-to-cui, code-to-cui and cui-to code (+ mapping relations) https://uts.nlm.nih.gov//doc/devGuide/index.html RxNorm SOAP-based and RESTful Supports term-to-rxcui, code-to-rxcui and rxcui-to code 65

66 Questions for Considerations
Frameworks (Ex. - i2B2, PMN, hQuery) Resources and Tools (UMLS/UTS, RxNorm/RxNav) Standards Overview and Current Status How do you define concept mapping within your system (e.g. are you mapping in between standards, or are you mapping from standards to your local data dictionary)? Are there any internal mechanism? Do you use any external tools? Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)? Terminology integration system Source transparency (most original terminologies can be recreated from the UMLS; generally not the case for RxNorm) How do your standards relate to concept mapping? Integration and Infrastructure How can you integrate with external tools for mapping? JavaScript library? Java? Web Services API? UMLS: - GUI: UTS - API: SOAP-based RxNorm - GUI: RxNav - API: SOAP-based + RESTful What infrastructure is necessary to implement / utilize your standard? Alignment to Query Health Is your framework geared towards the Data Source? The Information Requestor? Or Both? Includes all major clinical terminologies Bridges between query (text, code) and data source (standard code) Are the standards developed around concept mapping at the data source level? The Information Requestor level? Or Both? Maintenance Who maintains your concept mapping tool? Who maintains the mappings and how often are they released? What is the associated cost with maintenance? NLM develops the UMLS and RxNorm (data + tooling) Release schedule - UMLS: twice yearly - RxNorm: monthly No fee to the end user (but license agreement required*) Who maintains the development of standards? What is the associated cost with maintenance and periodic releases? Questions for Considerations 66

67 References O. Bodenreider - NLM 4/14/2017
67 Unified Medical Language System (UMLS) Overview

68 References: UMLS home page
O. Bodenreider - NLM 4/14/2017 References: UMLS home page UMLS home page UMLS documentation Reference manual Source documentation UMLS online tutorials 68 Unified Medical Language System (UMLS) Overview

69 Other things you would need to know
UMLS license agreement https://uts.nlm.nih.gov/help/license/LicenseAgreement.pdf MetamorphoSys s/metamorphosys/index.html UMLS Terminology Services (UTS) https://uts.nlm.nih.gov/ 69

70 References: RxNorm RxNorm home page RxNav home page Content
O. Bodenreider - NLM 4/14/2017 References: RxNorm RxNorm home page Content RxNav home page Browser + APIs 70 Unified Medical Language System (UMLS) Overview


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