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

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Presentation on theme: "Query Health Concept-to-Codes (C2C) SWG Meeting #4 January 3, 2012 1."— Presentation transcript:

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

2 Today’s Agenda TopicTime 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 2

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

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

5 Query Health – Clinical WG, 2011-12-20 5

6 CLINICAL PHENOTYPE GENOTYPE 6

7 CLINICAL PHENOTYPE GENOTYPE 7

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11 Query Health – Clinical WG, 2011-12-20 CATEGORY Hospital Physician Drug 11

12 Query Health – Clinical WG, 2011-12-20 ROLE Admitting Hospital Transferring Hospital Attending Physician Consulting Physician Admitting Physician Ordered Drug Administered Drug 12

13 Query Health – Clinical WG, 2011-12-20 PERSPECTIVE TYPE : Network, Directed Acyclic Graph, or Hierarchy ICD-9 384.6 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 Query Health – Clinical WG, 2011-12-20 How many analgesics were administered? PERSPECTIVE ROLE CATEGORY Query all facts where a drug (category) was administered (role) and Analgesic was available in any higher perspective. 14

15 Query Health – Clinical WG, 2011-12-20 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. 15

16 Query Health – Clinical WG, 2011-12-20 Who? What? Where? When? How? Why? QUERY 16

17 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) 17

18 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Calendar Clock Clinical Data Warehouse (CDW) (a) 18

19 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Geopolitics Calendar Clock Clinical Data Warehouse (CDW) (a) (b) 19

20 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Organization Caregiver Patient Anatomy Diagnosis Procedure Material Facility Accounting Geopolitics Calendar Clock Clinical Data Warehouse (CDW) (a) (b) (c) 20

21 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Organization Caregiver Patient Anatomy Diagnosis Procedure Material Facility Accounting Geopolitics Calendar Clock Clinical Data Warehouse (CDW) (d) 21

22 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Organization Caregiver Patient Encounter Anatomy Diagnosis Procedure Material Facility Accounting Geopolitics Calendar Clock Clinical Data Warehouse (CDW) Operation (d) (e) 22

23 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Organization Caregiver Patient Encounter Anatomy Diagnosis Procedure Material Facility Accounting Geopolitics Calendar Clock Clinical Data Warehouse (CDW) Operation Facts (d) (f) (e) 23

24 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Organization Caregiver Patient Encounter Anatomy Diagnosis Procedure Material Facility Accounting Geopolitics Calendar Clock Clinical Data Warehouse (CDW) Operation Facts (d) (g) (f) (e) 24

25 Query Health – Clinical WG, 2011-12-20 Spatiotemporal Region SNAP Continuant SPAN Occurrent Spatial Region Independent Continuant Dependent Continuant Processual Entity Temporal Region Site Object Object Aggregate Fiat Part of Object Boundary of Object 3D, 2D, 1D, 0D Quality Realizable Entity Function Role Disposition Scattered Spatiotemporal Region Connected Spatiotemporal Region Spatiotemporal Interval Spatiotemporal Instant Processual Context Process Aggregate Process Fiat Part of Process Boundary of Process Scattered Temporal Region Connected Temporal Region Temporal Interval Temporal Instant Basic Formal Ontology (BFO) Organization Caregiver Patient Encounter Anatomy Diagnosis Procedure Material Facility Accounting Geopolitics Calendar Clock Clinical Data Warehouse (CDW) Operation Facts Queries happen here! 25

26 Query Health – Clinical WG, 2011-12-20 Thank You! You are welcome to contact me for additional information at any time: Richard E. Biehl, Ph.D. Data-Oriented Quality Solutions rbiehl@doqs.com 26

27 Jeff Brown, Ph.D PopMedNet 27

28 PopMedNet PopMedNet Distributed Research Network Technologies for Population Medicine 28 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. tm PopMedNet tm

29 29 Lessons

30 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.

31 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…

32 32 Lessons

33 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

34 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.”

35 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 Distributed Querying Approach

36 Overview of Query Distribution /Response 36 Secure Network Portal (PopMedNet Portal) 2 1 5 Authorized Requestor/ Investigator 4 3 Review & Run Query Review & Return Results Data Partner N 4 3 Review & Run Query Review & Return Results 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

37 3 Approaches to Querying Distributed Data 1) Distribute custom programs (SAS, SQL, etc) for in- depth analysis against encounter-level data in a standard format (network specific) 2) Menu-driven querying against encounter-level data a in standard format (network specific) 3) Menu-driven querying of pre-tabulated summary tables (network specific) 37

38 Architecture: Keep Power in Hands of the DPs 38 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

39 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 Uses of PopMedNet 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 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 Uber-network versus Targeted Networks

42 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 Overview and Current Status

43 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 Overview and Current Status (2)

44 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 Overview and Current Status (3)

45 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 Overview and Current Status

46 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 Integration and Infrastructure

47 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 Alignment to Query Health

48 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 Maintenance

49 PopMedNet PopMedNet Distributed Research Network Technologies for Population Medicine 49 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. tm PopMedNet tm

50 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 Software Features

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

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

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

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 “Addison’s disease” translationquerydatabase ndc:16590052730 umls:C0001403 fdb:019188 rxnorm:854873 snomedct: 363732003 text-to-reference code-to-reference reference-to-code Zolpidem tartrate 10 MG Oral Tablet fdb:019188 snomedct: 363732003 55

56 Integrating vocabularies Biomedical literature Biomedical literature MeSH Genome annotations Genome annotations GO Model organisms Model organisms NCBI Taxonomy Genetic knowledge bases OMIM Clinical repositories Clinical repositories SNOMED CT Other subdomains Other subdomains … Anatomy FMA UMLS 56

57 Integrating vocabularies Biomedical literature Biomedical literature Genome annotations Genome annotations Model organisms Model organisms Genetic knowledge bases Clinical repositories Clinical repositories Other subdomains Other subdomains Anatomy 57

58 Integrating vocabularies Genome annotations Genome annotations GO Model organisms Model organisms NCBI Taxonomy Genetic knowledge bases OMIM Other subdomains Other subdomains … Anatomy FMA UMLS Addison Disease (D000224) Addison's disease (363732003) Biomedical literature Biomedical literature MeSH Clinical repositories Clinical repositories SNOMED CT UMLS C0001403 58

59 What does UMLS stand for? Unified Medical Language System UMLS ® Unified Medical Language System ® UMLS Metathesaurus ® 59

60 Organize terms Synonymous terms clustered into a concept Preferred term Unique identifier (CUI) Addison's disease Addison DiseaseMeSHD000224 Primary hypoadrenalismMedDRA10036696 Primary adrenocortical insufficiencyICD-10E27.1 Addison's disease (disorder)SNOMED CT363732003 C0001403 60

61 Source Vocabularies 160 source vocabularies 21 languages Broad coverage of biomedicine ◦ 8M names (normalized) ◦ 2.6M concepts ◦ >10M relations Common presentation (2011AB) 61

62 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

63 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) The UMLS serves as a vehicle for the regulatory standards (HIPAA, HITSP, Meaningful Use) 63

64 Source vocabularies in RxNorm 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 67 46 66 55 38 85 88* 13 (terms in thousands, as of October 2011) 116* 19 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 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 ◦ http://rxnav.nlm.nih.gov/ http://rxnav.nlm.nih.gov/ 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? Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)? 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? Who maintains the mappings and how often are they released? What is the associated cost with maintenance and periodic releases? 66

67 References 67

68 References: UMLS home page UMLS home page ◦ http://www.nlm.nih.gov/research/umls/ http://www.nlm.nih.gov/research/umls/ UMLS documentation ◦ Reference manual http://www.ncbi.nlm.nih.gov/books/NBK9676/ http://www.ncbi.nlm.nih.gov/books/NBK9676/ ◦ Source documentation http://www.nlm.nih.gov/research/umls/sourcereleasedocs/index.html http://www.nlm.nih.gov/research/umls/sourcereleasedocs/index.html UMLS online tutorials ◦ http://www.nlm.nih.gov/research/umls/user_education/index.html http://www.nlm.nih.gov/research/umls/user_education/index.html 68

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

70 References: RxNorm RxNorm home page ◦ Content ◦ http://www.nlm.nih.gov/research/umls/ http://www.nlm.nih.gov/research/umls/ RxNav home page ◦ Browser + APIs ◦ http://rxnav.nlm.nih.gov/ http://rxnav.nlm.nih.gov/ 70


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