Next-generation phenotyping using UMLS and Meaningful Use ontologies: SNOMED CT, RxNorm, and LOINC
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Next-generation phenotyping using UMLS and Meaningful Use ontologies: SNOMED CT, RxNorm, and LOINC

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SNOMED CT, LOINC, and RxNorm, fuelled by the Meaningful Use legislation, are poised to become the cornerstone of U.S. health information interchange. SNOMED CT is one of the most comprehensive, ...

SNOMED CT, LOINC, and RxNorm, fuelled by the Meaningful Use legislation, are poised to become the cornerstone of U.S. health information interchange. SNOMED CT is one of the most comprehensive, multilingual medical terminologies in the world. LOINC is a universal standard for identifying laboratory observations. RxNorm is a standardized nomenclature for generic and branded drugs. All three are integrated within the Unified Medical Language System (UMLS) maintained by the U.S. National Library of Medicine.

While physicians rarely have to deal with clinical terminologies directly, these are indispensable for data querying, validation and reconciliation. The Clinical Informatics team at the Medical College of Wisconsin has developed ClinMiner (https://clinminer.hmgc.mcw.edu), a clinical research portal for clinical and diagnostic information on patients in genetics clinics and clinical sequencing programs, as well as other clinical research projects. ClinMiner is a larger system that incorporates data entry forms, patient reports, advanced querying, export and data visualization. Data for the system consists of many clinical and referral documents the patients have accumulated throughout their clinic and diagnostic histories, and are standardized through the three Meaningful Use ontologies: SNOMED CT, RxNorm and LOINC; integrated into a single UMLS perspective that allows for seamless and dynamic translation between the annotating sources, as well as provides a consolidated view of the underlying patient data.

This approach is unique in integrating all three terminologies into a single workflow of a clinical application, and in fact is not limited to Meaningful Use, as any terminology integrated within the UMLS can be used to annotate, visualize, and query data. This is of particular significance for reintegrating legacy clinical information, for example, billing data annotated with ICD-9 codes in the process of transitioning to ICD-10. Most importantly, as large resources such as SNOMED CT and the UMLS often remain underused due to their sheer size and complexity, ClinMiner demonstrates that the additional effort is well worth it.

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Next-generation phenotyping using UMLS and Meaningful Use ontologies: SNOMED CT, RxNorm, and LOINC Next-generation phenotyping using UMLS and Meaningful Use ontologies: SNOMED CT, RxNorm, and LOINC Presentation Transcript

  • Next-generation phenotyping using UMLS and Meaningful Use ontologies: SNOMED CT, RxNorm, and LOINC Tomasz Adamusiak MD PhD 7omasz
  • $13 520 965 732.99 Paid by CMS in EHR incentive payments to EPs 2011 – 2013
  • Meanwhile across the pond…
  • It pays to get started early (259,000 providers so far) http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/Beginners_Guide.pdf
  • 2015 will be the defining year in the CMS EHR Incentive Programs https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/EHRIncentProgtimeline508V1.pdf Penalties!
  • Achieving Meaningful Use is “relatively” straightforward •15 CMs •10 MMs •3 Core + 3 Alternate CQMs •38 Additional CQMs Stage 1
  • Each stage will have its own set of requirements Final rule, September 4, 2012
  • Information exchange is at the heart of Meaningful Use Core Measures: 12. Provide patients with an electronic copy of their health information, upon request 13. Provide clinical summaries for patients for each office visit 14. Capability to exchange key clinical information Menu Measure: 8. The EP (…) should provide summary care record for each transition of care or referral https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/EHR_Medicaid_Guide_Remediated_2012.pdf Stage 2
  • “unstructured document” is explicitly prohibited in transition of care Electronic Access Clinical Summaries Information exchange in MU Stage 2 Patients Referring provider Receiving provider Receiving provider
  • Structured Summary of Care Problem list •ICD–9–CM •SNOMED CT 2009 •SNOMED CT 2012 Medications •Any source vocabulary that is included in RxNorm •RxNorm Encounter diagnoses •ICD-10-CM Laboratory tests •LOINC 2.24 •LOINC 2.27 Procedures •ICD-9-CM •HCPCS + CPT-4 •CDT •ICD–10–PCS
  • The era of non-MU ontologies is over Expertise in working with medical and pharmacy coding schemes (ICD9/ICD10, HCPCS, CPT4, hospital revenue codes, LOINC, SNOMED and NDC) Knowledge of clinical terminology and coding standards such as ICD-9, ICD-10, CPT, LOINC, and SNOMED. This individual will be well-versed in Meaningful Use and standard vocabularies (e.g. RxNorm, SNOMED, etc.) Experience with healthcare modeling efforts and terminologies such as HL7 v3, SNOMED, LOINC, FDB, CPT
  • If only there was something to pull all these terminologies together!
  • UMLS – an idea ahead of its time Donald A.B. Lindberg, M.D. C. Tilley and J. Willis, The Unified Medical Language System, What is it and how to use it?
  • Exanthema C0015230 UMLS mappings rash NOS ICD-10:R21 Cutaneous eruption SCT:112625008 Eruption SCT:1806006
  • Exanthema C0015230 UMLS mappings rash NOS ICD-10:R21 Cutaneous eruption SCT:112625008 Eruption SCT:1806006
  • The sheer scale makes manual integration impractical Gene tests HLA tests Evaluation and management Skin tests Patient information HPA tests Everything else Here Be Dragons
  • Ontology-based database, query and reporting system MU Terminologies •SNOMED CT •RxNorm •LOINC •+ Data types •Patient and family information •Demographic information •Laboratory and genetic test results •Clinical measurements •Phenotypes and diseases •Imaging phenotypes •Procedures
  • Data Warehouse EHR Reports Clinical Documents Multiple Data Sources Data Integration in ClinMiner Standardized with Meaningful Use Ontologies Study -> ETL -> Report
  • CCD/ Observ-OM based
  • Phenotypes Medications Labs LOINC RxNorm SNOMED CT UMLS UMLS Integration 2 900 000 concepts First Databank Micromedex MediSpan Gold Standard Multum NDF-RT Anti-infective agent Clinical finding SNOMED CT CONCEPT Pharmaceutical product Disease SNOMED CT 395 000 concepts RxNorm 242 000 concepts Clinical Class LOINC PARTS LOINC Root LOINC CLASSTYPES Laboratory Class Radiology Microbiology LOINC 180 000 concepts Data semantics in a separate UMLS-driven layer
  • MU ready, but also able to reintegrate any data input via UMLS UMLS SNOMED CT RxNorm LOINC SNOMED CT RxNorm LOINC ICD-9 MeSH OMIM CPT ≠
  • B C A E D F G H
  • Oracle Text based google-like search: cystic fibrosis gene carrier
  • Fuzzy/wildcard matching too! cron disease myleoid leukemia Technical note: Oracle Text-based query progression/relaxation with AND/OR/ACCUM/FUZZY operators and inbuilt TF-IDF ranking
  • Medication reconciliation via National Drug Code (NDC) and RxNorm 31722-331-01 100 tablets of Warfarin Sodium 4 MG Camber Pharmaceuticals
  • Data-driven customized ontology perspectives 2:0 5:4 3:2 60-80% reduction in graph sizes
  • Clinical Avatars ClinMiner entity MU source mapping UMLS mapping Term label GENDER F Phenotype None C0015780 Female GENDER M Phenotype None C0024554 Male gender RACE African American Asian Native American Other Pacific Islander Unknown White Phenotype OMB standard C0085756 C1515945 C0078988 C0043157 C0086409 C1513907 C1532697 African American American Indian or Alaska Native Asians Caucasians Hispanic or Latino Native Hawaiian or Other Pacific Islander Unknown racial group HEIGHT ClnicalResult LNC:3137-7 C0365282 Body height Measured WEIGHT ClnicalResult LNC:3141-9 C0365286 Body weight Measured BSA ClnicalResult LNC:3139-3 C0365285 body surface area measured INR ClnicalResult LNC:34714-6 C1369580 INR in Blood by Coagulation assay value SMOKER Y Phenotype SCT:77176002 C0337664 Smoker SMOKER N NormalPhenotype SCT:8392000 C0337672 Non-smoker DVT Y Phenotype SCT:128053003 C0149871 Deep venous thrombosis DVT N NormalPhenotype SCT:413076004 C1446197 No past history of venous thrombosis AMI Y Phenotype SCT:57054005 C0155626 Acute myocardial infarction AMI N NormalPhenotype SCT:301121007 C0577811 Myocardial perfusion normal CYP2C9 GeneticResult LNC:46724-1 C1830800 cyp2c9 gene mutations found [identifier] in blood or tissue by molecular genetics method nominal CYP2C92 GeneticResult LNC:56164-7 C2734139 cyp2c9 gene allele 2 [identifier] in blood by molecular genetics method nominal CYP2C93 GeneticResult LNC:56165-4 C2734141 cyp2c9 gene allele 3 [identifier] in blood by molecular genetics method nominal VKORC1 GeneticResult LNC:50722-8 C1978717 vkorc1 gene mutations found [identifier] in blood or tissue by molecular genetics method nominal VKORC1A GeneticResult LNC:50722-8 C1978717 vkorc1 gene mutations found [identifier] in blood or tissue by molecular genetics method nominal VKORC1G GeneticResult LNC:50722-8 C1978717 vkorc1 gene mutations found [identifier] in blood or tissue by molecular genetics method nominal WARFARIN Medication RxNorm:11289 C0043031 Warfarin Demo and evaluation: 100 000 clinical avatars x 90 days x genotype-guided warfarin dosing
  • Link to Query Builder
  • Query Builder
  • Add term to query
  • Add term to query
  • Add term to query
  • Smart reporting
  • Beyond text search and billing codes
  • Coming soon Cross participant query results •Value limits, negation •Visualization and cohort summary •Ability to add fields to query results – demographics, other measurements or parameters •Export to Excel Single participant results •Short summaries •Phenotypes •Medications •Labs •Genetic tests
  • State of the art ontology development vs. Yahoo! in 1996 Manually-curated ontology Request new terms
  • Public demo https://clinminer.hmgc.mcw.edu Use the link or google clinminer Click on login User: demouser Pass: demouser
  • Acknowledgments Marek Tutaj Stacy Zacher Clinical Avatars Vincent A. Fusaro PhD Peter J. Tonellato PhD Laboratory for Personalized Medicine Harvard Medical School