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CC BY-NC-ND 4.0 · Appl Clin Inform 2018; 09(03): 667-682
DOI: 10.1055/s-0038-1668090
Research Article
Georg Thieme Verlag KG Stuttgart · New York

SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data

Duwayne L. Willett
1   Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States
2   Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Vaishnavi Kannan
2   Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Ling Chu
1   Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States
2   Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Joel R. Buchanan
3   Department of Medicine, University of Wisconsin School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin, United States
,
Ferdinand T. Velasco
4   Texas Health Resources, Arlington, Texas, United States
5   Southwestern Health Resources, Dallas, Texas, United States
,
John D. Clark
1   Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Jason S. Fish
1   Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States
5   Southwestern Health Resources, Dallas, Texas, United States
,
Adolfo R. Ortuzar
2   Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Josh E. Youngblood
2   Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States
,
Deepa G. Bhat
5   Southwestern Health Resources, Dallas, Texas, United States
,
Mujeeb A. Basit
1   Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States
2   Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States
› Author Affiliations Funding Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award Number UL1TR001105. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Further Information

Publication History

12 February 2018

29 June 2018

Publication Date:
29 August 2018 (online)

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Abstract

Background Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or “grouper.” For constructing value sets, Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems.

Objective This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses.

Methods We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes.

Results A total of 125 condition-defining “standard” SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th–75th percentiles: 1–5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th–75th percentiles: 8–85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th–75th percentiles: 63–976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs.

Conclusion SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, “smarter” EHR feature configuration, and clinical–translational research employing EHR-derived data.

Keywords

SNOMED CT - ICD-10 - electronic health records and systems - disease management - health information exchanges - registries - translational research

Protection of Human and Animal Subjects

Creation of specialty registries at UT Southwestern was performed for quality improvement purposes and deemed exempt from institutional review board (IRB) review by UT Southwestern's IRB. Analysis of reuse and complexity of diagnosis groupers in the EHR did not involve human or animal subjects, and did not require IRB review.


Supplementary Material

 

  • References

  • 1 Motheral BR, Fairman KA. The use of claims databases for outcomes research: rationale, challenges, and strategies. Clin Ther 1997; 19 (02) 346-366
    CrossrefPubMedGoogle Scholar
  • 2 World Health Organization. WHO | ICD-11 Revision – International Classification of Diseases; 2017 [6/4/2017]. Available at: http://www.who.int/classifications/icd/revision/en/ . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 3 Benson T, Grieve G. Principles of Health Interoperability: SNOMED CT, HL7, and FHIR. 3rd ed. London: Springer; 2016. . xxix, 451 p
    Google Scholar
  • 4 Moriyama IM, Loy RM, Robb-Smith AHT, Rosenberg HM, Hoyert DL. , National Center for Health Statistics (U.S.). History of the Statistical Classification of Diseases and Causes of Death. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics; 2011. Available at: http://www.cdc.gov/nchs/data/misc/classification_diseases2011.pdf . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 5 National Library of Medicine (NLM) NLoM. Value Set Authority Center (VSAC): National Library of Medicine (NLM); [updated August 02, 2017]. Available at: https://vsac.nlm.nih.gov/ . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 6 Bowman S. Coordinating SNOMED-CT and ICD-10. J AHIMA 2005; 76 (07) 60-61
    PubMedGoogle Scholar
  • 7 HL7. HL7 Meaningful Use NPRM (Notice of Proposed Rule Making) comments – Final [Letter]; 2010 [updated 3/15/2010]. Available at: https://www.hl7.org/documentcenter/public_temp_85634A36-1C23-BA17-0C77886C56EADBE4/newsroom/HL7%20Meaningful%20Use%20NPRM%20comments%20FINAL.pdf . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 8 Winnenburg R, Bodenreider O. Metrics for assessing the quality of value sets in clinical quality measures. AMIA Annu Symp Proc 2013; 2013: 1497-1505
    PubMedGoogle Scholar
  • 9 Morley KI, Wallace J, Denaxas SC. , et al. Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation. PLoS One 2014; 9 (11) e110900
    CrossrefPubMedGoogle Scholar
  • 10 Cornet R, de Keizer N. Forty years of SNOMED: a literature review. BMC Med Inform Decis Mak 2008; 8 (Suppl. 01) S2
    CrossrefPubMedGoogle Scholar
  • 11 SNOMED_International. SNOMED CT United States Edition. Bethesda, MD: U.S. National Library of Medicine; 2017. Available at: https://www.nlm.nih.gov/healthit/snomedct/us_edition.html . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 12 Højen AR, Sundvall E, Gøeg KR. Methods and applications for visualization of SNOMED CT concept sets. Appl Clin Inform 2014; 5 (01) 127-152
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 4. SNOMED CT Basics: SNOMED International; 2017. Available at: https://confluence.ihtsdotools.org/display/DOCSTART/4.+SNOMED+CT+Basics . Accessed July 18, 2018
    PubMed
  • 14 Randorff Højen A, Rosenbeck Gøeg K. SNOMED CT implementation. Mapping guidelines facilitating reuse of data. Methods Inf Med 2012; 51 (06) 529-538
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 6. SNOMED CT Concept Model: SNOMED International; 2017. Available at: https://confluence.ihtsdotools.org/display/DOCSTART/6.+SNOMED+CT+Concept+Model . Accessed July 18, 2018
    PubMed
  • 16 CMS.gov. 2018 ICD-10-CM. Centers for Medicare and Medicaid Services; 2017 [updated 08/11/2017]. Available at: https://www.cms.gov/Medicare/Coding/ICD10/2018-ICD-10-CM-and-GEMs.html . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 17 Mohd Sulaiman I, Karlsson D, Koch S. Mapping acute coronary syndrome registries to SNOMED CT: a comparative study between Malaysia and Sweden. Methods Inf Med 2017; 56 (04) 330-338
    Article in Thieme ConnectPubMedGoogle Scholar
  • 18 Kannan V, Fish JS, Mutz JM. , et al. Rapid development of specialty population registries and quality measures from electronic health record data: an agile framework. Methods Inf Med 2017; 56 (99) e74-e83
    Article in Thieme ConnectPubMedGoogle Scholar
  • 19 D'Amore JD, Sittig DF, Wright A, Iyengar MS, Ness RB. The promise of the CCD: challenges and opportunity for quality improvement and population health. AMIA Annu Symp Proc 2011; 2011: 285-294
    PubMedGoogle Scholar
  • 20 Richesson RL, Green BB, Laws R. , et al. Pragmatic (trial) informatics: a perspective from the NIH Health Care Systems Research Collaboratory. J Am Med Inform Assoc 2017; 24 (05) 996-1001
    CrossrefPubMedGoogle Scholar
  • 21 Gottesman O, Kuivaniemi H, Tromp G. , et al; eMERGE Network. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med 2013; 15 (10) 761-771
    CrossrefPubMedGoogle Scholar
  • 22 Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV, Brown JS. Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc 2014; 21 (04) 578-582
    CrossrefPubMedGoogle Scholar
  • 23 Murphy SN, Weber G, Mendis M. , et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc 2010; 17 (02) 124-130
    CrossrefPubMedGoogle Scholar
  • 24 FitzHenry F, Resnic FS, Robbins SL. , et al. Creating a common data model for comparative effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 6 (03) 536-547
    Article in Thieme ConnectPubMedGoogle Scholar
  • 25 Richesson RL, Smerek MM, Blake Cameron C. A framework to support the sharing and reuse of computable phenotype definitions across health care delivery and clinical research applications. EGEMS (Wash DC) 2016; 4 (03) 1232
    PubMedGoogle Scholar
  • 26 Leavy MB. Registries for Evaluating Patient Outcomes: A User's Guide. In: Gliklich RE, Dreyer NA, Leavy MB. , eds. Registries for Evaluating Patient Outcomes: A User's Guide. Rockville, MD: Agency for Healthcare Research and Quality (U.S.); 2014
    Google Scholar
  • 27 SNOMED International SNOMED CT Browser. SNOMED International; 2017. Available at: http://browser.ihtsdotools.org/ . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 28 Lenfant C. Shattuck lecture--clinical research to clinical practice--lost in translation?. N Engl J Med 2003; 349 (09) 868-874
    CrossrefPubMedGoogle Scholar
  • 29 Avorn J. Transforming trial results into practice change: the final translational hurdle: comment on “Impact of the ALLHAT/JNC7 Dissemination Project on thiazide-type diuretic use”. Arch Intern Med 2010; 170 (10) 858-860
    CrossrefPubMedGoogle Scholar
  • 30 Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med 2010; 2 (57) 57cm29
    CrossrefPubMedGoogle Scholar
  • 31 Boxwala AA, Rocha BH, Maviglia S. , et al. A multi-layered framework for disseminating knowledge for computer-based decision support. J Am Med Inform Assoc 2011; 18 (Suppl. 01) i132-i139
    CrossrefPubMedGoogle Scholar
  • 32 Hooks CDS. HL7 and Boston Children's Hospital; 2018. Available at: http://cds-hooks.org/ . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 33 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010; 363 (06) 501-504
    CrossrefPubMedGoogle Scholar
  • 34 Mennemeyer ST, Menachemi N, Rahurkar S, Ford EW. Impact of the HITECH Act on physicians' adoption of electronic health records. J Am Med Inform Assoc 2016; 23 (02) 375-379
    CrossrefPubMedGoogle Scholar
  • 35 Kruse CS, Kothman K, Anerobi K, Abanaka L. Adoption factors of the electronic health record: a systematic review. JMIR Med Inform 2016; 4 (02) e19 . Doi: 10.2196/medinform.5525
    CrossrefPubMedGoogle Scholar
  • 36 Hsiao CJ, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001-2013. NCHS Data Brief 2014; (143) 1-8
    PubMedGoogle Scholar
  • 37 Meigs SL, Solomon M. Electronic health record use a bitter pill for many physicians. Perspect Health Inf Manag 2016; 13: 1d
    PubMedGoogle Scholar
  • 38 Abramson EL, Patel V, Pfoh ER, Kaushal R. How physician perspectives on e-prescribing evolve over time. A case study following the transition between EHRs in an outpatient clinic. Appl Clin Inform 2016; 7 (04) 994-1006
    Article in Thieme ConnectPubMedGoogle Scholar
  • 39 Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R. ; with the HITEC Investigators. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak 2017; 17 (01) 36
    CrossrefPubMedGoogle Scholar
  • 40 McCoy AB, Waitman LR, Lewis JB. , et al. A framework for evaluating the appropriateness of clinical decision support alerts and responses. J Am Med Inform Assoc 2012; 19 (03) 346-352
    CrossrefPubMedGoogle Scholar
  • 41 Buchanan J. Accelerating the benefits of the problem oriented medical record. Appl Clin Inform 2017; 8 (01) 180-190
    Article in Thieme ConnectPubMedGoogle Scholar
  • 42 Harry E, Pierce RG, Kneeland P, Huang G, Stein J, Weller J. Cognitive load and its implications for health care [Web page]. NEJM Catalyst: NEJM; 2018 [updated March 14, 2018]. Available at: https://catalyst.nejm.org/cognitive-load-theory-implications-health-care/ . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 43 Maddox TM, Albert NM, Borden WB. , et al; American Heart Association Council on Quality of Care and Outcomes Research; Council on Cardiovascular Disease in the Young; Council on Clinical Cardiology; Council on Functional Genomics and Translational Biology; and Stroke Council. The learning healthcare system and cardiovascular care: a scientific statement from the American Heart Association. Circulation 2017; 135 (14) e826-e857
    CrossrefPubMedGoogle Scholar
  • 44 Adamusiak T, Shimoyama N, Shimoyama M. Next generation phenotyping using the unified medical language system. JMIR Med Inform 2014; 2 (01) e5
    CrossrefPubMedGoogle Scholar
  • 45 Reich C, Ryan PB, Stang PE, Rocca M. Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases. J Biomed Inform 2012; 45 (04) 689-696
    CrossrefPubMedGoogle Scholar
  • 46 Rodrigues J-M, Robinson D, Della Mead V. , et al , eds. Semantic Alignment between ICD-11 and SNOMED CT. MEDINFO 2015: eHealth-Enabled Health; 2015
    Google Scholar
  • 47 7. SNOMED CT Expressions SNOMED International; 2017. Available at: https://confluence.ihtsdotools.org/display/DOCSTART/7.+SNOMED+CT+Expressions . Accessed July 18, 2018
    PubMed
  • 48 Halpern Y, Horng S, Choi Y, Sontag D. Electronic medical record phenotyping using the anchor and learn framework. J Am Med Inform Assoc 2016; 23 (04) 731-740
    CrossrefPubMedGoogle Scholar
  • 49 Hoffman JM, Dunnenberger HM, Kevin Hicks J. , et al. Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC). J Am Med Inform Assoc 2016; 23 (04) 796-801
    CrossrefPubMedGoogle Scholar
  • 50 Wright A, Pang J, Feblowitz JC. , et al. Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial. J Am Med Inform Assoc 2012; 19 (04) 555-561
    CrossrefPubMedGoogle Scholar
  • 51 McEvoy D, Gandhi TK, Turchin A, Wright A. Enhancing problem list documentation in electronic health records using two methods: the example of prior splenectomy. BMJ Qual Saf 2018; 27 (01) 40-47
    CrossrefPubMedGoogle Scholar
  • 52 Falck S, Adimadhyam S, Meltzer DO, Walton SM, Galanter WL. A trial of indication based prescribing of antihypertensive medications during computerized order entry to improve problem list documentation. Int J Med Inform 2013; 82 (10) 996-1003
    CrossrefPubMedGoogle Scholar
  • 53 Weed LL. Medical records that guide and teach. N Engl J Med 1968; 278 (11) 593-600
    CrossrefPubMedGoogle Scholar
  • 54 Khatipov E, Madden M, Chiang P. , et al. Creating, maintaining and publishing value sets in the VSAC. AMIA Annu Symp Proc 2014; 20 14: 1459
    PubMedGoogle Scholar
  • 55 Fung KW. How SNOMED CT can help in the ICD-10-CM transition [PowerPoint]. NLM; 2012. Available at: https://www.nlm.nih.gov/research/umls/mapping_projects/SNOMED_ICD10_KWFung.pptx . Accessed July 18, 2018
    PubMedGoogle Scholar
  • 56 5. SNOMED CT Logical Model: SNOMED International; 2017. Available at: https://confluence.ihtsdotools.org/display/DOCSTART/5.+SNOMED+CT+Logical+Model . Accessed July 18, 2018
    PubMedGoogle Scholar