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

Duwayne L. Willett; Vaishnavi Kannan; Ling Chu; Joel R. Buchanan; Ferdinand T. Velasco; John D. Clark; Jason S. Fish; Adolfo R. Ortuzar; Josh E. Youngblood; Deepa G. Bhat; Mujeeb A. Basit

doi:10.1055/s-0038-1668090

Applied Clinical Informatics, Table of Contents

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

¹Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States

²Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Vaishnavi Kannan

²Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Ling Chu

¹Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States

²Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Joel R. Buchanan

³Department of Medicine, University of Wisconsin School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin, United States

,

Ferdinand T. Velasco

⁴Texas Health Resources, Arlington, Texas, United States

⁵Southwestern Health Resources, Dallas, Texas, United States

,

John D. Clark

¹Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Jason S. Fish

¹Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States

⁵Southwestern Health Resources, Dallas, Texas, United States

,

Adolfo R. Ortuzar

²Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Josh E. Youngblood

²Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States

,

Deepa G. Bhat

⁵Southwestern Health Resources, Dallas, Texas, United States

,

Mujeeb A. Basit

¹Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States

²Health System Information Resources Department, University of Texas Southwestern Medical Center, Dallas, Texas, United States

› Author Affiliations

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

Full Text

References

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
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
3 Benson T, Grieve G. Principles of Health Interoperability: SNOMED CT, HL7, and FHIR. 3rd ed. London: Springer; 2016. . xxix, 451 p
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
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
6 Bowman S. Coordinating SNOMED-CT and ICD-10. J AHIMA 2005; 76 (07) 60-61
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
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
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
10 Cornet R, de Keizer N. Forty years of SNOMED: a literature review. BMC Med Inform Decis Mak 2008; 8 (Suppl. 01) S2
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
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
13 4. SNOMED CT Basics: SNOMED International; 2017. Available at: https://confluence.ihtsdotools.org/display/DOCSTART/4.+SNOMED+CT+Basics . Accessed July 18, 2018
14 Randorff Højen A, Rosenbeck Gøeg K. SNOMED CT implementation. Mapping guidelines facilitating reuse of data. Methods Inf Med 2012; 51 (06) 529-538
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
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
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
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
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
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
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
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
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
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
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
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
27 SNOMED International SNOMED CT Browser. SNOMED International; 2017. Available at: http://browser.ihtsdotools.org/ . Accessed July 18, 2018
28 Lenfant C. Shattuck lecture--clinical research to clinical practice--lost in translation?. N Engl J Med 2003; 349 (09) 868-874
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
30 Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med 2010; 2 (57) 57cm29
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
32 Hooks CDS. HL7 and Boston Children's Hospital; 2018. Available at: http://cds-hooks.org/ . Accessed July 18, 2018
33 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010; 363 (06) 501-504
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
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
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
37 Meigs SL, Solomon M. Electronic health record use a bitter pill for many physicians. Perspect Health Inf Manag 2016; 13: 1d
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
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
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
41 Buchanan J. Accelerating the benefits of the problem oriented medical record. Appl Clin Inform 2017; 8 (01) 180-190
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
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
44 Adamusiak T, Shimoyama N, Shimoyama M. Next generation phenotyping using the unified medical language system. JMIR Med Inform 2014; 2 (01) e5
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
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
47 7. SNOMED CT Expressions SNOMED International; 2017. Available at: https://confluence.ihtsdotools.org/display/DOCSTART/7.+SNOMED+CT+Expressions . Accessed July 18, 2018
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
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
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
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
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
53 Weed LL. Medical records that guide and teach. N Engl J Med 1968; 278 (11) 593-600
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
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
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

Supplementary Material

Supplementary Material