Health Level Seven Interoperability Strategy: Big Data, Incrementally Structured

Summary

Objectives: Describe how the HL7 Clinical Document Architecture (CDA), a foundational standard in US Meaningful Use, contributes to a “big data, incrementally structured” interoperability strategy, whereby data structured incrementally gets large amounts of data flowing faster. We present cases showing how this approach is leveraged for big data analysis.

Methods: To support the assertion that semi-structured narrative in CDA format can be a useful adjunct in an overall big data analytic approach, we present two case studies. The first assesses an organization’s ability to generate clinical quality reports using coded data alone vs. coded data supplemented by CDA narrative. The second leverages CDA to construct a network model for referral management, from which additional observations can be gleaned.

Results: The first case shows that coded data supplemented by CDA narrative resulted in significant variances in calculated performance scores. In the second case, we found that the constructed network model enables the identification of differences in patient characteristics among different referral work flows.

Discussion: The CDA approach goes after data indirectly, by focusing first on the flow of narrative, which is then incrementally structured. A quantitative assessment of whether this approach will lead to a greater flow of data and ultimately a greater flow of structured data vs. other approaches is planned as a future exercise.

Conclusion: Along with growing adoption of CDA, we are now seeing the big data community explore the standard, particularly given its potential to supply analytic engines with volumes of data previously not possible.

Keywords

Big data - CDA - HL7 - Semantic Interoperability

• References

• 1 Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shabo A. HL7 Clinical Docu- ment Architecture, Release 2. J Am Med Inform Assoc 2006; 13: 30-39. http://www.jamia.org/cgi/ content/short/13/1/30.
• 2 Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shabo A. Editors HL7 Clinical Document Architecture, Release 2.0. ANSI-approved HL7 Standard, May 2005. Ann Arbor, MI: Health Level Seven, Inc.; 2005. http://www.hl7.org/implement/standards/product_brief.cfm? product_id=7.
• 3 Department of Health and Human Services, Office of the National Coordinator for Health Information Technology. Health Information Technology: Standards, Implementation Specifications, and Certification Criteria for Electronic Health Record Technology, 2014 Edition; Revisions to the Permanent Certification Program for Health Information Technology (ONC Meaningful Use Stage 2 Final Rule). Sept,. 2012 http://www.gpo.gov/fdsys/pkg/FR-2012-09-04/pdf/2012-20982.pdf.
• 4 Marquard B, Dolin RH, Dolin G. et al. editors HL7 Implementation Guide for CDA® R2: Consolidated CDA (C-CDA), Draft Standard for Trial Use Release 1.1; 2012. http://www.hl7.org/implement/standards/product_brief.cfm?product_id= 258.
• 5 Dolin G, Dolin RH, Gaunt S. et al. editors HL7 Implementation Guide for CDA® R2: Quality Reporting Document Architecture - Category I (QRDA-I), Draft Standard for Trial Use Release 2; 2012. http://www.hl7.org/implement/standards/product_brief.cfm?product_id=35.
• 6 Implementation Guide for CDA® R2: Ambula- tory Healthcare Provider Reporting to Central Cancer Registries, 2012. http://www.cdc.gov/phin/library/guides/Implementation_Guide_for_ Ambulatory_Healthcare_Provider_Reporting_to_ Central_Cancer_Registries_August_2012.pdf.
• 7 Wood L, Dolin RH, Dolin G, Gaunt S. et al. editors HL7 Implementation Guide for CDA® Re-lease 2: Quality Reporting Document Architec-ture - Category III (QRDA-III), Draft Standard for Trial Use Release 1; 2012.. http://www.hl7. org/implement/standards/product_brief.cfm? product_id=286.
• 8 Healthcare Information and Management Systems Society (HIMSS)’ Health Story Project. http:// www.healthstory.com/.
• 9 Fung KW, Jao CS, Demner-Fushman D. Extracting drug indication information from structured product labels using natural language processing. J Am Med Inform Assoc 2013; 20: 482-488.
• 10 Harpaz R, Vilar S, DuMouchel W. et al. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J Am Med Inform Assoc 2013; 20: 413-419.
• 11 LePendu P, Iyer SV, Bauer-Mehren A, Harpaz R, Mortensen JM, Podchiyska T, Ferris TA, Shah NH. Pharmacovigilance Using Clinical Notes. Nature: Clinical Pharmacology & Therapeutics 2013; 93: 547-555.
• 12 Gandhi TK, Zuccotti G, Lee TH. Incomplete care - on the trail of flaws in the system. NEJM 2011; 365 (06) 486-488. PMID: 21830964.
• 13 Apixio White Paper. Big Data Reveals Crucial Hidden Information in Healthcare Systems - Health Technology Use Case. 2013. http:// apixio.com/big-data-reveals-crucial-hidden- information.
• 14 Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959 22. 719-748.