CC BY 4.0 · ACI open 2019; 03(01): e44-e62
DOI: 10.1055/s-0039-1688936
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes

Fabrizio Pecoraro
1   National Research Council, Institute for Research on Population and Social Policies, Rome, Italy
,
Daniela Luzi
1   National Research Council, Institute for Research on Population and Social Policies, Rome, Italy
,
Fabrizio L. Ricci
1   National Research Council, Institute for Research on Population and Social Policies, Rome, Italy
› Author Affiliations
Funding None.
Further Information

Publication History

01 August 2018

11 April 2019

Publication Date:
14 June 2019 (online)

Abstract

Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems.

Objective This article aims to design and implement a conceptual framework that semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse.

Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach.

Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse.

Authors' Contributions

All authors contributed in the conception of the study as well as in the design of the conceptual framework that was subsequently implemented by F.P. All authors contributed equally in drafting, critically revising, and writing the final version of the article.


 
  • References

  • 1 Safran C, Bloomrosen M, Hammond WE. , et al; Expert Panel. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inform Assoc 2007; 14 (01) 1-9
  • 2 Rea S, Pathak J, Savova G. , et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project. J Biomed Inform 2012; 45 (04) 763-771
  • 3 Danciu I, Cowan JD, Basford M. , et al. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform 2014; 52: 28-35
  • 4 Diomidous M, Zimeras S, Mantas J. Spatial electronic health record for the epidemiological clinical data. Travel Health Informat Telehealth 2009; 1: 66-72
  • 5 Abhyankar S, Demner-Fushman D, McDonald CJ. Standardizing clinical laboratory data for secondary use. J Biomed Inform 2012; 45 (04) 642-650
  • 6 Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009; 16 (03) 328-337
  • 7 Roque FS, Jensen PB, Schmock H. , et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLOS Comput Biol 2011; 7 (08) e1002141
  • 8 Lurio J, Morrison FP, Pichardo M. , et al. Using electronic health record alerts to provide public health situational awareness to clinicians. J Am Med Inform Assoc 2010; 17 (02) 217-219
  • 9 Zhou X, Chen S, Liu B. , et al. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif Intell Med 2010; 48 (2-3): 139-152
  • 10 Sahama TR, Croll PR. A data warehouse architecture for clinical data warehousing. Proceedings of the fifth Australasian Symposium on ACSW Frontiers, Ballarat, Victoria, Australia; 2007: 227-232
  • 11 de Mul M, Alons P, van der Velde P, Konings I, Bakker J, Hazelzet J. Development of a clinical data warehouse from an intensive care clinical information system. Comput Methods Programs Biomed 2012; 105 (01) 22-30
  • 12 Stow PJ, Hart GK, Higlett T. , et al; ANZICS Database Management Committee. Development and implementation of a high-quality clinical database: the Australian and New Zealand Intensive Care Society Adult Patient Database. J Crit Care 2006; 21 (02) 133-141
  • 13 Roelofs E, Persoon L, Nijsten S, Wiessler W, Dekker A, Lambin P. Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial. Radiother Oncol 2013; 108 (01) 174-179
  • 14 Pecoraro F, Luzi D, Ricci FL. Secondary uses of EHR systems: a feasibility study. Proceedings of IEEE International Conference on E-Health and Bioengineering (EHB), Iasi, Romania; 2013: 1-6
  • 15 Selby JV, Krumholz HM, Kuntz RE, Collins FS. Network news: powering clinical research. Sci Transl Med 2013; 5 (182) 182fs13
  • 16 Casey JA, Schwartz BS, Stewart WF, Adler NE. Using electronic health records for population health research: a review of methods and applications. Annu Rev Public Health 2016; 37: 61-81
  • 17 Prokosch HU, Ganslandt T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf Med 2009; 48 (01) 38-44
  • 18 Hu H, Correll M, Kvecher L. , et al. DW4TR: a data warehouse for translational research. J Biomed Inform 2011; 44 (06) 1004-1019
  • 19 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 (7494): 765
  • 20 Schubart JR, Einbinder JS. Evaluation of a data warehouse in an academic health sciences center. Int J Med Inform 2000; 60 (03) 319-333
  • 21 Rubin DL, Desser TS. A data warehouse for integrating radiologic and pathologic data. J Am Coll Radiol 2008; 5 (03) 210-217
  • 22 Watson HJ, Goodhue DL, Wixom BH. The benefits of data warehousing: why some organizations realize exceptional payoffs. Inf Manage 2002; 39: 491-502
  • 23 Serbanati LD, Contenti M, Mercurio G, Ricci FL. LUMIR: a region-wide virtual longitudinal EHR. Proceedings of the 9th International HL7 Interoperability Conference (IHIC), Crete, Greece; 2008
  • 24 Roth MT, Schwarz P. Don't scrap it, wrap it! A wrapper architecture for legacy data sources. Proceeding of the Conference on Very Large Data Bases (VLDB), Athens, Greece; 1997: 266-275
  • 25 HL7 Implementation Guide; CDA Release 2–Continuity of Care Document (CCD). Ann Arbor, MI: Health Level Seven, Inc.; 2007
  • 26 Lupse O, Vida O, Stoicu-Tivadar L, Stoicu-Tivadar V. Using HL7 CDA and CCD standards to improve communication between healthcare information systems. IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY); 2011: 453-457
  • 27 Kimball R, Ross M. The Data Warehouse Toolkit Second Edition: The Complete Guide to Dimensional Modelling. New York: Wiley Computing Publishing; 2002
  • 28 Schadow G, Mead CN, Walker DM. The HL7 Reference Information Model under scrutiny. Stud Health Technol Inform 2006; 124: 151-156
  • 29 Luzi D, Pecoraro F, Ricci FL, Mercurio G. A medical device Domain Analysis Model based on HL7 Reference Information Model. Proceeding of Medical Informatics in a United and Healthy Europe (MIE); 2009: 162-166
  • 30 Inmon WH. Building the Data Warehouse. 4th ed. New York: John Wiley & Sons; 2005
  • 31 Inmon WH, Zachman JA, Geiger JG. Data Stores, Data Warehousing, and the Zachman Framework: Managing Enterprise Knowledge. New York: McGraw-Hill; 1997
  • 32 Eggebraaten TJ, Tenner JW, Dubbels JC. A health-care data model based on the HL7 Reference Information Model. IBM J Res Develop 2006; 46: 5-18
  • 33 Hümmer W, Bauer A, Harde G. XCube: XML for data warehouses. Proceedings of the 6th ACM International Workshop on Data Warehousing and OLAP; 2003: 33-40
  • 34 Boussaid O, Messaoud RB, Choquet R, Anthoard S. X-warehousing: an XML-based approach for warehousing complex data. Advances in Databases and Information Systems. Berlin, Germany: Springer; 2006: 39-54
  • 35 Park BK, Han H, Song IY. XML-OLAP: a multidimensional analysis framework for XML warehouses. Data Warehousing and Knowledge Discovery. Berlin, Germany: Springer; 2005: 32-42
  • 36 Mahboubi H, Ralaivao JC, Loudcher S, Boussaïd O, Bentayeb F. X-wacoda: an XML-based approach for warehousing and analyzing complex data. Data Warehousing Design and Advanced Engineering Applications: Methods for Complex Construction. Pennsylvania, United States: IGI Global; 2009: 38-54
  • 37 Metller T, Rohner P. Supplier relationship management: a case study in the context of health care. J Theor Appl Electron Commer Res 2009; 4: 58-71
  • 38 Committee on Data Standards for Patient Safety. Key Capabilities of an Electronic Health Record System. Institute of Medicine Report, 5; 2003
  • 39 Kimball R, Caserta J. The Data Warehouse ETL Toolkit. Indianapolis: Wiley; 2006
  • 40 Serbanati LD, Ricci FL. EHR-centric integration of Health Information Systems. E-Health and Bioengineering Conference (EHB). Iaşi, Romania 2013; 1-4