The Virtual Enterprise Data Warehouse for Healthcare

James P. McGlothlin, Amar Madugula, Ilija Stojic


Healthcare organizations have access to more data than ever before. Healthcare analytics is a vital tool for healthcare organizations and hospitals to analyze performance, identify opportunities to improve, make informed decisions, and comply with government and payor regulations. However, the field of medicine and the political and regulatory landscape are constantly changing, thus these requirements and opportunities rapidly evolve. The traditional best practice solution for business analytics is to organize and consolidate the data into a dimensional data warehouse for analytics purposes. Due to the size of the data, the number of disparate sources and the volume of analytics needs, the overhead to create and maintain such a data warehouse is becoming prohibitive. In this paper, we introduce a virtual data warehouse solution that combines the design and modelling principles of traditional dimensional modelling with data virtualization and in-memory database architectures to create a system which is more agile, flexible and scalable.


  1. Leventhal, R, 2014. Report: Healthcare Data is Growing Exponentially, Needs Protection. In Healthcare Informatics.
  2. Miliard, M, 2014. Data variety bigger hurdle than volume. In Healthcare IT News.
  3. Sikka, V., Färber, F., Goel, A., Lehner, W., 2013. SAP HANA: the evolution from a modern main-memory data platform to an enterprise application platform. In Very Large Databases.
  4. Stonebraker, M., Abadi, D, Batkin, A. et al, 2005. C-Store: A Column-oriented DBMS. In Very Large Databases.
  5. Kimball, R, 2011. The data warehouse toolkit: the complete guide to dimensional modelling. Wiley Computer Publishing.
  6. Bender, D, 2013. HL7 FHIR:An agile and RESTful approach to healthcare information exchange. In CBMS.
  7. Zikopoulos, P, Eaton, C, 2011. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media.
  8. Ellisman, M. and Peltier, S., 2003, December. Medical data federation: The biomedical informatics research network. In The Grid (Vol. 2).
  9. Bloom, K. and Cms Collaboration, 2014. CMS Use of a Data Federation. In Journal of Physics: Conference Series (Vol. 513, No. 4, p. 042005). IOP Publishing.
  10. Kahn, B.K., Strong, D.M. and Wang, R.Y., 2002. Information quality benchmarks: product and service performance. Communications of the ACM, 45(4), pp.184-192.
  11. Tesch, T. and Levy, A., 2008. Measuring service line success: the new model for benchmarking: the service line model benefits nearly all stakeholders involved in healthcare delivery. But how is its success measured?. Healthcare Financial Management, 62(7), pp.68-75.
  12. Schneider, Polly. "How Do You Measure Success?." In Healthcare Informatics 15.3 (1998): 45-56.
  13. Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), p.1.
  14. Goth, G., 2007. Virtualization: Old technology offers huge new potential. IEEE Distributed Systems Online, 8(2), p.3.
  15. Feldman, B., Martin, E.M. and Skotnes, T., 2012. Big Data in Healthcare Hype and Hope. October 2012. Dr. Bonnie, 360.
  16. Hopkins, B., Cullen, A., Gilpin, M., Evelson, B., Leganza, G. and Cahill, M., 2011. Data virtualization reaches the critical mass. Forrester Report.
  17. Lupse, O.S., Vida, M.M. and Tivadar, L., 2012. Cloud computing and interoperability in healthcare information systems. In The First International Conference on Intelligent Systems and Applications (pp. 81-85).
  18. Koufi, V. and Vassilacopoulos, G., 2008. Context-aware access control for pervasive access to process-based healthcare systems. Studies in health technology and informatics, 136, p.679.
  19. Knaus, W.A., Zimmerman, J.E., Wagner, D.P., Draper, E.A. and Lawrence, D.E., 1981. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Critical care medicine, 9(8), pp.591-597.
  20. Pollack, Murray M., Urs E. Ruttimann, and Pamela R. Getson. "Pediatric risk of mortality (PRISM) score." Critical care medicine 16.11 (1988): 1110-1116.
  21. Jameson, J.L. and Longo, D.L., 2015. Precision medicinepersonalized, problematic, and promising. Obstetrical & Gynecological Survey, 70(10), pp.612-614.

Paper Citation

in Harvard Style

McGlothlin J., Madugula A. and Stojic I. (2017). The Virtual Enterprise Data Warehouse for Healthcare . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 469-476. DOI: 10.5220/0006253004690476

in Bibtex Style

author={James P. McGlothlin and Amar Madugula and Ilija Stojic},
title={The Virtual Enterprise Data Warehouse for Healthcare},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - The Virtual Enterprise Data Warehouse for Healthcare
SN - 978-989-758-213-4
AU - McGlothlin J.
AU - Madugula A.
AU - Stojic I.
PY - 2017
SP - 469
EP - 476
DO - 10.5220/0006253004690476