KI - Künstliche Intelligenz

, Volume 29, Issue 2, pp 131–141 | Cite as

Technology Roadmap Development for Big Data Healthcare Applications

Technical Contribution

Abstract

Big data applications indicate a wide range of opportunities to improve the overall quality and efficiency of healthcare delivery. The highest impact of big data applications is expected when data from various healthcare areas, such as clinical, administrative, financial, or outcome data, can be integrated. However, as of today, the realization of big data healthcare applications aggregating various kinds of data sources is still lacking behind. In order to foster the implementation of comprehensive big data applications, a clear understanding of short-term and long-term goals of envisioned big data scenarios is needed to forecast which emerging big data technologies are needed at what point in time. The contribution of this paper is to introduce the development of a technology roadmap for big data technologies in the healthcare domain. Beside the description of user needs and the technologies needed in order to satisfy those needs, the technology roadmap provides a basis to forecast technology developments and, thus, guidance in planning and coordinating technology developments accordingly.

Keywords

Big data User needs Requirements analysis Technology roadmap 

Notes

Acknowledgments

This research has been supported in part by the Big Data Public Private Forum, a project that is co-funded by the European Commission within the 7th Framework Programme under the Grant number 318062. The responsibility lies with the authors.

References

  1. 1.
    Accenture (2012) Connected health: the drive to integrated healthcare delivery. http://www.acccenture.com/connectedhealthstudy
  2. 2.
    Attenberg J, Ipeirotis PG, Provost F (2011) Beat the machine: challenging workers to find the unknown unknowns. In: Proceedings of the AAAI Human Computation Workshop. San FranciscoGoogle Scholar
  3. 3.
    Bretschneider C, Zillner S, Hammon M (2013) Grammar-based Lexicon enhancement for aligning German radiology text and images. In: Proceedings of the Recent Advances in Natural Language Processing (RANLP 2013). Hissar, BulgariaGoogle Scholar
  4. 4.
    Bucko AD, Hunt BJ, Kidd SL, Hom R (2002) Randomized, double-blind, multicenter comparison of oral cefditoren 200 or 400 mg BID with either cefuroxime 250 mg BID or cefadroxil 500 mg BID for the treatment of uncomplicated skin and skin-structure infections. Clin Ther 24:1134–1147CrossRefGoogle Scholar
  5. 5.
    Channin D, Mongkolwat P, Kleper V, Sepukar K, Rubin D (2009) The cabib annotation and image markup project. In: Journal of digital imagingGoogle Scholar
  6. 6.
    Cloud Security Alliance (2012) Top ten big data security and privacy challenges. http://www.isaca.org/Groups/Professional-English/big-data/GroupDocuments/Big_Data_Top_Ten_v1.pdf
  7. 7.
    CMS (Center for Medicare & Medicaid services) (2014) Medicare & medicaid EHR incentive programs. HIT Plociy CommitteeGoogle Scholar
  8. 8.
    Cornet R (2009) Definitions and qualifiers in SNOMED CT. Methods Inf Med 48(2):178–183. doi: 10.3414/ME9215 Google Scholar
  9. 9.
    El Emam K, Dankar FK (2008) Protecting privacy using k-anonymity. J Am Med Inf AssocGoogle Scholar
  10. 10.
    El Emam K et al (2014) De-identification methods for open health data: the case of the heritage health prize claims dataset. J Med Internet Res 14(1):627–637Google Scholar
  11. 11.
    Fan JW, Friedman C (2011) Deriving a probabilistic syntacto-semantic grammar for biomedicine based on domain-specific terminologies. J Biomed Inform 44(5):805–814CrossRefGoogle Scholar
  12. 12.
    Feulner J, Zhou SK, Seifert S, Cavallaro A, Hornegger JM, Comaniciu D (2009) Estimating the body portion of CT volumes by matching histograms of visual words. In: Proceedings of SPIE Medical ImagingGoogle Scholar
  13. 13.
    Flick U (2011) Triangulation: Eine Einführung. VS Verlag, WiesbadenCrossRefGoogle Scholar
  14. 14.
    FP7 BIG European Parliament and the Council of the European Union (1995) Directive 95/46/EC of the European Parliament and of the Council 1995. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:31995L0046:en:HTML
  15. 15.
    FP7 BIG International Organization for Standardization (2008) ISO/TS 25237:2008 Health informatics—pseudonymization, 1 edn. GenevaGoogle Scholar
  16. 16.
    Friedman C, Alderson PO, Austin JH, Cimino J, Johnson SB (1994) A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1:161–174CrossRefGoogle Scholar
  17. 17.
    Friedman C, Kra P, Rzhetksy A (2002) Two biomedical sublanguages: a description based on the theories of Zellig Harris. J Biomed Inform 35:222–235CrossRefGoogle Scholar
  18. 18.
    Frost and Sullivan (2012) US Hospital Health Data Analytics Market [Internet]Google Scholar
  19. 19.
    Göbel G (2013) Big Sector Healthcare Expert- Interview with Prof Georg Göbel, 8.8.2013Google Scholar
  20. 20.
    Health Consumer Powerhouse (2009) Euro Health Consumer Index 2009 (online)Google Scholar
  21. 21.
    Kayyali B, Knott D, Van Kuiken S (2013) The ‘big data’ revolution in healthcare. McKinsey & CompanyGoogle Scholar
  22. 22.
    Korster P, Seider C (2010) The world’s 4 trillion dollar challenge. Executive Report of IBM Global Business ServicesGoogle Scholar
  23. 23.
    Lobillo F, et al (2014) D2.4.2.Final Version of Sector’s Roadmap. Public Deliverable of the EU-Project BIGGoogle Scholar
  24. 24.
    Lünendonk Company (2013) Big Data within health insurances: mastering data in a changing health care system. Trend reportGoogle Scholar
  25. 25.
    Markwell D, Sato L, Cheetham E (2008) Representing clinical information using SNOMED Clinical Terms with different structural information models. In: Spackman K, Cornet R (eds) Proceedings of the 3rd international conference on Knowledge Representation in Medicine (KR-MED 2008)Google Scholar
  26. 26.
    Martínez-Costa C and Schulz S (2013) Ontology-based reinterpretation of the SNOMED CT context model. In: Proceedings of the International Conference on Biomedical Ontology. pp 1–6Google Scholar
  27. 27.
    McKinsey & Company (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey and CompanyGoogle Scholar
  28. 28.
    Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF (2008) Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 24(11):128–144Google Scholar
  29. 29.
    Neubauer T, Kolb M (2009) An evaluation of technologies for the pseudonymization of medical data. In: Lee R, Hu G, Miao H (eds) Computer and Information Science, SCI 208. Springer, New YorkGoogle Scholar
  30. 30.
    Oberkampf H, Zillner S, Bauer B, Hammon M (2013) An OGMS-based model for clinical information (MCI). In: Proceedings of the International Conference on Biomedical Ontology. Montreal, CanadaGoogle Scholar
  31. 31.
    Porter M, Teisberg OE (2006) Redefining health care: creating value-based competition on results. Harvard Business Review Press, BostonGoogle Scholar
  32. 32.
    Rector A, Brandt S, Schneider T (2011) Getting the foot out of the pelvis: modeling problems affecting use of SNOMED CT hierarchies in practical applications. J Am Med Inform Assoc 18(4):432–440. doi: 10.1136/amiajnl-2010-000045 CrossRefGoogle Scholar
  33. 33.
    Rubin D, Mongkolwat P, Kleper V, Supekar K, Channin D (2008) Medical imaging on the semantic web: annotation and image markup. In: AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration. StanfordGoogle Scholar
  34. 34.
    Sanders T, Bowens F, Pierce W, Stasher-Booker B, Thompson E, Jones W (2012) The Road to ICD-10-CM/PCS Implementation: forecasting the transition for providers, payers, and other healthcare organizations. Perspect Health Inf ManagGoogle Scholar
  35. 35.
    Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG (2010) Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 17(5):507–513CrossRefGoogle Scholar
  36. 36.
    Seifert S, Barbu A, Zhou K, Liu D, Feulner J, Huber M, Suehling M, Cavallaro A, Comaniciu D (2009) Hierarchical parsing and semantic navigation of full body CT data. In: Proceedings of SPIE Medical ImagingGoogle Scholar
  37. 37.
    Seifert S, Kelm M, Möller M, Mukherjee S, Cavallaro A, Huber M, Comaniciu D (2010) Semantic annotation of medical images. In: Proceedings of SPIE medical imagingGoogle Scholar
  38. 38.
    Soderland N, Kent J, Lawyer P, Larsson S (2012) Progress towards value-based health care. Lessons from 12 Countries. The Boston Consulting Group, IncGoogle Scholar
  39. 39.
    Wiggins D, Otterbach G (2013) Big sector forum health interview with D. Wiggins and G.Otterbach (Company Teradata). Accessed 26 Feb 2013Google Scholar
  40. 40.
    Zillner S, Lasierra N, Faix W, Neururer S (2014a) User needs and requirements analysis for big data healthcare applications. In: Proceeding of the 25th European Medical Informatics Conference (MIE 2014). Istanbul, TurkeyGoogle Scholar
  41. 41.
    Zillner S, et al (2014b) D 2.3.1 Final Version of Sector’s Requisites. Public Deliverable of the EU-Project BIG (318062; ICT-2011.4.4)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Corporate Technology, Research and Technology CenterSiemens AGMunichGermany
  2. 2.School of International Business and EntrepreneurshipSteinbeis UniversityBerlinGermany
  3. 3.Semantic Technology Institute InnsbruckUniversity of InnsbruckInnsbruckAustria
  4. 4.Department of Medical Statistics, Informatics and Health EconomicsMedical University of InnsbruckInnsbruckAustria

Personalised recommendations