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Analytics and Quality in Medical Encoding Systems

  • John PuentesEmail author
  • Laurent Lecornu
  • Clara Le Guillou
  • Jean-Michel Cauvin
Chapter
Part of the Information Fusion and Data Science book series (IFDS)

Abstract

Medical practice support intends to provide important complementary information for diagnosis by preprocessing voluminous data available on separate, distributed, commonly noninteroperable applications of complex existing medical information systems. Such technology is being investigated to support medical encoding, which manually identifies groups of patients with equivalent diagnosis to determine healthcare expenses, billing, and reimbursement. Medical encoding is expensive, takes considerable time, and depends on multiple scattered and heterogeneous data sources. This chapter summarizes some relevant approaches and findings that illustrate how the considerations of information quality and analytics technologies may enable to improve medical practice. Essential components of a conceived medical encoding support system are described, followed by the associated data analysis, information fusion, and information quality measurement. Results show that it is possible to process, generate, and qualify pertinent medical encoding information in this manner, meeting physicians’ requirements, making use of data available in existing systems and clinical workflows.

Keywords

Computerized decision aid Medical encoding support Information analysis Information fusion Information quality 

References

  1. 1.
    P. Cheng, A. Gilchrist, K.M. Robinson, L. Paul, The risk and consequences of clinical miscoding due to inadequate medical documentation: a case study of the impact on health services funding. Health Inf. Manag. 38(1), 35–46 (2009)Google Scholar
  2. 2.
    C.J. Buck, Step-by-Step Medical Coding (Elsevier Health Sciences, St. Louis, 2016)Google Scholar
  3. 3.
    L. Lecornu, C. Le Guillou, G. Thillay, et al., C2i: a tool to gather medical indexed information. Paper presented at the 9th IEEE international conference on information technology and applications in biomedicine, Larnaca, 5–7 November 2009Google Scholar
  4. 4.
    L. Lecornu, C. Le Guillou, F. Le Saux, et al., ANTEROCOD: actuarial survival curves applied to medical coding support for chronic diseases. Paper presented at the 32nd IEEE International Conference of the Engineering in Medicine and Biology Society, Buenos Aires, 29 August–4 September 2010Google Scholar
  5. 5.
    L. Lecornu, G. Thillay, C. Le Guillou, et al., REFEROCOD: a probabilistic method to medical coding support. Paper presented at the 31st IEEE international conference of the engineering in medicine and biology society, Minneapolis, 3–6 September 2009Google Scholar
  6. 6.
    J. Puentes, J. Montagner, L. Lecornu, J.M. Cauvin, Information quality measurement of medical encoding support based on usability. Comput. Methods Prog. Biomed. 112(3), 329–342 (2013)CrossRefGoogle Scholar
  7. 7.
    World Health Organization, International classification of diseases: ICD-10, vol. I–XXII, 2016, http://apps.who.int/classifications/icd10/browse/2016/en
  8. 8.
    M. Maravic, C. Le Bihan, P. Landais, La classification commune des actes médicaux (CCAM) : de la description à la tarification. Rev. Rhum. 70(9), 785–789 (2003)CrossRefGoogle Scholar
  9. 9.
    L. Lecornu, C. Le Guillou, F. Le Saux, et al., Information fusion for diagnosis coding support. Paper presented at the 33rd IEEE International Conference of the Engineering in Medicine and Biology Society, Boston, 30 August–03 September 2011Google Scholar
  10. 10.
    I. Bloch, Fusion d’informations en traitement du signal et des images (Lavoisier, Hermes Science, Paris, 2003)Google Scholar
  11. 11.
    D. Dubois, H. Prade, Théorie de possibilité, théorie des probabilités et logiques multiple-évaluées: une clarification. Annales des Mathématiques et de l'Intelligence Artificielle 32, 35–66 (2001)CrossRefGoogle Scholar
  12. 12.
    I.G. Todoran, L. Lecornu, A. Khenchaf, J.-M. Le Caillec, A methodology to evaluate important dimensions of information quality in systems. ACM JDIQ. (2015). https://doi.org/10.1145/2744205

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • John Puentes
    • 1
    Email author
  • Laurent Lecornu
    • 1
  • Clara Le Guillou
    • 2
  • Jean-Michel Cauvin
    • 2
  1. 1.IMT Atlantique, Lab-STICC, Technopole Brest Iroise – CS 83818BrestFrance
  2. 2.CHRU Brest, Medical Information Department, DIM - Hôpital de La Cavale Blanche, Boulevard Tanguy PrigentBrestFrance

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