How Should Data Be Shared and Rapid Learning Health Care Promoted?

  • Ruud van Stiphout
  • Erik Roelofs
  • Andre Dekker
  • Philippe Lambin
Chapter

Abstract

The current increasing amount of digitalized medical data in health-care demands for solutions to store, share, mine, and analyze these data. Today, medical knowledge and evidence is based on outdated data. Tomorrow, we aim to have a rapid learning health medicine system in which evidence can be generated instantly, based on the most recent data available. The development of this system requires dedication and support of health-care providers, politicians, and patients on many levels. The aim of this system is improvement of health-care quality and support in clinical decision making. Full integration of data handling systems within the clinic and between institutes is inevitable in the near future.

Keywords

Electronic Health Record Clinical Decision Support Health Information Technology Semantic Interoperability Paper Medical Record 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abernethy AP, Etheredge LM, Ganz PA et al (2010) Rapid-learning system for cancer care. J Clin Oncol 28(27):4268–4274PubMedCrossRefGoogle Scholar
  2. 2.
    Chan KS, Fowles JB, Weiner JP (2010) Electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev 67(5):503–527PubMedCrossRefGoogle Scholar
  3. 3.
    Dehing-Oberije C, Aerts H, Yu S (2010) Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone. Int J Radiat Oncol Biol Phys 81:360–368PubMedCrossRefGoogle Scholar
  4. 4.
    Iasonos A, Schrag D, Raj GV et al (2008) How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 26(8):1364–1370PubMedCrossRefGoogle Scholar
  5. 5.
    Lambin P, Petit SF, Aerts HJ et al (2010) The ESTRO Breur Lecture 2009. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer. Radiother Oncol 96(2):145–152PubMedCrossRefGoogle Scholar
  6. 6.
    Roelofs E et al (2010) Design of and technical challenges involved in a framework for multicentric radiotherapy treatment planning studies. Radiother Oncol 97(3):567–571PubMedCrossRefGoogle Scholar
  7. 7.
    Starmans MH, Zips D, Wouters BG (2009) The use of a comprehensive tumour xenograft dataset to validate gene signatures relevant for radiation response. Radiother Oncol 92:417–422PubMedCrossRefGoogle Scholar
  8. 8.
    Valentini V, van Stiphout RGPM, Lammering G (2011) Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials. J Clin Oncol 29:3163–3172PubMedCrossRefGoogle Scholar
  9. 9.
    van Stiphout RG, Lammering G, Buijsen J et al (2011) Development and external validation of a predictive model for pathological complete response of rectal cancer patients including sequential PET-CT imaging. Radiother Oncol 98(1):126–133PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ruud van Stiphout
    • 1
  • Erik Roelofs
    • 1
  • Andre Dekker
    • 1
  • Philippe Lambin
    • 1
  1. 1.Department of Radiation Oncology (MAASTRO)GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+MaastrichtThe Netherlands

Personalised recommendations