Decision Support Systems in Oncology

Sean Walsh*, Evelyn E. C. de Jong, Janna E. van Timmeren, Abdalla Ibrahim, Inge Compter, Jurgen Peerlings, Sebastian Sanduleanu, Turkey Refaee, Simon Keek, Ruben T. H. M. Larue, Yvonka van Wijk, Aniek J. G. Even, Arthur Jochems, Mohamed S. Barakat, Ralph T. H. Leijenaar, Philippe Lambin

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

52 Citations (Web of Science)

Abstract

Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available dataclinical, imaging, biologic, genetic, costto produce validated predictive models. DSSs compare the personalized probable outcomestoxicity, tumor control, quality of life, cost effectivenessof various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholdersclinicians, medical directors, medical insurers, patient advocacy groupsand are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology. (c) 2019 by American Society of Clinical Oncology.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalJCO Clinical Cancer Informatics
Volume3
DOIs
Publication statusPublished - 7 Feb 2019

Keywords

  • LEARNING HEALTH-CARE
  • RADIATION ONCOLOGY
  • LUNG-CANCER
  • STRATEGY
  • SURVIVAL
  • TIME
  • ERA

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