Decision Support Systems in Oncology
| Autor: | Seán 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 |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty Decision support system Computer science Cost-Benefit Analysis Big data Context (language use) Patient advocacy 03 medical and health sciences 0302 clinical medicine Quality of life (healthcare) LUNG-CANCER Internal medicine Neoplasms Patient-Centered Care Health care medicine Biomarkers Tumor Humans STRATEGY Precision Medicine business.industry Patient Selection General Medicine LEARNING HEALTH-CARE Precision medicine Decision Support Systems Clinical TIME ERA 030104 developmental biology Workflow 030220 oncology & carcinogenesis SURVIVAL Quality of Life RADIATION ONCOLOGY business Special Series: Decision Making in Oncology REVIEW ARTICLE Algorithms Software |
| Zdroj: | JCO Clinical Cancer Informatics |
| ISSN: | 2473-4276 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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