Clinical decision-making and secondary findings in systems medicine
Clinical decision-making and secondary findings in systems medicine
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DOI：
10.1186/s12910-016-0113-5
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发表时间：
2016-05-21
影响因子：
2.7
通讯作者：
Langanke, M.
中科院分区：
文献类型：
--
作者：
Fischer, T.;Brothers, K. B.;Langanke, M.
背景：系统医学是科学策略和实践的集合，包括对人类生物学（特别是系统生物学）的生物信息学方法；“大数据”统计分析；以及医疗信息工具。个性化和精准医疗涉及应用于基因组和病历数据的类似分析方法，而系统医学则利用这些以及其他数据来源。鉴于这一区别，系统医学的临床翻译对致力于产生系统医学知识的研究人员和致力于应用系统医学知识的临床医生提出了许多重要的伦理和认识论挑战。讨论：本文关注三个关键挑战：首先，我们将讨论当致力于实验医学或循证医学原则的医疗保健提供者遇到来自计算机算法的个性化建议时，可能出现的决策冲突。我们将特别探讨对照实验，如比较有效性试验，是否应该调解系统医学的转化，或者通过“大数据”方法产生的个性化发现是否可以直接应用于临床决策。其次，我们将研究利雅得重症监护计划死亡率预测算法的案例，该算法被贬义地称为“死亡计算机”，以展示在临床环境中应用大数据驱动的评分系统时可能出现的伦理挑战。我们认为，不加批判地使用预测性临床算法，包括那些为系统医学设想的算法，挑战了对医患关系的基本理解。第三，我们将以最近关于基因组学和成像的二次发现的论述为基础，提请人们注意从不同来源的数据（包括患者为了实现“量化自我”而记录的数据）的联合分析中得出的二次发现的重要含义。摘要：本文探讨了系统医学转化为临床医学时可能出现的伦理挑战。这些挑战包括临床决策的认识论挑战，使用大数据技术优化的评分系统，以及附带和次要发现的风险将显著增加。虽然一些伦理影响仍然是假设的，但我们应该利用这个机会前瞻性地识别挑战，以避免在系统医学不可避免地进入常规护理时犯可预见的错误。
Background: Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it.Discussion: This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their " quantified self."Summary: This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.