Clinical decision-making and secondary findings in systems medicine

Abstract

BackgroundSystems 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.DiscussionThis 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.”SummaryThis 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.

Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14–16, 2018; Vienna, Austria

“…Recent Advances Systems medicine reconciles heterogeneous approaches, using not only modelling methods of systems biology to obtain information about the aetiology of complex diseases but also integrating big amounts of data from various sources (omics data, data from imaging methods, treatment data, etc.) as well [137][138][139][140]. By committing itself to the paradigm of predictive medicine, ethical challenges remain highly virulent in the approach of systems medicine.…”

“…The problem of so-called Bhealth-related personal responsibility^remains potentially as relevant for systems medicine as it is for the approaches of precision medicine and individualised medicine. Challenges regarding the clinical processing of incidental or secondary findings could arise from the translation of systems medicine into clinical practice [138]. Fig.…”

The DSM-ICD diagnostic approach as an essential bridge between the patient and the “big data”

“…They focus on the use of information technologies in medicine without depending on a detailed understanding of biological mechanisms. 31,32 This approach has been considered to be analogous to Amazon's recommendation engine, which utilizes a huge database of past purchasing behavior to predict which items individuals might want to purchase in the future. 31 It seems that this approach, being a top-down modelling, considers the human body more or less as a "black box", the knowledge of the internal workings of which is rendered abundant vis-àvis the information derived by big data analysis.…”

Introduction to the article collection ‘Translation in healthcare: ethical, legal, and social implications’

“…Careful consideration of the ethical implications of big data for clinical decision-making is addressed by Fischer et al, who identify the epistemological questions that ‘systems medicine’ and the use of bioinformatics tools and algorithms raise for patient care [29]. This theme of unintended consequences is taken up in by Newson et al in their call for greater engagement with the normative questions of ambiguous genomic information.…”