Digital Biobanking and Big Data as a New Research Tool: A Position Paper

Abstract

Big data analytics in medicine is driving significant change, as it offers vital information for improving functions, developing cutting-edge solutions and overcoming inefficiencies. With the right archiving and analysis tools, all players in the healthcare system, from hospitals to patients and from medical personnel to the pharmaceutical industry, can yield numerous benefits. Therefore, to analyze and interpret these analytics effectively, so that they can be useful for the advancement of scientific knowledge, we require information sharing, specific skills, training, integration between all system players, unique infrastructures and security. All these characteristics will make it possible to establish and harmonize real big data biobanks, for which it will be appropriate to consider new forms of governance compared to those traditionally conceived for large-sample biobanks.

Unlocking the potential of big data and AI in medicine: insights from biobanking

“…The ways in which risks are approached in biobanking and the normative arguments regarding how they should, such as future-proofing the governance of biobanks ( 64 ) and adaptive risk governance ( 65 ), suggest biobanking may be helpful in identifying key questions medical AI and health data spaces are facing from informed consent, representation in datasets, to risks associated with data protection and responsibility. While acknowledging the digital divide and its consequences, the increased ability of participants to follow and engage with biobanking and healthcare infrastructures are leading to reconfigurations of “traditional boundaries between the public domain (healthcare systems, medical research, and clinical practice) and the private one (patients and citizens)” which necessitate new approaches to fostering trust ( 63 ). Health data spaces bring such observations to a new level.…”

“…Not only are data not always perfect due to inherent finite categorization of potentially infinite diversity, but their capacity to represent should always be continuously problematized . Against the biobanking professionals’ concerns, the tendency to see biobanks as data repositories and medicine as increasingly digital ( 27 , 63 ) can result in a false sense of security in the imaginary of increasing data interoperability and connectedness at the peril of ignoring what D’Ignazio and Klein ( 77 ) rightly note the existence of “problems that cannot be represented—or addressed—by data alone” (p. 10). Risks accompany the opportunities in a datafied world.…”

“…There are efforts such as workshops or collections of best practices to increase the “readiness” of these infrastructures for AI ( 60 ) with calls, checklists, tools and frameworks for ethical use of AI in medicine/biobanking ( 47 , 62 ). New and alternative forms of governance are needed for a new form of biobanking that revolves around big data considering the increasing widening of the scope of data from social media to devices capturing bodily function, resulting in streams of data over time and analytical capacity over space ( 63 ). Biobanks’ positioning at the in practice often gray intersection of healthcare and research can inform the discussions on health data spaces, in light of the recent developments.…”

Genetic tests as the strongest motivator of cooperation between participants and biobanks–Findings from cross-sectional study

“…There are numerous issues that current and future biobanks must address. Several examples among many others analyzed in the latest literature on biobanking include material transfer agreements, access to samples and data, ownership and custodianship of data and samples, feedback regarding results and incidental findings , big data, and artificial intelligence in biobanking (Kinkorová and Topolčan, 2020;Tozzo et al, 2023). All these issues need to be taken into consideration when planning future research.…”

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

“…In relation to this aspect, Holub P et al proposed FAIR-HEALTH principles [ 60 ], including additional components such as quality aspects related to research reproducibility and meaningful reuse of the data [ 61 , 62 ]; incentives to stimulate effective enrichment of data sets and biological material collections and their reuse on all levels [ 60 , 63 ]; privacy-respecting approaches for working with the human material and data. Ultimately, to overcome these challenges, digital biobanks must ensure that data and information are standardized, reproducible and integrated, following regulatory and bioethical guidelines [ 64 ]. Additionally, adhering to ISO standards and SOPs, as well as following the FAIR principles, will contribute to the success of the digital biobank model.…”