European healthcare systems readiness to shift from ‘one-size fits all’ to personalized medicine

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Objective: To assess the willingness of older Swiss adults to share genetic data for research purposes and to investigate factors that might impact their willingness to share data. Methods: Semi-structured interviews were conducted among 40 participants (19 male and 21 female) aged between 67 and 92 years, between December 2013 and April 2014 attending the Seniorenuniversität Zürich, Switzerland. All interviews were audio-recorded, transcribed verbatim, and anonymized. For the analysis of the interviews, an initial coding scheme was developed, refined over time, and applied afterwards to all interviews. Results: The majority of participants were in favor of placing genetic data to research’s disposal. Participant’s motivations to share data were mainly driven by altruistic reasons and by contributing to the greater good. Furthermore, several factors which might impact the willingness to share data such as sharing data with private companies, generational differences, differences between sharing genetic data or health data, and sharing due to financial incentives were highlighted. Last, some participants indicated concerns regarding data sharing such as misuse of data, the fear of becoming a transparent citizen, and data safety. However, 20% of the participants express confidence in data protection. Even participants who were skeptical in the beginning of the interviews admitted the benefits of data sharing. Discussion: Overall, this study suggests older citizens are willing to share their data for research purposes. However, most of them will only contribute if their data is appropriately protected and if they trust the research institution to use the shared data responsibly. More transparency and detailed information regarding the data usage are urgently needed. There is a great need to increase the engagement of older adults in research since they present a large segment of our society – one which is often underexamined in research. Conclusion: Increased focus on general public engagement, especially of older adults, in scientific research activities known as “citizen science” is needed to further strengthen the uptake of personalized medicine.

Section: Discussionmentioning

confidence: 99%

“…Healthcare systems are in the process of being transformed by the emergence of “omics” technologies [1]. Genetic testing became one of the cornerstones in the development and implementation of personalized medicine (PM) [2].…”

Section: Introductionmentioning

confidence: 99%

Attitudes towards Personal Genomics and Sharing of Genetic Data among Older Swiss Adults: A Qualitative Study

Mählmann

Halfmann

Wyl

et al. 2017

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“…Fifth, to overcome the current practice of “one-size-fits-all” treatment, HDC can leverage information for the development of innovative approaches to improve current risk stratification methodologies and adjust therapies and prevention strategies to the individual’s needs, opening the way for personalized medicine [26]. The first success stories in this context are mainly reported in the field of oncology [27].…”

Section: Hdc For Policy Empowermentmentioning

confidence: 99%

Big Data for Public Health Policy-Making: Policy Empowerment

Mählmann

Reumann

Evangelatos

et al. 2017

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Digitization is considered to radically transform healthcare. As such, with seemingly unlimited opportunities to collect data, it will play an important role in the public health policy-making process. In this context, health data cooperatives (HDC) are a key component and core element for public health policy-making and for exploiting the potential of all the existing and rapidly emerging data sources. Being able to leverage all the data requires overcoming the computational, algorithmic, and technological challenges that characterize today’s highly heterogeneous data landscape, as well as a host of diverse regulatory, normative, governance, and policy constraints. The full potential of big data can only be realized if data are being made accessible and shared. Treating research data as a public good, creating HDC to empower citizens through citizen-owned health data, and allowing data access for research and the development of new diagnostics, therapies, and public health policies will yield the transformative impact of digital health. The HDC model for data governance is an arrangement, based on moral codes, that encourages citizens to participate in the improvement of their own health. This then enables public health institutions and policymakers to monitor policy changes and evaluate their impact and risk on a population level.

“…Moreover, aggregated data make it possible for P4 medicine to develop new multilevel biological networks. These might help to understand currently unknown causes of diseases and to identify which biological systems are disturbed in bodies during illness [6,23]. As a result, prevention and predictive disease models might be established.…”

Section: Knowledge Benefitmentioning

confidence: 99%

Potentials and Challenges of the Health Data Cooperative Model

Roessel

Reumann

Brand

2017

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Introduction: Currently, abundances of highly relevant health data are locked up in data silos due to decentralized storage and data protection laws. The health data cooperative (HDC) model is established to make this valuable data available for societal purposes. The aim of this study is to analyse the HDC model and its potentials and challenges. Results: An HDC is a health data bank. The HDC model has as core principles a cooperative approach, citizen-centredness, not-for-profit structure, data enquiry procedure, worldwide accessibility, cloud computing data storage, open source, and transparency about governance policy. HDC members have access to the HDC platform, which consists of the “core,” the “app store,” and the “big data.” This, respectively, enables the users to collect, store, manage, and share health information, to analyse personal health data, and to conduct big data analytics. Identified potentials of the HDC model are digitization of healthcare information, citizen empowerment, knowledge benefit, patient empowerment, cloud computing data storage, and reduction in healthcare expenses. Nevertheless, there are also challenges linked with this approach, including privacy and data security, citizens’ restraint, disclosure of clinical results, big data, and commercial interest. Limitations and Outlook: The results of this article are not generalizable because multiple studies with a limited number of study participants are included. Therefore, it is recommended to undertake further elaborate research on these topics among larger and various groups of individuals. Additionally, more pilots on the HDC model are required before it can be fully implemented. Moreover, when the HDC model becomes operational, further research on its performances should be undertaken.