The MIDATA Platform for Ethical and Fair Citizen/Patient Participation in Personalized Health Research
Short Summary
The MIDATA platform provides the optimal environment for secure and comprehensive aggregation and analysis of all relevant personal data under the control of the citizen/patient. It unlocks the power of personalized health research in a secure, ethical and fair manner. This project will further develop the MIDATA platform and proof its clinical utility in Multiple Sclerosis (MS), a chronic, highly heterogeneous disease with a high socio-economic impact. Core to the project is the development of the MitrendS App that will allow MS patients to monitor “on-the-go” various important disease parameters.
Goals
This project will implement three key functionalities to transform MIDATA into a full-featured and universal platform for patient-controlled data aggregation, consent management, and analytics. 1) Efficient interfaces between MIDATA and existing e-health platforms and clinical information systems to obtain a comprehensive data set across traditional and novel data sources and to facilitate the development of new tools for risk assessment and treatment decisions. 2) Machine Learning techniques to make personalized predictions concerning each patient’s disease progression. For this purpose, algorithms capable of finding complex patterns in aggregated data (from mobile apps, medical reports, and environmental data) will be developed. 3) Systemic ethical oversight for data-driven personalized health research. We will create a toolbox of processes, mechanisms and governance solutions for the MIDATA platform. Systemic oversight principles will define the tools tailored to patient-driven data aggregation. To ensure real-world utility and compatibility with daily clinical and research practice, these developments and governance infrastructure will be implemented and tested in the framework of a clinical research study (MS) carried out at the University Hospital in Zurich.
Significance
This project develops a technical, procedural and ethical framework for the inclusion of personal data into research and their optimized clinical use, to the best benefit of society and patients, respectively. It establishes within the clinical context of MS, how different sets of health-related data from different sources (clinical information systems, EHRs, environmental sources and mobile sensors) can be securely stored and aggregated under the control of the patient. MS patients participating in the study will highly benefit from integrated data analysis, as their treatment regimen can be adapted to fit individual disease progression and state. All components will be available as a resource to all future SPHN / PHRT projects.
Background
Multiple sclerosis is an autoimmune disease associated with recurring inflammation of brain and spinal cord. Worldwide there are over a million cases of MS with around 10’000 affected individuals in Switzerland. MS is especially suited to tackle the challenges arising with the acquisition and aggregation of health-related personal data and to probe the development of new tools for prediction of disease evolution since an interplay of genetic, environmental and lifestyle factors strongly influence disease course. MS is highly heterogeneous in clinical presentation and shows strong variability in disease course and response to different treatments. MS is ideal to establish personalized approaches that aim to integrate all available patient-related information to predict context-dependent disease progression and guide treatment decisions for individual patients.
Publications
Patents / Startups
Publications
- Alessandro Blasimme, Effy Vayena, and Ernst Hafen (2018). Democratizing Health Research Through Data Cooperatives. Philosophy & Technology, vol. 31: no. 3, pp. 473-479, Dordrecht: Springer.
- Barrios, L., Oldrati, P., Lindlbauer, D., Hilty, M., Hayward-Koennecke, H., Holz, C., & Lutterotti, A. (2020, April). A Rapid Tapping Task on Commodity Smartphones to Assess Motor Fatigability. 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-10).
- Ernst Hafen (2018). Personal Data Cooperatives – A New Data Governance Framework for data Donations and Precision Health. The Ethics of Medical Data Donation; Editors: Krutzinna, Jenny, Floridi, Luciano; Chapter 9, pages 141-149; SpringerOpen Book.
- Ernst Hafen (2018). Personal Data Cooperatives – A New Data Governance Framework for Data Donations and Precision Health. The Ethics of Medical Data Donation; Editors: Krutzinna, Jenny, Floridi, Luciano; Chapter 9, pages 141-149; SpringerOpen Book.
- Gille, and E. Vayena. “How private individuals maintain privacy and govern their own health data cooperative – MIDATA in Switzerland.” In Governing Privacy as Knowledge Commons, M.R. Sanfilippo, K.J. Strandburg, and B.M. Frischmann, eds. Cambridge University Press, forthcoming 2020.
- Gille F., Axler R. & Blasimme A. Transparency About Governance Contributes to Biobanks’ Trustworthiness: Call for Action. Biopreservation and Biobanking. https://doi.org/10.1089/bio.2020.0057
- Gille, F., Vayena, E. & Blasimme, A. (2020). Future-proofing biobanks’ governance. Eur J Hum Genet. https://doi.org/10.1038/s41431-020-0646-4
- Liliana Barrios, Pietro Oldrati, David Lindlbauer, Marc Hilty, Helen Hayward-Koennecke, Christian Holz, Andreas Lutterotti. A Rapid Tapping Task on Commodity Smartphones to Assess Motor Fatigability. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI 2020, ISBN 978-1-4503-6708-0/20/04, Honolulu, HI, USA, April 2020; doi: 10.1145/3313831.3376588
- Liliana Barrios, Pietro Oldrati, David Lindlbauer, Marc Hilty, Helen Hayward-Koennecke,Christian Holz, Andreas Lutterotti. (2020, April) Measuring Motor Fatigability on a Smartphone App in Multiple Sclerosis. Österreichische Gesellschaft für Neurologie ÖGN 2020
- Hilty, H. Hayward-Koennecke, C. Kaiser, F. von Kaenel, C. Esteban, S. Napolitano, F. Luchs, O. Mujkic, R. Iseli, S. Iseli , S. Broicher, O. Geisseler, G. Raetsch , E. Hafen, R. Martin, S. Bignens, A. Lutterotti. (2020, April). MitrendS an App-based Hand Function Test for Multiple Sclerosis. Österreichische Gesellschaft für Neurologie ÖGN 2020
- Vayena and Blasimme (2018). Health Research With Big Data – Time for Systemic Oversight. The Journal of Law, Medicine & Ethics, 46 (2018): 119-129. Final Scientific Report 9
Patents / Startups
Technology Translation
Prof. Dr. Ernst Hafen
ETH Zurich
Consortium
Status
Completed
- 1st Call, Tech Trans
Funded by
