Background

The results of data analyses from (randomized) clinical trials typically form the basis for the approval of medications and interventions in the United States and Europe. Following growing demands for greater transparency regarding individual patient data, pharmaceutical and medical technology companies, as well as public institutions, have adapted their data management practices and now make these data available to other researchers through dedicated data-sharing infrastructures.

The largest of these infrastructures is Vivli. Originating from a research initiative at Harvard Medical School, Vivli currently provides access to data from more than 5 million patients across over 7,500 clinical trials.

As part of a broader research project at the Chair of Information Systems (Prof. Fürstenau), both quantitative and qualitative data on this infrastructure have been collected. These include metadata on the 7,500 clinical trials, more than 800 data-sharing requests, and over 400 publications resulting from the reuse of these data.

Research Directions

• Data Reuse
Clinical trial data vary in their characteristics, such as the targeted disease area or the medication under investigation. The anonymization of clinical trial data is costly and complex. Therefore, it would be worthwhile to examine which criteria can predict the potential for secondary use of such data.

• Innovation Outcomes
For many data infrastructures, assessing the value created through data sharing remains a challenge. To ensure the long-term sustainability of these infrastructures, it is essential to capture the benefits generated. While usage metrics (such as data requests and resulting publications) provide a descriptive foundation, there is a lack of tools and indicators to measure the innovation outcomes that emerge from data reuse.

• Infrastructure Emergence and Development
In addition to the Vivli platform—considered the global hub for clinical trial data—there are other infrastructures that specifically focus on rare diseases. Comparing these infrastructures could help identify and explain key differences in their development and functioning.

References

Anckaert, P.-E. (2025). When the drugs (don’t) work: The role of science in product commercialization. Research Policy, 54(5), 105237. https://doi.org/10.1016/j.respol.2025.105237

Kilgus, T., Patecka, A., Schurig, T., Kari, A., Gubser, R., Gersch, M., Wessel, L., & Fürstenau, D. (2024). Creating Value from the Secondary Use of Health Data: International Examples, Best Practices, and Opportunities to Scale. Communication of the Association for Information Systems, 55, 507–534. https://doi.org/10.17705/1CAIS.05520

Kilgus, Tim; Kari, Arthur; Gubser, Rahel; Dewey, Marc; Gersch, Martin; and Fürstenau, Daniel (2024). Bridging the valley of death: balancing value creation and capture in health data sharing platforms. ICIS 2024 Proceedings. 19. https://aisel.aisnet.org/icis2024/ishealthcare/ishealthcare/19

Pujol Priego, Laia and Wareham, Jonathan. (2024). Data Commoning in the Life Sciences. MIS Quarterly, 48(2), 491–520.