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Background information for the postdoctoral line of research: Disruption in Healthcare

“Disruption in Healthcare”, one of the lines of research of the Data Science partnership program between CZ and JADS. The objective of this line of research is to gain insight into the future of healthcare with possible disruptive forces, and the importance and role of data in this future.

CZ and JADS have decided to appoint two postdoctoral researchers to this position for two 2-year terms.

In order to determine the impact of new disruptive technologies (including Data Science) on healthcare and the healthcare system, qualitative research will initially be conducted (e.g. in-depth interviews, working with focus groups, etc.). New business models within the healthcare sector and the possible changes caused by these models in the existing value chains will be investigated. Gartner's hype cycle methodology will be used as a theoretical starting point for identifying disruptive technologies.

The impact of disruption (visualized in the figure below) will be explored in detail from four different possible perspectives:

  1. The business impact of new Data Science initiatives of CZ (CZ Data Lab). An analysis is performed to determine the business models of new data propositions of CZ and how they influence the various value chain parties within the healthcare sector.
  2. The business impact of Health start-ups. The most innovative technologies and new business models in healthcare start-ups, and the impact these start-ups have on the value chains within the healthcare sector (patients/consumers, providers, insurers), are investigated. The researcher can choose to follow 10-20 start-ups for a longer period.
  3. Learning from international parallels. In this case, a group of 10-20 leading international health insurers will be examined (within their own institutional frameworks) to determine if they have embraced disruptive technologies and, if so, how they have done so, and how this has changed their business model.
  4. Learning from historical parallels. Here, research is conducted into other sectors that have experienced a significant technological digital disruption (including media, retail), and the manner in which this changed the business model and the value chains. Which organizations dealt with this transition in the best way and which were unable to cope with it. The advantage of the latter approach is that the researcher can learn more about the long-term impact of new business models and the reactions of existing companies.


It may not be possible to explore each of the four tracks to the same extent. The last track is especially time-consuming. The tracks to be followed will be determined later. The results of related research tracks could be included.

As an additional result of the qualitative research, early warning signs of disruption within the healthcare sector may be found. If we can identify these early warning signs of disruption, an early warning system could be developed in a second phase.

Figure: Learning about disruptive business models

A phased approach will therefore be followed, in which we will start with an initial 2-year postdoctoral researcher who will focus on the qualitative research in accordance with the four proposed tracks. If this research quickly yields results, we may choose to focus on more quantitative research in the next two years, in which early warning signs are researched using available data (including healthcare data, open data, business statistics). We are therefore aiming for a 2+2-year appointment. The appointment of the first postdoctoral researcher may be extended by 2 years if we decide to continue with the qualitative approach.

Postdoctoral researcher profile (first 2 years, then possible extension)

  • PhD in a relevant subject (healthcare, statistics, econometrics, mathematics, entrepreneurship, innovation).
  • Knowledge of modern Data Science technologies and/or statistics (or the willingness to acquire this knowledge) will be an advantage.
  • Knowledge of qualitative and/or quantitative research.
  • Affinity with healthcare / health insurance.
  • Affinity with research into business models and digital disruption, innovation and entrepreneurship.
  • Is able to bridge the gap between different disciplines and groups; between management and the workforce, between Data Science and healthcare, between technical experts and substantive professionals.
  • A great deal of knowledge about the world of large organizations and the complexity of digital transformations.
  • Knowledge of organizational issues and the possible impact of data on people's work.
  • Is able to be in charge of a project team / project.