Developing a causal model to understand and intervene in organizational patterns
In a new video released about the lessons learned in the framework of BigMedilytics, Dr. Anne Marie Weggelaar-Jansen of the Erasmus School of Health Policy & Management, explains a tool that enables you to understand the organizational dynamics and how you can get an understanding of how to intervene on actors and factors that reinforce each other.
This causal modelling tool is used in the BigMedilytics project to understand the uptake of big data technology in healthcare. As a result, three videos were made explaining causal models on how the innovative nature of big data is slowing down the uptake of big data; on the interdependences between actors and the factors with different expertise and their rules and regulations, and on how to measure the productivity of big data.
Causal models are visual aids that are helpful to understand how self-enforcing patterns can be broken and how we can change things. In this video, you will find out guidance on how to develop a causal model yourself.
Three lessons can be learned from the BigMedilytics project:
- One should develop causal models when one sees a problem recurring and/or simple solutions that do not work.
- In understanding the uptake of big data a focus on feedback mechanisms that influence things or start patterns is important.
- Making and testing causal models are interventions in their own right. It can empower early adaptors, shift power balances, and so forth.