Challenges of Adopting Human-Centered Intelligent Systems: An Organizational Learning Approach

Abstract

Clinical decision support systems (CDSSs) are human-centered intelligent systems (HCISs) that use codified medical expertise or large data sets for medical decision recommendations. Most analytical CDSS that exploit the opportunities of large data sets and analytic technique remain within a research and development environment and lack adoptions in clinical contexts. To understand this, we analyse CDSS adoption as an organizational learning process. We apply a model of organizational learning on the case of an analytical CDSS implementation which analyses medical data to predict the probability on sepsis for prematurely born babies to support the physicians’ decision-making on ministering antibiotics. In our discussion, we next compare our case findings with possible organizational learning challenges for the adoption of other (medical) HCISs and we draw consequences for projects of HCIS adoption in organizations.