Complex Decision Technologies

Clusters of interacting methods for improving health services network design and delivery. Simplified version of IM-14982 combined with IM-17598 and IM-9773

Clusters of interacting methods for improving health services network design and delivery. Simplified version of IM-14982 combined with IM-17598 and IM-9773
Here we unfold modelling methods for improving health services network design and delivery. Clusters of these interacting methods provide advanced decision technologies for supporting a range of strategic resource allocation, tactical service management and daily individual clinical decisions.
The Science of Improvement requires both theory and data; the amount of data is rapidly increasing due to pervasive digital technologies
Models or hypotheses based on theory sharpen models and guide data collection. As James Hansen puts it, "Model is a word for people who can't spell hypothesis". Data grounds models in reality and leads to model refinement.
"Theory without data is myth: data without theory is madness." Phil Zuckerman
Data science is the study of the generalizable extraction of knowledge from data. 
A data scientist requires an integrated skill set spanning mathematics, machine learning, artificial intelligence, statistics, databases, and optimization, along with a deep understanding of the craft of problem formulation to engineer effective solutions.
Data science is being incorporated into many decision support technologies as business intelligence and data analytics.
An important addition to these decision technologies is the use of systems science to allow virtual experiments comparing the effects of a range of potential interventions
These decision technologies combining data and dynamic models produce more rapid learning about what works to improve systems of services
You can explore this in more detail by opening folders (click on + squares) and reading notes (click on i circles when hovering over an element).

View the model in Insight Maker