Towards a New Science of a Clinical Data Intelligence
This work aims to establish a foundational framework for data-driven healthcare improvements, but it is incremental as it builds on existing fields like knowledge engineering and machine learning.
The paper defines Clinical Data Intelligence as analyzing clinical routine data to improve patient care, proposing it as a science for deriving generalizable results through big data analysis, and relates this to a funded research project in German hospitals.
In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.