Foundations of Clinical Data and Intelligent Care Systems

Abstract

The scope is clinical data—systems generating, collecting, storing, distributing, and integrating clinical data for individual and population health. Foundations and characteristics of the data are explored first, including types, formats, lifecycles, quality, and governance. Analysis then turns to intelligent care systems—systems providing automated information supports and recommendations to clinical professionals—for the evaluation, validation, ethical, legal, and societal implications of intelligent care systems. The exploration reveals that intelligent care systems can and do provide useful decision support, but areas of significant concern also remain. Intensive work in evaluation, validation, ethical, legal, and related fields represents an exciting area of research, especially as trust and assurance hinge on successful and comprehensive work in these domains. The first tier of exploration addresses the generation, collection, storage, distribution, and integration of clinical data. A collection is needed to provide the data support for clinical decision support systems that partially automate generation of information for health professionals, for machine-learning solutions and other forms of artificial intelligence in medicine that combine learning-based modeling and reasoning to augment professional capabilities, and especially in medicine, for training in clinically relevant tasks through analysis of video streams of medical procedures. Analytical focus includes the types of data produced in healthcare and clinical practice, their representation in structured, semi-structured, and unstructured forms, the factors influencing their quality and accuracy, the lifecycles of healthcare data (from the patients’ perspective), and the governance frameworks ensuring appropriate and sufficient levels of data quality and suitability for downstream use.

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