Clinical Decision Support Systems in Practice: Current Status and Challenges

Decision support systems (DSS) are computer programs based on artificial intelligence methods that contribute to reaching a correct decision in an often-narrow domain of interest. Clinical decision support systems (CDSS) are such DSSs that may be used by medical professionals in clinics and hospitals. They are used for diagnosis, treatment protocol recommendations, treatment outcome predictions and other tasks. CDSS are constructed based on symbolic and machine learning (including deep learning) approaches to represent and infer medical knowledge. The aim of this work is to provide an overview of past and current methods in designing a successful CDSS. The study considers the systems that were claimed to be implemented in clinical practice. Currently, the development of a CDSS is mostly pursued in two directions: 1) a more traditional approach based on rules, ontologies, probabilistic models, and the use of standards; 2) machine learning based approach. Both approaches may be used complementary within a healthcare information system. This work seeks to provide an objective view on the advantages and limitations of the approaches as well to discuss future research avenues that could lead to more accurate and trustworthy CDSS and improved healthcare.