We report on the current state of type 2 diabetes clinical decision support systems (CDSS), identify gaps that contribute to the lack of CDSS success, and apply lessons learned from practice for developing and implementing a localized diabetes CDSS. A survey of the literature reveals mixed findings regarding the efficacy of the CDSS; they do not include patient-rich information – the patient experience data in the electronic health records. We believe that diabetes care can improve by guiding clinical decisions using published evidence, patient preferences, and clinical data augmented by the local patient experience and social determinants of health using natural language processing and machine learning techniques
