Grand Valley State University Professor Computing and Information Systems
Clinical decision support (CDS) is considered knowledge and person-specific information, intelligently filtered or presented at appropriate times to improve healthcare decisions. So far, CDS enjoyed partial successes in selected areas within the US healthcare system. For example, its role in reducing care costs and improving care quality as well as positively impacting healthcare providers’ performance with drug ordering and preventive care reminder systems is well established, however, most likely only because this type of CDS relies on a minimum of patient data that are readily available before the advice is generated. The difficulty to access appropriately selected information from the entire patient record, for instance, due to distribution on different providers electronic health records or the lack of integration between clinical and administration systems, may be one important reason that it has not realized its anticipated potential to transform healthcare. With the advance of big data technologies and artificial intelligence (AI) CDS in healthcare is changing, but new challenges arise. For instance, the question on how to transform big data into accessible “smart data” has attracted commercial players like IBM Watson Health or inspired the exploration of the use of blockchain technology for electronic health records. Since there is a need for high-quality and effective ways to design, develop, present, implement, evaluate, and maintain various types of clinical decision support capabilities for clinicians, patients and consumers, the presentation further will explore issues to be resolved before patients and organizations can begin to realize the maximum possible benefits of these systems. One example would be discussing the potential of AI to reduce the cognitive burden of the user of the technology.