Transforming Electronic Health Records from Annoyances to Assistants: A Research Agenda for the Next Decade

Clinical informatics research, and before that, medical informatics research, has made great strides in developing tools to help clinicians improve clinical decision-making and patient care. Yet, electronic health records (EHR) systems today show little aptitude for even simple tasks, like retrieving relevant patient information, while suppressing that which is irrelevant. When bringing artificial intelligence to bear, the best EHRs seem to do is to overwhelm us with alerts that a clinician must override to take action. When the “ learning health system ” attempts to use data from these systems, it must rely on indirect methods, such as machine learning and natural language processing, to figure out what was actually going on with the patient. The advances that have been made to bring decision support into EHRs rely on formally represented – that is structured and coded – data, such as problem lists, laboratory results and medication lists. What ’ s missing is a formal representation of the clinical cognition of the patient ’ s situation: what we think is going on, what our goals are, what we are trying to do about it, and why we have chosen to do it that way. Adding such information to the EHR would enable informaticians to enhance their tools in ways that will improve situational awareness, reduce information overload, make decision support systems provide more relevant knowledge to clinicians, and enable clinical researchers to draw more solid inferences from o...
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