Collaborative Research: SCH: Machine Learning Driven User Interfaces for Information Gathering and Synthesis from Medical Records

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Clinicians have to search through a patient’s past medical records to contextualize the patient’s condition and reach a personalized diagnosis and treatment plan. However, user interfaces in healthcare are unwieldy to navigate and are largely a digitization of static paper forms from legacy clinical workflows, a paradigm that has contributed to poor usability and clinician burnout. This project brings together experts in machine learning and human-computer interaction to develop a novel dynamic contextual user interface that transforms the medical note-taking interface from a simple recording device to a tool that can help clinicians quickly find and synthesize information. The project’s novelties are in advancing the foundations of human-AI interaction and in advancing the state-of-the-art in artificial intelligence for health care with novel methods that autonomously retrieve, summarize, and surface relevant information for clinicians, at the right time. The project’s impacts are in an area of national priority, health IT, as it aims to modernize electronic health records. The resulting system will help prevent subtle findings from being overlooked, patients from being misdiagnosed, and critical interventions from being missed, ultimately resulting in a decrease in morbidity, mortality, and overall cost of health care. It will additionally decrease documentation burden and mitigate physician burnout. Prior attempts at developing contextual displays of patient information were manual, labor-intensive processes that relied on domain expertise and were neither scalable, maintainable, nor customized to individual users. Automating contextual displays is challenging because what information is relevant highly depends on the user, patient, and specific clinical context. Traditional machine learning approaches are infeasible because of the lack of labeled training data. This project develops new user interfaces that enable the large-scale collection of implicit usage-based training data as part of routine user workflows. Specifically, this project develops a novel information foraging interface, the ‘semantic clipboard’, which clinicians will use while reading patients’ past medical records and while writing notes. Using the data collected through this new interface, the investigators will develop new machine learning methodologies to predict the relevant pieces of information that should appear in these contextual displays, customized to the clinical scenario as well as the user. Through this project and the investigators’ academic teaching, a new generation of cross-disciplinary researchers will be educated: graduate students who understand the fundamental challenges of human computer interaction, machine learning, and clinical medicine, and medical fellows who understand machine learning and the subtleties of deploying machine learning in health care.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.