MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records

Abstract

Clinical documentation can be transformed by Electronic Health Records, yet the documentation process is still a tedious, timeconsuming, and error-prone process. Clinicians are faced with multi-faceted requirements and fragmented interfaces for information exploration and documentation. These challenges are only exacerbated in the Emergency Department–clinicians often see 35 patients in one shift, during which they have to synthesize an often previously unknown patient’s medical records in order to reach a tailored diagnosis and treatment plan. To better support this information synthesis, clinical documentation tools must enable rapid contextual access to the patient’s medical record. MedKnowts is an integrated note-taking editor and information retrieval system which unifes the documentation and search process and provides concise synthesized concept-oriented slices of the patient’s medical record. MedKnowts automatically captures structured data while still allowing users the fexibility of natural language. MedKnowts leverages this structure to enable easier parsing of long notes, auto-populated text, and proactive information retrieval, easing the documentation burden.

Publication
UIST ‘21: The 34th Annual ACM Symposium on User Interface Software and Technology
Divya Gopinath
Divya Gopinath
Master’s student

Truera

Monica Agrawal
Monica Agrawal
PhD Student

Monica’s research interests include reasoning over longitudinal clinical notes, building more intelligent electronic health records, studying user-ML interactions in clinical settings, and developing algorithms that can incorporate domain knowledge.

David Sontag
David Sontag
Associate Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

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