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  4. MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records

MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records

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sword-2022-07-20T16:20:21.original.xml (130 B)
Original SWORD entry document
Author(s)
Murray, Luke
•
Gopinath, Divya
•
Agrawal, Monica
•
Horng, Steven
•
Sontag, David
•
Karger, David R
Date Issued
October 10, 2021
Journal
The 34th Annual ACM Symposium on User Interface Software and Technology
Publisher
ACM
Citation
Murray, Luke, Gopinath, Divya, Agrawal, Monica, Horng, Steven, Sontag, David et al. 2021. "MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records." The 34th Annual ACM Symposium on User Interface Software and Technology.
Version
Final published version
Abstract
Clinical documentation can be transformed by Electronic Health Records, yet the documentation process is still a tedious, time-consuming, 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 unifies 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 flexibility 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/143893
DOI of Published Version
https://doi.org/10.1145/3472749.3474814
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