Researchers at the MIT CSAIL and the Beth Israel Deaconess Medical Center have teamed up to improve electronic health records (EHRs) with AI machine learning.
The process of clinical documentation remains a “tedious, time-consuming, and error-prone process.”
The scientists cite how this is especially the case in emergency rooms, where clinicians may see as many as thirty-five patients during a shift, requiring them to rapidly absorb the content of medical histories of patients from “multi-faceted requirements and fragmented interfaces for information exploration and documentation” that are often new to them before creating an informed diagnosis and targeted plan of care.
Although EHRs offer vast improvements in speeding up the access and retrieval of patient records, the documentation systems can be time-consuming and burdensome for clinicians to use.
To address these pain points of electronic health records, the MIT CSAIL researchers created an AI machine learning system called MedKnowts and implemented it at the Beth Israel Deaconess Medical Center.
The AI-backed EHR system integrates the information retrieval system with a note-taking editor so that the search is efficient.
The system enables clinicians to use natural language and automates the intake of structured data.
Documentation is streamlined with features such as auto-population of text, proactive information retrieval, and easy parsing of long notes.
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