Toward a smarter electronic health record

news.mit.edu
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fairly difficult
MedKnowts, a "smart" electronic health record system, can help doctors work more efficiently by presenting relevant information from a patient's medical history, autocompleting medical terms as a clinician types, and auto-populating repetitive fields. The system was developed by MIT and Beth Israel Deaconess Medical Center researchers.
Electronic health records have been widely adopted with the hope they would save time and improve the quality of patient care. But due to fragmented interfaces and tedious data entry procedures, physicians often spend more time navigating these systems than they do interacting with patients.

Researchers at MIT and the Beth Israel Deaconess Medical Center are combining machine learning and human-computer interaction to create a better electronic health record (EHR). They developed MedKnowts, a system that unifies the processes of looking up medical records and documenting patient information into a single, interactive interface.

Driven by artificial intelligence, this "smart" EHR automatically displays customized, patient-specific medical records when a clinician needs them. MedKnowts also provides autocomplete for clinical terms and auto-populates fields with patient information to help doctors work more efficiently.

"In the origins of EHRs, there was this tremendous enthusiasm that getting all this information organized would be helpful to be able to track billing records, report statistics to the government, and provide data for scientific research. But few stopped to ask the deep questions around whether they would be of use for the clinician. I think a lot of clinicians feel they have had this burden of EHRs put on them for the benefit of bureaucracies and scientists and accountants. We came into this project asking how EHRs might actually benefit clinicians," says David Karger, professor of computer science in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and senior author of the paper.

The research was co-authored by CSAIL graduate students Luke Murray, who is the lead author, Divya Gopinath, and Monica Agrawal. Other authors include Steven Horng, an emergency medicine attending physician and clinical lead for machine learning at the Center for Healthcare Delivery Science of Beth Israel Deaconess Medical Center, and David Sontag,…
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