The collaborators will embark on a nine-month study, during which they will apply machine learning algorithms to detect patterns in audio recordings of patient visits. To collect audio samples, physicians at Stanford Family Medicine will record visits with patients who opt into the research project.
The goal of the study is to identify audio patterns that inform an algorithm’s ability to complete progress notes. This “digital scribe” would be able to save physicians time by allowing them to concentrate on patients, while the machine learning algorithm automatically enters information into the EHR.
The researchers will use the pilot study to identify challenges and indicate whether a digital scribe is feasible to create. If successful, they will continue to develop a tool for physician use.
“This is really new and we’re in the early stages of this technology,” Dr. Lin noted.
More articles on EHRs:
UC San Diego Health, UC Irvine Health begin sharing single Epic EMR
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AMA President Dr. David Barbe: ‘We are fighting to prevent IT vendors from blocking information’
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