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.
To design an EHR that would benefit doctors, the researchers had to think like doctors.
They created a note-taking editor with a side panel that displays relevant information from
the patient’s medical history.
That historical information appears in the form of cards that are focused on particular
problems or concepts.
For instance, if MedKnowts identifies the clinical term “diabetes” in the text as
a clinician types, the system automatically displays a “diabetes card” containing
medications, lab values, and snippets from past records that are relevant to diabetes
treatment.
Most EHRs store historical information on separate pages and list medications or lab
values alphabetically or chronologically, forcing the clinician to search through data
to find the information they need.
MedKnowts only displays information relevant to the particular concept the clinician is
writing about.
Pieces of interactive text called chips serve as links to related cards.
As a physician types a note, the autocomplete system recognizes clinical terms, such as
medications, lab values, or conditions, and transforms them into chips.
Each chip is displayed as a word or phrase that has been highlighted in a certain color
depending on its category (red for a medical condition, green for a medication, yellow
for a procedure, etc.)
Through the use of autocomplete, structured data on the patient’s conditions, symptoms,
and medication usage is collected with no additional effort from the physician.
After a year-long iterative design process, the researchers tested MedKnowts by deploying
the software in the emergency department at Beth Israel Deaconess Medical Center in Boston.
They worked with an emergency physician and four hospital scribes who enter notes into
the electronic health record.
Deploying the software in an emergency department, where doctors operate in a high-stress environment,
involved a delicate balancing act.
The researchers plan to improve the machine learning algorithms that drive MedKnowts so
the system can more effectively highlight parts of the medical record that are most
relevant.
They also want to consider the needs of different medical users.
The researchers designed MedKnowts with an emergency department in mind — a setting
where doctors are typically seeing patients for the first time.
A primary care physician who knows their patients much better would likely have some different
needs.
In the longer-term, the researchers envision creating an adaptive system that clinicians
can contribute to.
For example, perhaps a doctor realizes a certain cardiology term is missing from MedKnowts
and adds that information to a card, which would update the system for all users.
The team is exploring commercialization as an avenue for further deployment.