Here’s How Doctors Can Use Machine Learning To Access Patients’ Data

A physician’s consultation is more likely to be successful when complete information about a patient is available. With the introduction of electronic records, doctors can easily navigate hospital records. And the records are easily accessible with the push of a button.

However, the nature of the records is often cumbersome to handle, making the whole process a bit complicated for health personnel.

Studies have shown that even when a physician is trained to handle electronic health records (EHR), finding some patient data can be time-consuming. As a result, physicians allocate more time to looking for patients’ information than interacting with patients.

A Machine Learning Model to Simplify Patient Health Database

Developers have identified machine learning (ML) as the solution to help streamline the automation of EHR. Accordingly, developers are working on creating a flexible ML-enabled model where physicians can easily find patients’ data.

Developers will further integrate EHRs with ML features that could eliminate cumbersome navigation faced by doctors. However, developers must train the models intended for use with multiple health datasets containing relevant information.

But this will be difficult to do since accessing patients’ health records for whatever reason is restricted due to privacy concerns. Meanwhile, relevant medical information about patients’ is needed for this to work.

Researchers at the Massachusetts Institute of Technology (MIT) have developed a model based on ML features. The MIT collaborates with seasoned medical experts to examine physicians’ questions when perusing EHR.

Doctors already find scanning patients’ electronic records challenging and often fish out the correct data. By implication, physicians spend most of their consultation time looking for vital information instead of performing patient examinations.

The ML Model to the Rescue

A public dataset of over 2,000 clinical questions was developed after a partnership between MIT researchers and medical experts. The researchers used the datasets to train a machine learning platform to generate relevant clinical questions.

According to the researchers, the newly developed model asked high-quality, clinically-focused questions. This is compared to real-life questions asked by doctors, which turned out to be over 60%.

Using this dataset, the developers plan to generate millions of medically necessary questions. In addition, they also intend to use the same questions to train an ML model to help doctors access sought-after patient data.

The exciting thing about machine learning is that a trained model can simultaneously accommodate billions of data points. However, its application in the healthcare sector requires careful planning and creativity due to the lack of ample data.

Machine learning has many applications spanning different industries and providing unrivaled results. Healthcare settings can integrate technology to support several aspects of their operations.

Likewise, integrating ML models into the health sector will be a game changer in terms of efficiency. The latest development of an ML-based EHR will make patient records readily available for consultants to review.

Machine learning will undoubtedly accelerate innovative ideas for adoption in health settings in the next few years.