Information that’s accessible regarding patients is increasing by the day. And we have access to a lot of this data at our fingerprints thanks to advances in technology. We should be able to draw insights from this and put data to work. The first challenge is the huge amount of data that cannot be processed by mere mortals. Machine learning when combined with data can help us make better decisions, transform healthcare, save lives and improve the quality of life.

Machine learning can transform EHR

EHR generally relies on technology that was built decades ago.

The only way they’ve been able to keep with changing times is by making sudden improvements. Every time the need arises for this EHRs have to added in new changes and retrofitted innovations which is a bit of a challenge.

EHRs primarily store data but there’s so much more they could do. For example just storing data isn’t helping anyone. Sending that data to the right person at the right time can make all the difference.

If we were able to use machine learning along with it we can pre populate patient information, add reminders, survey trends and usage patterns to create opportunities and precise delivery.

Machine learning helps solve problems that appear in real time helping improve chances of survival for the patient and reducing documentation time and EHR fatigue while focusing on activities that pay rich dividends.

New venues for Precision Medicine

Precision medicine is what everyone wants from doctors to patients. With machine learning and access to each person’s individual genetic makeup, environmental factors, lifestyle, history of disease and other such factors accurate diagnosis and personalized treatment is on the horizon.

Genomics and precision medicine brought to the hospital can offer a new platform for trial and research.

This way genomic data models can be applied and new ones broached upon to deliver right information to the practitioner.

With this the way healthcare is approached for a variety of diseases and conditions say cancer, diabetes, schizophrenia and others will get a new radical shift.

This knowledge will be available for future generations and inform research.

Predictive Modelling

Machine learning can analyze data on patients are levels never seen before. This helps to quickly turn data into actionable insight.

Stripped of information that links data to patient this delinked data can be analyzed and structured to study broader problems that plague the human condition. This will enable and empower us to solve meaningful challenges in healthcare.

Comparing data pieces like blood sugar levels, BMI, age and risk factors we can model appropriate treatments.

We can understand why there’s an opioid crisis and probably chance upon effective treatments for diseases like AIDS and diabetes.

We could also predict which patients could be at risk for what kind of diseases.

Concluding thoughts

The capabilities that machine learning presents us with didn’t exist 5 to 10 years ago. Computers are now capable of handling a sea of data that could never be processed by humans alone. Such endeavours and data processing marathons will revolutionize how we approach healthcare, save lives and treat people.