Technology 3 min read

New Machine Learning Algorithm Reduces Emergency Admissions Rates

New machine learning software could help significantly reduce emergency admission wait times. | Image By VILevi | Shutterstock

New machine learning software could help significantly reduce emergency admission wait times. | Image By VILevi | Shutterstock

Using machine learning algorithms, hospitals can now significantly reduce emergency admissions.

Health AI’s power is that of any AI: predictive analysis, which is at the heart of pretty much every machine learning application.

Some hospitals in the U.S. are testing AI’s potential in many frontline and back-office tasks such as robot-assisted surgery, virtual nursing, and administrative workflow.

However, many of these issues arise after staff admit the patient into the hospital. Now, staff can use machine learning algorithms to predict their risk for a stay based on their medical history.

This strategy would be particularly effective in reducing wait times for emergency admissions.

AI Schedules Emergency Room Admissions

Before the logistical and financial burden on hospitals, unplanned emergency admissions are unpleasant experiences for patients.

Using AI systems, medical teams could avoid many emergency admissions. Using a new machine learning algorithm, researchers could significantly reduce emergency admission rates.

Researchers at the George Institute for Global Health at the University of Oxford (UK) say machine learning can analyze individuals’ health records to predict their risk of emergency admissions.

“There were over 5.9 million recorded emergency hospital admissions in the UK in 2017, and a large proportion of them were avoidable. We wanted to provide a tool that would enable healthcare workers to accurately monitor the risks faced by their patients, and as a result make better decisions around patient screening and proactive care that could help reduce the burden of emergency admissions,” said data scientist Fatemeh Rahimian who led the research.

Read More: 3 Ways AI is Helping Identify Health Risks and Disease

For the study, the team used the UK’s Clinical Practice Research Datalink database. This contained data on 4.6 million patients aged between 18 and 100 years old.

Data included “age, sex, ethnicity, socioeconomic status, family history, lifestyle factors, comorbidities, medication and marital status, as well as the time since first diagnosis, last use of the health system and latest laboratory tests.”

The wide range of variables and the incorporation of temporal information substantially improved the performance of machine learning algorithms. It particularly helped in predicting the risk of emergency admissions compared to existing models.

“Our findings show that with large datasets which contain rich information about individuals, machine learning models outperform one of the best conventional statistical models. We think this is because machine learning models automatically capture and ‘learn’ from interactions between the data that we were not previously aware of.”

Researchers said there’s still room for model improvement through the inclusion of more variables and more information about their timing.

“By deploying such models in practices, physicians would be able to accurately monitor the risk score of their patients and take the necessary actions in time to avoid unplanned admissions.”

Is this the earliest that AI can interfere in healthcare practices?

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Zayan Guedim

Trilingual poet, investigative journalist, and novelist. Zed loves tackling the big existential questions and all-things quantum.

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  1. Fraction Tech November 28 at 4:30 am GMT

    A field of Artificial intelligence that uses statistical techniques to enable computer systems to ‘learn’ from data — can be used to analyse electronic health records and predict the risk of emergency hospital admissions, a new study from The George Institute for Global Health at the University of Oxford has found. The study, of 4.6 million patients from 1985 to 2015, was conducted using linked electronic health records from the UK’s Clinical Practice Research Datalink. Whether machine learning models can lead to similarly strong improvements in risk prediction in other areas of medicine requires further research.

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