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Hitachi develops AI tech to predict hospital readmissions

Regina Pazvakavambwa
By Regina Pazvakavambwa, ITWeb portals journalist.
Johannesburg, 14 Dec 2017
The AI technology helps select appropriate patients to participate in a readmission prevention programme.
The AI technology helps select appropriate patients to participate in a readmission prevention programme.

Hitachi, in collaboration with Partners Connected Health (PCH), has developed artificial intelligence (AI) technology which can predict the risk of hospital readmissions for patients with heart failure within 30 days.

According to Hitachi, the AI technology helps select appropriate patients to participate in a readmission prevention programme following hospital discharge, and can explain the reason why patients were identified as being high risk.

It says the 30-day readmission rate is regarded as one of the important indicators in hospital management, and can carry significant penalties for hospitals.

Hitachi says the new AI technology uses deep learning to construct this prediction model.

It notes the technology is an example of explainable AI, a new term currently defined as enabling machines to explain their decisions and actions to human users, and enabling them to understand, appropriately trust and effectively manage AI tools, while maintaining a high level of prediction accuracy.

As part of a study, the Partners Connected Health Innovation team says it simulated the readmission prediction programme among heart failure patients participating in the Partners Connected Cardiac Care Program (CCCP), a remote monitoring and education programme designed to improve the management of heart failure patients at risk for hospitalisation.

The analysis showed the prediction algorithm achieved a high accuracy of approximately AUC 0.71, and can significantly reduce the number of patient readmissions, says PCH.
AUC, area under the curve, is a measure of prediction model performance with an ideal value range from 0 to 1. As a result, approximately an additional US $7 000 savings per patient per year among the cohort of CCCP patients can be expected, it adds.

"Traditional machine learning can help us predict events, but as end-users, we can't tell why the machine is predicting something a certain way, says Kamal Jethwani, MD, MPH, senior director, Partners Connected Health Innovation.

"With this innovation, doctors and nurses using the algorithm will be able to tell exactly why a certain patient is at high risk for hospital admission, and what they can do about it. We want to enable our providers to act on this information, which is a step beyond the state-of-the-art today, in terms of machine learning algorithms."

The companies say they will jointly conduct a prospective study, which evaluates the prediction programme by clinicians, and study how to integrate this within clinical workflows.

By using this new AI technology, Hitachi says it will provide solutions for the medical field, including solutions for insurance and pharmaceutical companies, emergency services, and other healthcare services where prediction-based on medical data can be utilised.

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