Machine Learning can be used to Predict Peak Medical Emergency Times

Hospital admission rates have long been something that studied to prepare staff members and facilities for peak times. With machine learning, artificial intelligence can use a series of statistical techniques to make sure computer systems can work at learning from data. This can be used to then analyze a series of electronic health records with emergency admission times. A study of these ongoing records through the University of Oxford is hoping to create a predictive model for staffing in emergency departments eventually.

By regularly analyzing health logs it’s possible that health practitioners could continuously have risk management for hospital admission times as well as for patients. Unplanned admissions are a serious cost with healthcare spending across the UK.

The predictive model could go even further with triaging patients identifying various risks and determining the types of hospital visits that could be considered avoidable. By being able to have predictive models for certain conditions and triage patients that do not face a high risk of re-admittance, it could be possible to reduce the drain on emergency departments as well.

These types of predictive models could make for better decisions from staffing as well as lead to improved decisions when it comes to triaging patients. By being able to make a clear decision on a patient’s condition based off of computer modeling, it’s possible for healthcare staff to reduce the burden on their emergency department while having clear statistics suggesting that the person will not re-admit. Better decision-making ultimately leads to a more efficient emergency department.

To back up the findings in this study there are electronic health records that have been collected from the year 1985 up to 2015 that are being examined. The wide range of data includes factors like ethnicity, family history, social-economic status, marital status, medication combinations, time since first diagnosis and more. By working within all of these variables, it’s possible to reduce risk is in the emergency department and recognize greater risks based off of models for healthcare records in any area.

The large data sets and the rich number of common nations within this data are now creating models that can be highly detailed and capable of outperforming the most any other type of machine learning algorithm that has previously looked into emergency data.

What could happen with more of the analysis into these machine learning models is a series of prevention steps. If there are major causes for concern in some of the combinations that are examined in healthcare data, this could change prescriptive models for healthcare facilities. The data provided will also have a major effect on the process of triage and more.

Of course, these models will require some further research, but as healthcare data continues to build up further and the models grow in their knowledge, they will only become more efficient and work at delivering improvements throughout peak emergency times.

Source:ย The information used in this article is fromย

Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

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