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Data to predict patient admission rate in Paris’ hospitals​

Bernard Marr(*) explains how Big data and machine learning systems using data from the past 10 years are designed to forecast future trends of visitors expected at the Parisian hospitals (1).

Such algorithms, if proven valid, will enable a better planning of staff, which will lead to more efficient use of resources, reduced waiting time for patients and ultimately better patient care.

Surprisingly, despite French Privacy laws not allowing for as much data to be used as would have been the case in the US, the algorithms can be based on external parameters such as time of year, holidays, weather forecast, flue monitoring and work just as accurately.

The next step is to build data warehouses from which non technical staff will be able to query the data using common techniques such as Python or R algorithms, while at all times complying with the sturdy EU data governance rules.

As Marr writes “Ultimately, reducing the cost of healthcare is likely to lead to longer and happier lives for everyone.” and this can only be effectively achieved through thorough analysis of quality clinical data.

*Bernard Marr is a best-selling author & keynote speaker on business, technology and big data. His new book is Data Strategy.
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 (1).Forbes: Big Data in Healthcare: Paris Hospitals Predict Admission Rates Using Machine Learning

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