Case StudyAssistance Publique-Hôpitaux de Paris (AP-HP)

Assistance publique – Hôpitaux de Paris optimises staffing by predicting emergency room admission rates by hour and day using machine learning

Using TAP the open source AI platform powered by Intel to analyse data from internal and external sources such as hospital admissions records, emergency arrival rates can be predicted accurately. This has led to better staffing and resource allocation at the hospital.

Context

To better allocate resources and serve patients better, a predictive AI platform was employed by the AP-HP hospital in Paris

The Project

"Hospitals in Paris are trialling Big Data and machine learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes."

Data

"At four of the hospitals which make up the Assistance Publique-Hôpitaux de Paris (AP-HP), data from internal and external sources – including 10 years’ worth of hospital admissions records has been crunched to come up with day and hour-level predictions of the number of patients expected through the doors."

Results

Results not yet available. However expansion to 44 hospitals is planned indicating positive results.

Related Use Cases

Use Case

Predict potential staffing requirements to optimise resourcing

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