How hospitals in Paris are using Big Data in practice
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.
The system allows doctors, nurses and hospital administration staff to forecast visit and admission rates for the next 15 days. This means extra staff can be drafted in when high numbers of visitors are expected, leading to reduced waiting times for patients and better quality of care.
The technical details
The system is built on the open source Trusted Analytics Platform (TAP) – which was chosen for the task due to its capacity for ingesting and crunching large amounts of data. As well as the hospital’s internal data, several external datasets such as weather, public holidays and flu patterns were tapped.The result is a web browser-based interface designed to be used by staff who are untrained in data science.
The core of the analytics work involves using time series analysis techniques – looking for ways in which patterns in the data can be used to predict the admission rates at different times. Machine learning is employed to determine which algorithms provide the best indicator of future trends, when they are fed data from the past.
Lessons learned will undoubtedly prove valuable for the group’s next Big Data project – building a data warehouse to store all of its clinical data in a form that can be interrogated by common techniques such as Python or R algorithms.
Ideas and insights you can steal
With the cost of providing healthcare increasing at more than the rate of GDP in every developed country, smart, intelligent systems like AP-HP’s are likely to play an important part in the future of healthcare. By more accurately predicting the demand for services, waste can be cut, and patient care can become more efficient.
Reducing waste and increasing efficiency is something that most companies in most industries can aspire to – from predicting customer numbers in order to plan staffing, to predicting the popularity of products and services at various times of the year, and many more applications. Predictive modelling can help you do all this and more.
You can read more about how organisations are using Big Data to drive success in Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results.
Bernard Marr is a bestselling author, keynote speaker, and advisor to companies and governments. He has worked with and advised many of the world's best-known organisations. LinkedIn has recently ranked Bernard as one of the top 10 Business Influencers in the world (in fact, No 5 - just behind Bill Gates and Richard Branson). He writes on the topics of intelligent business performance for various publications including Forbes, HuffPost, and LinkedIn Pulse. His blogs and SlideShare presentation have millions of readers.