By Dr. David Rozier
In a hospital emergency room rapid decision making happens every day. It seems the perfect fit for data analytics platforms which can cull information in ways that can offer insights to medical staff. But the emergency room often is ignored when it comes to data science because:
- Data collected during difficult emergency situations is frequently of low quality for a computer.
- Given the life and death nature of emergency medical situations, the reliability of data analysis must be high even though it is working on low-quality data.
- Rule-based systems often are ineffective because of the high diversity of situations.
I’m part of a team of machine learning experts at the Xerox Research Centre Europe in Grenoble, France. We’ve taken up the challenge, and we’re working with a French teaching hospital’s Accident and Emergency (A&E) department. We’re exploring how data analysis can help doctors and nurses make fast, dependable, on-the-fly decisions in order to treat their patients.
Test Case: Accidental Poisoning
Each year, 25,000 children under five-years old are rushed to A&E departments with suspected poisoning, according to the United Kingdom’s Child Accident Prevention Trust. Moreover, a 2015 report from Safe Kids Worldwide points out that nearly half of the 1.34 million calls to poison centers in the United States are for children, and are related to medicine poisoning.
Using anonymized medical data from 200 patients admitted for medicine poisoning in France, learning how to forecast what sort of care each new case will require.
The challenge is difficult because of the very nature of poisoning. The patient is often unconscious or delirious, and the medical staff rarely knows what caused the poisoning. Our team set out to help the staff determine if a patient would likely recover naturally, or if there was a high probability the patient’s condition would worsen and require admittance to the intensive care unit.
If data analytics can help with this real-time sorting, the hospital can increase the quality of patient care and improve how resources are spent on patients.
The initial results are very encouraging. We’ve used data to identify what class of medicine could have caused the poisoning, which helps the medical staff decide on the appropriate level of care.
We’ll expand on these preliminary results in 2016 with a larger study that uses more of our analytics algorithms.
We view this work as an opportunity to use clinical innovation to solve some of the most painful – and costly – issues in society.
David Rozier, an expert in artificial intelligence and scientific computing, is the Healthcare Technology Transfer group manager within XRCE’s Advanced Development Lab.