Abbie (Roth) Miller| Special to The Columbus Dispatch | USA TODAY network

Artificial intelligence, including machine learning, is everywhere these days. From news headlines to talk show monologues, it seems like everyone is talking about artificial intelligence (AI) and how it is rapidly changing the world around us.

Machine learning is a type of AI that uses computer systems that can learn and adapt without exact instructions. They use algorithms and statistical models to analyze and make inferences based on patterns in data. Many forms of AI that we use regularly, such as facial recognition, product recommendations and spam filtering, are based on machine learning.

More: Paving future solutions for congenital heart disease | Pediatric Research

At Nationwide Childrens Hospital, experts in critical care, hospital medicine, data science and informatics recently published a machine learning tool that identifies children at risk for deterioration. In hospital settings, deterioration refers to a patient getting worse and having a higher risk of morbidity or mortality.

A year and a half after the team implemented the tool, deterioration events were down 77% compared to expected rates.

The tool is called the Deterioration Risk Index (DRI). It is trained on disease-specific groups: structural heart defects, cancer and general (neither cancer nor heart defect). By training the algorithm for each subpopulation, the research team improved the accuracy of the tool.

A lot of factors, including changing lab values, medications, medical history, nurse observations and more, come together to determine a patients risk of deterioration. Because the DRI is integrated into the electronic medical record, the algorithm can take all the data and analyze it in real time. It sounds an alarm if a patient becomes high risk for deterioration, triggering the action and attention of the care team. To promote adoption of the DRI, the team integrated the tool into existing hospital emergency response workflows. When an alert sounds, the care team responds with a patient assessment and huddle at the bedside to develop a risk mitigation and escalation plan for the identified patient.

More: Genomic medicine offers diagnostic hope for people with rare diseases | Pediatric Research

Many algorithms have been developed to predict risk and improve clinical outcomes. But the majority dont make it from the computer to the clinic. According to the DRI team, collaboration and transparency were key to making the DRI work in the real world. The tool was in development for more than five years. During that time, the team met with clinical units and demonstrated the tool in its various stages of development. In those meetings, the care teams asked questions and provided feedback.

Perhaps most importantly, the tool was built with full transparency about how it works. The DRI is not a black box like some machine learning or AI tools that have made headlines recently. The team can show clinicians what data goes into the algorithm and how the algorithm evaluates it.

More: Insights lead to new guidelines for children with cerebral palsy | Pediatric Research

The DRI team has also published the full algorithm in its report in the journal Pediatric Critical Care Medicine. Using this information, other hospitals can retrain the algorithm on their own data to help improve care for children at their hospital.

This project is just one example of how machine learning and AI are showing up in health care and research. It is also a great example of how collaboration and transparency can help us make the most of these new tools.

Abbie(Roth) Miller is the managing editor for Pediatrics Nationwide and manager for science and medical content at Nationwide Childrens Hospital.

Abbie.Roth@nationwidechildrens.org

Visit link:

Using AI in electronic medical records to save the lives of children - The Columbus Dispatch