Baxter (NYSE:BAX) announced today that data shows the potential for machine learning supporting decision-making with infusion pump programming.

Deerfield, Illinois-based Baxter presented the data from a retrospective study — part of a collaboration with MedAware aimed to develop next-generation dose error reduction software — at the American Society of Health-System Pharmacists (ASHP) 2021 Midyear Clinical Meeting.

According to a news release, the study shows that machine learning and its algorithms for finding patterns in large amounts of data could improve the process during programming of smart infusion pumps, which use dose error reduction systems (DERS) to help prevent medication errors by checking programmed doses against preset limits specific to a drug.

Baxter said that developing meaningful DERS limits across all drugs and care areas and deploying changes through thousands of pumps within a hospital is challenging and requires detailed analysis and significant resources. The study sought to examine whether machine learning and artificial intelligence (AI) could inform adjustments to DERS limits using MedAware’s machine learning technology.

The study analyzed more than 3.8 million infusions performed on 20,542 Baxter infusion pumps over a 10-month period, with algorithms set to identify “outliers” that included infusions deviating from commonly programmed doses/rates for specific drugs, uncommon drug concentrations and patient weight entries outside of common weight ranges.

Baxter’s analysis found that 44,819 pump programming entries were outliers and 23% of those triggered DERS soft limits, while 52% triggered DERS hard limits. Approximately 25% of the outliers were identified through MedAware’s technology but did not trigger DERS because the programming parameters were within DERS soft limits.

With the results confirming the challenges associated with maintaining meaningful DERS limits, study investigators concluded that machine learning could help inform future collaboration with hospitals around the development of more clinically relevant DERS limits, potentially supporting increased infusion safety and reducing unnecessary alerts.

“This study shows promise around the potential to enhance patient safety by using machine learning platforms to build and maintain smart infusion drug libraries that dynamically review infusions and signal possible infusion errors,” VP of Medical Affairs for Baxter’s Medication Delivery Business Dr. Douglas M. Hansell said in the release. “Baxter is eager to further explore the use of machine learning and other digital health platforms to generate real-time insights that support individualized clinical decisions.”