Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data

TL;DR

This paper presents machine learning models that detect line-access artifacts in noisy waveform data to automate documentation, reduce errors, and improve patient safety in critical care settings.

Contribution

It introduces real-time ML classifiers capable of identifying line-access artifacts within noisy waveform data, enhancing clinical documentation accuracy.

Findings

ML classifiers achieved high accuracy in artifact detection

Real-time implementation demonstrated at a children's hospital

Potential to reduce documentation errors and improve safety

Abstract

Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a distinct artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children's hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety.