Decision Support Analytics Workgroup
Weight Entry Error Detection

Weight Entry Error Detection

Aim 1: Realtime identification of erroneous weight values entered into the EHR.

Aim 2: Annotation tool for identifying bad weight data - Create training sets for machine learning, other analytical tools.

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Aim 1: Wrong weight data entered into the EHR is very hard to detect and leads to potentially catastrophic dosing errors.

Aim 2: Weight data errors are amenable to machine learning, but it can be very time consuming to build the training sets needed to employ ML.

Aim 1: Prevents overdosing because of bad weight data.

Aim 2: Cutting edge use of ML, more robust detection capabilities.

S. Andy Spooner (co-PI), Danny Wu (co-PI, UC), Eric Kirkendall (co-I), Kevin Dufendach, Josh Courter

Yizhao Ni, Pieter-Jan VanCamp

Mary K. Logan Research Award, Association for the Advancement of Medical Instrumentation (AAMI)

Hagedorn PA1, Kirkendall ES2, Kouril M3, Dexheimer JW4, Courter J5, Minich T5, Spooner SA6. Assessing Frequency and Risk of Weight Entry Errors in Pediatrics. JAMA Pediatr. 2017 Apr 1;171(4):392-393.