Decision Support Analytics Workgroup

Weight Entry Error Detection

Description

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.

Weight Entry Error Detection

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Clinical Rationale/Problem Solved

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.

Potential Impact

Aim 1: Prevents overdosing because of bad weight data.

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

DSAW Investigators

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

Collaborators

Yizhao Ni, Pieter-Jan VanCamp

Grants

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

Publications

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