Clinical Rationale/Problem Solved

Aim 1: Medication administration errors are among the most common types of medical errors

Aim 2: Are the algorithms feasible in other environments and settings, and what customization is needed?

Aim 3: Smart infusion pumps are a source for new types of administration errors

Potential Impact

Aim 1: Reduction of administration errors with high-risk medications.

Aim 2: Prove that the algorithms are generalizable depending on workflows and data of the setting.

Aim 3: More robust detection and mitigation of medication administration errors.

DSAW Investigators

Eric Kirkendall (co-PI)

Collaborators

Kristin Melton (co-PI), Yizhao Ni, Todd Lingren, Jareen Meinzen-Derr, Katie Walsh, Matt Leonard,  (UC) Vivek Narendren, (UC) Nishant Gupta, (UC) Dan Schauer

Grants

2015-2019 NIH/NLM Grant #1R01LM012230-01 (Peer-reviewed)
“Improving Intensive Care Medication Safety through EHR-based Algorithms.”
Principal Investigator: Eric Kirkendall, MD, MBI, Kristin Melton, MD

NIH/NICHHD Grant #1R21HD072883-01 (Peer-reviewed)
“EHR-based patient safety: Automated error detection in neonatal intensive care unit”
Principal Investigator: Imre Solti, MD, PhD
Role: Principal Investigator

Publications

Ni Y, Lingren T, Hall ES, Leonard M, Melton K, Kirkendall ES. Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.J Am Med Inform Assoc. 2018 May 1;25(5):555-563.

 Li Q, Kirkendall ES, Hall ES, Ni Y, Lingren T, Kaiser M, Lingren N, Zhai H, Solti I, Melton K. Automated detection of medication administration errors in neonatal intensive care.  J Biomed Inform. 2015 Oct;57:124-33.

 Li Q, Melton K, Lingren T, Kirkendall ES, Hall E, Zhai H, Ni Y, Kaiser M, Stoutenborough L, Solti I. Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care. J Am Med Inform Assoc. 2014 Sep-Oct;21(5):776-84.