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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 3 of 3 Research Studies DisplayedKing CR, Abraham J, Fritz BA
Predicting self-intercepted medication ordering errors using machine learning.
Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, the investigators described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. In this paper, they updated the analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors.
AHRQ-funded; HS025443.
Citation: King CR, Abraham J, Fritz BA .
Predicting self-intercepted medication ordering errors using machine learning.
PLoS One 2021 Jul 14;16(7):e0254358. doi: 10.1371/journal.pone.0254358..
Keywords: Medication, Medical Errors, Adverse Drug Events (ADE), Adverse Events, Medication: Safety, Patient Safety, Electronic Prescribing (E-Prescribing), Health Information Technology (HIT)
Abraham J, Galanter WL, Touchette D
Risk factors associated with medication ordering errors.
This study’s goal was to collect data on “voided” orders in computerized order entry systems for medication to 1) identify the nature and characteristics of medication ordering errors; 2) investigate the risk factors associated with these errors and; 3) explore potential strategies to mitigate these risk factors. Data was collected using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. During the 16-month study period 1074 medication orders were voided, with 842 being true medication errors. A total of 22% reached the patient, with at least a single administration, but without causing patient harm. Interviews were conducted on 355 voided orders (33%). Errors were associated with multiple factors not just a single risk factor. The causal contributors included a combination of technological-, cognitive-, environment-, social-, and organization-level factors.
AHRQ-funded; HS025443.
Citation: Abraham J, Galanter WL, Touchette D .
Risk factors associated with medication ordering errors.
J Am Med Inform Assoc 2021 Jan 15;28(1):86-94. doi: 10.1093/jamia/ocaa264..
Keywords: Medication: Safety, Electronic Prescribing (E-Prescribing), Medication: Safety, Medication, Medical Errors, Adverse Drug Events (ADE), Adverse Events, Risk, Health Information Technology (HIT), Patient Safety
Adelman JS, Applebaum JR, Southern WN
Risk of wrong-patient orders among multiple vs singleton births in the neonatal intensive care units of 2 integrated health care systems.
Researchers assessed the risk of wrong-patient orders among multiple-birth infants and singletons receiving care in the NICU and examined the proportion of wrong-patient orders between multiple-birth infants and siblings (intrafamilial errors) and between multiple-birth infants and nonsiblings (extrafamilial errors). They found that multiple-birth status in the NICU is associated with significantly increased risk of wrong-patient orders compared with singleton-birth status. Strategies to reduce this risk include using given names at birth, changing from temporary to given names when available, and encouraging parents to select names for multiple births before they are born when acceptable to families.
AHRQ-funded; HS024538.
Citation: Adelman JS, Applebaum JR, Southern WN .
Risk of wrong-patient orders among multiple vs singleton births in the neonatal intensive care units of 2 integrated health care systems.
JAMA Pediatr 2019 Oct 10;173(10):979-85. doi: 10.1001/jamapediatrics.2019.2733..
Keywords: Newborns/Infants, Intensive Care Unit (ICU), Adverse Drug Events (ADE), Adverse Events, Medication: Safety, Medication, Patient Safety, Electronic Prescribing (E-Prescribing), Health Information Technology (HIT)