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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedIqbal AR, Parau CA, Kazi S
Identifying electronic medication administration record (eMAR) usability issues from patient safety event reports.
This study investigated the contribution of usability challenges associated with the electronic medication administration record (eMAR) to medication errors using patient safety event reports (PSEs). The authors analyzed free-text descriptions of 849 medication-related PSEs selected from 2.3 million reports. Specific health IT components, usability challenge categories, and nuanced usability themes that contributed to each PSE were identified by coders. Usability challenges included workflow support, alerting, and display/visual clutter.
AHRQ-funded; HS025136.
Citation: Iqbal AR, Parau CA, Kazi S .
Identifying electronic medication administration record (eMAR) usability issues from patient safety event reports.
Jt Comm J Qual Patient Saf 2021 Dec;47(12):793-801. doi: 10.1016/j.jcjq.2021.09.004..
Keywords: Electronic Prescribing (E-Prescribing), Health Information Technology (HIT), Medication, Medical Errors, Patient Safety
King 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