Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records.

Title

Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records.

Creator

Kirkendall Eric S; Kouril Michal; Dexheimer Judith W; Courter Joshua D; Hagedorn Philip; Szczesniak Rhonda; Li Dan; Damania Rahul; Minich Thomas; Spooner S Andrew

Publisher

Journal of the American Medical Informatics Association : JAMIA

Date

2017
2017-03

Description

Objectives: Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications. Materials and Methods: We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs. Results: Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts. Discussion: Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts. Conclusion: The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.

Subject

*Algorithms; *Medical Order Entry Systems; Adolescence; Adolescent; adverse drug event; Adverse Drug Event – Prevention and Control; Age Distribution; Algorithms; Anti-Bacterial Agents/*administration & dosage/adverse effects; Antibiotics – Administration and Dosage; Antibiotics – Adverse Effects; Child; clinical; Clinical; Computer Assisted; Computer-Assisted; CPOE; decision support systems; Decision Support Systems; Demography; Drug Overdose/diagnosis/*prevention & control; Drug Therapy; Drug-Related Side Effects and Adverse Reactions/*prevention & control; electronic health record; Electronic Health Records; electronic medical record; Electronic Order Entry; Female; Funding Source; Hospitals; Humans; Infant; Male; medical order entry system; Medication Errors – Prevention and Control; Medication Errors – Statistics and Numerical Data; Medication Errors/*prevention & control/statistics & numerical data; Newborn; Overdose – Diagnosis; Overdose – Prevention and Control; patient safety; Pediatric; Preschool; risk management; Young Adult

Rights

Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).

Pages

295–302

Issue

2

Volume

24

Citation

Kirkendall Eric S; Kouril Michal; Dexheimer Judith W; Courter Joshua D; Hagedorn Philip; Szczesniak Rhonda; Li Dan; Damania Rahul; Minich Thomas; Spooner S Andrew, “Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records.,” NEOMED Bibliography Database, accessed September 28, 2021, https://neomed.omeka.net/items/show/4198.

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