The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study
Abstract
Background: False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. Objective: The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. Methods: This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts. For each of the DDI pairs, 2 algorithms featuring different values for modifying factors were made. DDI alerts from the 7 drug pairs were collected over 30 days. Outcome measures included the number of DDI alerts generated before and after patient-specific algorithm application to the same patients over the same time period. Results: A total of 14 algorithms were generated, and each was evaluated by comparing the number of alerts generated by our existing, customized clinical decision support (CDS) software and the patient-specific algorithms. The CDS DDI alerting software generated an average of 185.3 alerts per drug pair over the 30-day study period. Patient-specific algorithms reduced the number of alerts resulting from the algorithms by 11.3% to 93.5%. Conclusion and Relevance: Patient-specific DDI alerting is an innovative and effective approach to reduce the number of DDI alerts, may potentially increase the appropriateness of alerts, and may decrease the potential for alert fatigue.
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Different studies have shown a substantial reduction in DDI alert burden by applying patient and context-specific factors.
“Different studies have shown a substantial reduction in DDI alert burden by applying patient and context-specific factors [14–18].”
From Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey (2022) · cites this paper as [18]
Alerts were reduced by up to 93.5% in the study of Horn and Ueng.
“Alerts were reduced by up to 93.5% in the study of Horn and Ueng [18].”
From Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey (2022) · cites this paper as [18]
Recent studies already showed the potential of including patient-specific and context-specific characteristics into DDI CDSS algorithms on reducing the DDI alert burden.
“Recent studies already showed the potential of including patient-specific and context-specific characteristics into DDI CDSS algorithms on reducing the DDI alert burden [17, 18].”
From Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey (2022) · cites this paper as [18]
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- Authors
- Horn, J., & Ueng, S.
- Affiliations
- University of Washington (US)
- Alameda Health System (US)
- Corresponding author
- Horn, John
- ORCID
- Horn, John
- Venue
- Annals of Pharmacotherapy
- Publisher
- SAGE Publications
- Published
- 2019-07-11
- Volume
- 53
- Issue
- 11
- Pages
- 1087-1092
- ISSN
- 0012-6578 (ISSN-L)
- 1060-0280
- 1542-6270
- OpenAlex
- W2957625677
- Cited by
- 26
- References
- 24
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 4 · 2022: 12 · 2021: 2 · 2020: 2
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Pharmacotherapy (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- drug-drug interactions, clinical decision support, alarm fatigue, false positive alerts, alert burden, electronic prescribing
- MeSH terms
- Drug Interactions, Humans, Pilot Projects, Decision Support Systems, Clinical, Medical Order Entry Systems, Electronic Health Records, Patient-Specific Modeling
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