The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; none of the 12 sources its claims cite is checked for support yet.
Drug interaction alert override rates in the Meaningful Use era: No evidence of progress
Open accessAbstract
Summary: Background: Interruptive drug interaction alerts may reduce adverse drug events and are required for Stage I Meaningful Use attestation. For the last decade override rates have been very high. Despite their widespread use in commercial EHR systems, previously described interventions to improve alert frequency and acceptance have not been well studied. Objectives: (1) To measure override rates of inpatient medication alerts within a commercial clinical decision support system, and assess the impact of local customization efforts. (2) To compare override rates between drug-drug interaction and drug-allergy interaction alerts, between attending and resident physicians, and between public and academic hospitals. (3) To measure the correlation between physicians’ individual alert quantities and override rates as an indicator of potential alert fatigue. Methods: We retrospectively analyzed physician responses to drug-drug and drug-allergy interaction alerts, as generated by a common decision support product in a large teaching hospital system. Results: (1) Over four days, 461 different physicians entered 18,354 medication orders, resulting in 2,455 visible alerts; 2,280 alerts (93%) were overridden. (2) The drug-drug alert override rate was 95.1%, statistically higher than the rate for drug-allergy alerts (90.9%) (p < 0.001). There was no significant difference in override rates between attendings and residents, or between hospitals. (3) Physicians saw a mean of 1.3 alerts per day, and the number of alerts per physician was not significantly correlated with override rate (R2 = 0.03, p = 0.41). Conclusions: Despite intensive efforts to improve a commercial drug interaction alert system and to reduce alerting, override rates remain as high as reported over a decade ago. Alert fatigue does not seem to contribute. The results suggest the need to fundamentally question the premises of drug interaction alert systems. Citation: Bryant AD, Fletcher GS, Payne TH. Drug interaction alert override rates in the Meaningful Use era: No evidence of progress. Appl Clin Inf 2014; 5: 802–813 http://dx.doi.org/10.4338/ACI-2013-12-RA-0103
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TL;DR
In a 96-hour retrospective audit at two UW Medicine teaching hospitals using a commercial Cerner/Multum CDS, 93% of 2,455 physician-visible drug interaction alerts were overridden, with drug-drug alerts overridden more often than drug-allergy alerts (95.1% vs 90.9%). Despite local reclassification efforts, override rates remained as high as historical reports, and alert quantity was not correlated with override rate.
Key findings
- Over four days, 461 physicians entered 18,354 medication orders, generating 2,455 visible alerts; 2,280 alerts (93%) were overridden.
- Drug-drug alert override rate was 95.1%, significantly higher than drug-allergy alerts at 90.9% (p < 0.001); there was no significant difference between attendings and residents (p = 0.11) or between hospitals (p = 0.25).
- Physicians saw a mean of 1.3 alerts per day; individual alert quantity was not significantly correlated with override rate (R² = 0.03, p = 0.41).
- Exact allergy matches were 123/1,302 (9%) of allergy alerts, and 94/123 (76%) of these exact-match alerts were overridden; antibiotic allergy alerts had the highest compliance among drug-allergy classes (121/172, 70% overridden).
Methods
- Retrospective review of major medication alerts at UW Medical Center and Harborview Medical Center from Monday, June 10, 2013 00:00 to Thursday, June 13, 2013 23:59 (96 hours), using Cerner Millennium with the Multum knowledge base; only major interactions generated interruptive physician alerts.
- Alerts were included if they occurred in the study period, were handled by a physician, and occurred on an inpatient; there were no exclusion criteria. Records were filtered to MD, DO, DDS, or MBBS credentials, and coincident alerts in the same window were counted as a single override in the main analysis.
- Override rates across categories were compared with Pearson’s chi-squared tests; alert quantity and override rate were correlated with simple linear regression and a 2-tailed F test; significance was α = 0.05; analysis used Microsoft Excel.
Limitations
- Stated: the study examined only medication alert override rates and associated metadata, over a short time span, within a single vendor’s CDS; it did not assess alert or override appropriateness, review adverse events, or examine behavior outside the alert window, and could not determine whether alert fatigue or other factors increased inappropriate overrides. The authors also note that some physicians may override alerts but later cancel orders, leading to falsely high estimates of noncompliance, and that high baseline override rates may have masked differences due to alert fatigue or other variability.
- Evident from the text: the findings come from one health system’s two hospitals over four days, are limited to physicians, and do not include patient outcomes or independent appropriateness review; override reasons were coded and most physicians selected the first option, which may limit interpretation of override intent.
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26 Own result · 9 Not checked yet · 9 Own interpretation · 6 Own method · 3 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Opioids accounted for the majority of allergy alert triggers; drug-drug alert triggers were more diverse.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Physicians override cross-reaction alerts more often than exact allergy matches, and likely view the former with a healthy skepticism that is only enforced by daily false alarms.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Antibiotic allergy alerts had the highest compliance rate.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most (76% or 94/123) of these exact allergy alerts were overridden.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Individual override rates varied widely, even among physicians with identical alert quantities.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Over four days, 461 different physicians entered 18,354 medication orders, resulting in 2,455 visible alerts; 2,280 alerts (93%) were overridden.”
- Also stated in: § 4. Results · plain
“During the four day audit period providers entered 43,287 medication orders, but only 18,354 were entered directly by physicians.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § 4. Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Physicians triggered a median of 3 alerts over the 4 day period, with a mean of 1.3 alerts per day, while entering an average of 10 medication orders per day.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The four day audit period captured 2,455 distinct alerts, involving 461 distinct physicians, for an overall alerting rate of 13.4% (2,455 alerts per 18,354 orders)”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was no significant difference in override rates between the two hospital sites (p = 0.25), or between attending and resident physicians (p = 0.11).”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fewer than 5% of individuals viewed more than 4 alerts per day.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 11
- Probably not a substantial claim. 10
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the drug-drug alert override rate was significantly higher than that for drug-allergy alerts (95.1% vs. 90.9%,p < 0.001)”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“About 20% of all alerts occurred as multiples within the same alert window.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A similar review of the allergy cross-reactivity rules before implementation found that most were appropriate.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.2 Knowledge base customization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Override rates for drug-allergy alerts were similarly high among all classes, with the notable exception of antibiotics.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“All interactions were reviewed before the implementation of CDS, causing 65% of all major interactions to be reclassified to lower levels [15].”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.2 Knowledge base customization
What the sources say
- Not checked yetcites [15] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Most of our allergy alerts were triggered by narcotic allergies, or by cross-reactions of unknown significance [29,30].”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
What the sources say
- Not checked yetcites [29] Abookire SA, Teich JM, Sandige H, Paterno MD, Martin MT, Kuperman GJ, Bates DW. Improving allergy alerting in a computerized physician order entry system. Proc AMIA Symp 2000: 2-6
- Not checked yetcites [30] source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, the olanzapine-lorazepam drug interaction alert occurred 92 times and was overridden every time.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 4. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Similar override rates of over 90% have been found in drug dose alerts [21].”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
What the sources say
- Not checked yetcites [21] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This continuous 96 hour order entry period provided adequate data and excluded dates of resident and faculty turnover.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.3 Design and scope
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: The four-day audit period is representative of typical inpatient medication ordering and alerting at the study hospitals.
In the paper
“The panel uses the operational classification scheme described by Hansten and Horn to reassign major drug-drug interactions to lower severity levels, and thus decrease interruptive alerting [14].”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.2 Knowledge base customization
What the sources say
- Not checked yetcites [14] Hansten, 2001, J Am Pharm Assoc source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the override of multiple alerts represented a single decision, these coincident alerts were included as a single override in the main analysis.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.4 Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The frequent overrides of most alert types including exact allergy matches, and the often inappropriate (unmatched to the alert) override reasons suggest that physicians dismiss alerts without assessing the specific risk of each alert.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“While the overall risk of ADEs is low, our very high override rates could increase the absolute number of inappropriate overrides, raising the ADE risk [5,28].”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This study was approved by the University of Washington Human Subjects Division.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.4 Analysis
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Because of these confounding factors, we focused on physicians alone to assess the impact of alert reclassification.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.4 Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“The lack of correlation between alert quantity and override rate also supports prior evidence that reducing the number of alerts will not appreciably increase physician acceptance or reduce overrides [23].”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
What the sources say
- Not checked yetcites [23] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A panel of physicians, pharmacists, and information technology staff has met monthly since 2008 to review all major drug-drug interaction alerts.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.2 Knowledge base customization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The study period fell at the end of the academic year to ensure that all providers had several months experience with the order entry system.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 3.3 Design and scope
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, our experience illustrates the substantial obstacles when trying to customize a commercial product to local needs.”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 6. Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“As Hayward et al. describe, alerts appear”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > 5. Discussion
What the sources say
- Not checked yetcites [24] source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Override rates are a valid measure of clinical decision support acceptance and utility.
“We are the first to report a lack of improvement in decision support acceptance (as measured by overrides) after Meaningful Use requirements took effect.”
The paper relies on this without support.
The four-day audit period is representative of typical inpatient medication ordering and alerting at the study hospitals.
“This continuous 96 hour order entry period provided adequate data and excluded dates of resident and faculty turnover.”
The paper relies on this without support.
The study's commercial CDS system is similar to those used in many U.S. hospitals.
“We demonstrated this lack of improvement within a popular commercial decision support system, similar to those used in many hospitals nationwide.”
The paper relies on this without support.
The local customization efforts represent a strong effort to improve CDS acceptance.
“University of Washington made a strong effort to improve CDS and its acceptance based on suggestions in the literature using local expertise to modify the default alerts within the constraints of our commercial system.”
The paper relies on this without support.
Historical override rates reported in prior studies are comparable to the current study's rates.
“Our current rates are at or above the rates in previous studies, including those at our regional VA hospital [7,8].”
The authors state this as an assumption.
If alert fatigue contributes to overrides, physicians with higher alert quantities will have higher override rates.
“Further, we hypothesized that physicians who saw higher quantities of alerts would have higher override rates, as predicted by the theory of alert fatigue.”
The paper relies on this without support.
The coded override reason selected by a physician accurately reflects the physician's reason for overriding the alert.
“Most overriding physicians entered the reason”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“High override rates of 56.3%-95.7% have already been described for DDI alerts [13, 21, 27–32].”
From Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey (2022) · cites this paper as [21]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Optimal alert volume is unknown, but alerts fire so frequently that most are ignored, with prior research demonstrating rates of ignoring as high as 96%, almost entirely defeating their purpose.”
From Qualitative Study of Inpatient Hospital Medicine Interruptive Alerts and Clinical Decision Support (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The inherent design of our site’s EHR makes interruptive alerts one of the easiest and least expensive forms of CDS to implement, even though they are known to be disruptive and often ineffective. 5 15 16”
From Qualitative Study of Inpatient Hospital Medicine Interruptive Alerts and Clinical Decision Support (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Bryant and colleagues also recently found that alert volume was not correlated with override rate [24].”
From Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system (2017) · cites this paper as [24]
The cited source says what the claim attributes to it.
In the citing paper
“Our study results are in agreement with previous reports.”
From Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey (2022) · cites this paper as [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“Total 2455 1153 1302 123 1179 480 1975 1200 1255 % of all alerts 100 47 53 9 48 20 80 49 51”
§ Drug interaction alert override rates in the Meaningful Use era No evidence of progress > Interaction type
Reported 9 · recomputed 5.010183
Citations
Cites
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The Impact of Computerized Physician Order Entry on Medication Error Prevention
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Overriding of Drug Safety Alerts in Computerized Physician Order Entry
van der Sijs, H., Aarts, J., Vulto, A., & Berg, M. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1809 · Cited by 1,117
Drug-Drug Interactions Among Elderly Patients Hospitalized for Drug Toxicity
Juurlink, D. N., Mamdani, M., Kopp, A., Laupacis, A., & Redelmeier, D. A. (2003) · JAMA · doi:10.1001/jama.289.13.1652 · Cited by 766
Medication-related clinical decision support in computerized provider order entry systems: a review
Kuperman, G. J., Bobb, A., Payne, T. H., Avery, A. J., Gandhi, T. K., Burns, G., Classen, D. C., & Bates, D. W. (2007) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2170 · Cited by 763
Self-citationPhysicians' Decisions to Override Computerized Drug Alerts in Primary Care
Weingart, S. N., Toth, M., Sands, D. Z., Aronson, M. D., Davis, R. B., & Phillips, R. S. (2003) · Archives of Internal Medicine · doi:10.1001/archinte.163.21.2625 · Cited by 487
Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care
Shah, N. R., Seger, A. C., Seger, D. L., Fiskio, J. M., Kuperman, G. J., Blumenfeld, B., Recklet, E. G., Bates, D. W., & Gandhi, T. K. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1868 · Cited by 353
Absence of Cross-Reactivity between Sulfonamide Antibiotics and Sulfonamide Nonantibiotics
Strom, B. L., Schinnar, R., Apter, A. J., Margolis, D. J., Lautenbach, E., Hennessy, S., Bilker, W. B., & Pettitt, D. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa022963 · Cited by 349
Tiering Drug-Drug Interaction Alerts by Severity Increases Compliance Rates
Paterno, M. D., Maviglia, S. M., Gorman, P. N., Seger, D. L., Yoshida, E., Seger, A. C., Bates, D. W., & Gandhi, T. K. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2808 · Cited by 263
Phansalkar, S., van der Sijs, H., Tucker, A. D., Desai, A. A., Bell, D. S., Teich, J. M., Middleton, B., & Bates, D. W. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001089 · Cited by 239
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with the HITEC Investigators, Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0430-8 · Cited by 709
Schwartz, P. J., & Woosley, R. L. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.12.063 · Cited by 329
“Unfortunately, the beneficial impact of these CDSS has been limited because in 75% to 95% of cases, physicians and pharmacists override or ignore the alerts (50,52,69), and no CDSS has yet been found to have sufficient impact on prescribing to reduce the”
BackgroundClinical Decision Support: a 25 Year Retrospective and a 25 Year Vision
Sittig, D. F., Wright, A. T., & Middleton, B. (2016) · Yearbook of Medical Informatics · doi:10.15265/iys-2016-s034 · Cited by 287
“Given this variation, it is no surprise that many clinicians view CDS as arising at an inopportune time in their workflow [219, 220], and many CDS alerts are simply ignored or overridden [221-224].”
BackgroundElectronic health record alerts for acute kidney injury: multicenter, randomized clinical trial
Wilson, F. P., Martin, M., Yamamoto, Y., Partridge, C., Moreira, E., Arora, T., Biswas, A., Feldman, H., Garg, A. X., Greenberg, J. H., et al. (2021) · BMJ · doi:10.1136/bmj.m4786 · Cited by 200
Medication-related clinical decision support alert overrides in inpatients
Nanji, K. C., Seger, D. L., Slight, S. P., Amato, M. G., Beeler, P. E., Her, Q. L., Dalleur, O., Eguale, T., Wong, A., Silvers, E. R., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx115 · Cited by 179
Missing clinical and behavioral health data in a large electronic health record (EHR) system
Madden, J. M., Lakoma, M. D., Rusinak, D., Lu, C. Y., & Soumerai, S. B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw021 · Cited by 181
Jacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177
“For example, studies in medical informatics found that when DST alerts arise at inopportune times (from clinicians’ vantage), the alerts become ignored or overridden [1, 35, 43].”
BackgroundZheng, W. Y., Richardson, L. C., Li, L., Day, R. O., Westbrook, J. I., & Baysari, M. T. (2017) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-017-2357-5 · Cited by 176
Colicchio, T. K., Cimino, J. J., & Fiol, G. D. (2019) · Journal of Medical Internet Research · doi:10.2196/13313 · Cited by 163
“Given the complexity and high cost involved in implementing a commercial EHR, health care organizations are unlikely to change an EHR vendor anytime soon, causing a saturation of the US EHR market.”
Hussain, M., Reynolds, T. L., & Zheng, K. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz095 · Cited by 141
“12,13 The results have been mixed.”
Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review
Poly, T. N., Islam, M. M., Yang, H.-C., & Li, Y.-C. (. (2020) · JMIR Medical Informatics · doi:10.2196/15653 · Cited by 118
“1 DDI June 10-13, 2013 PTHcc ROS Bryant et al [28]”
BackgroundKane‐Gill, S. L., O’Connor, M. F., Rothschild, J. M., Selby, N. M., McLean, B. A., Bonafide, C. P., Cvach, M. M., Hu, X., Konkani, A., Pelter, M. M., et al. (2017) · Critical Care Medicine · doi:10.1097/ccm.0000000000002580 · Cited by 121
Segal, G., Segev, A., Brom, A., Lifshitz, Y., Wasserstrum, Y., & Zimlichman, E. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz135 · Cited by 116
“12,13 Moreover, most systems focus on identifying the potential errors at the prescribing event but fail to identify ADEs that occur in ongoing active prescriptions due to the frequent changes, which are characteristic of patients during their in-hospital stay.”
BackgroundOlakotan, O. O., & Mohd Yusof, M. (2021) · Health Informatics Journal · doi:10.1177/14604582211007536 · Cited by 101
“3,38,42,45,86,99 Therefore, CDSS alerts must be designed to provide clinicians with patient-specific assessment and recommendation (i.e. appropriate alert) during task execution to improve the quality of care and clinical outcomes.”
BackgroundCredibleMeds.org: What does it offer?
Woosley, R. L., Black, K., Heise, C. W., & Romero, K. (2017) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2017.07.010 · Cited by 99
Clinical Decision Support Systems for Drug Allergy Checking: Systematic Review
Légat, L., Laere, S. V., Nyssen, M., Steurbaut, S., Dupont, A. G., & Cornu, P. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8206 · Cited by 98
Alert override as a habitual behavior – a new perspective on a persistent problem
Baysari, M. T., Tariq, A., Day, R. O., & Westbrook, J. I. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw072 · Cited by 85
“For example, a detailed retrospective review of alerts triggered by physicians over a 4-day period at two US primary teaching hospitals showed that even highly relevant alerts (eg, exact allergy matches) were frequently overridden.(8) In a study where alert content was modified to include more relevant patient informa…”
BackgroundRecommendations for selecting drug–drug interactions for clinical decision support
Tilson, H. H., Hines, L. E., McEvoy, G. K., Weinstein, D. M., Hansten, P. D., Matuszewski, K. A., le Comte, M., Higby-Baker, S., Hanlon, J. T., Pezzullo, L., et al. (2016) · American Journal of Health-System Pharmacy · doi:10.2146/ajhp150565 · Cited by 81
McEvoy, D. S., Sittig, D. F., Hickman, T.-T., Aaron, S., Ai, A., Amato, M., Bauer, D. W., Fraser, G. M., Harper, J., Kennemer, A., et al. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw114 · Cited by 75
Kawamoto, K., & McDonald, C. J. (2020) · Annals of Internal Medicine · doi:10.7326/m19-0875 · Cited by 72
“However, clinicians have ignored up to 93% of alerts (15), and they complain about excessive rates of false-positive reminders (16–18).”
BackgroundOlakotan, O. O., & Yusof, M. M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103453 · Cited by 77
Trinkley, K. E., Kahn, M. G., Bennett, T. D., Glasgow, R. E., Haugen, H. A., Kao, D. P., Kroehl, M., Lin, C., Malone, D. C., & Matlock, D. D. (2020) · Journal of Medical Internet Research · doi:10.2196/19676 · Cited by 56
“Up to 95% of CDS tools are dismissed [5], 52%-66% improve process outcomes such as appropriate drug selection, and only 21%-43% lead to improvements in clinical outcomes [6-9].”
BackgroundArtificial intelligence and machine learning for clinical pharmacology
Ryan, D. K., Maclean, R., Balston, A. J., Scourfield, A., Shah, A. D., & Ross, J. (2023) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15930 · Cited by 67
Kim, H. S., McCarthy, D. M., Hoppe, J. A., Courtney, D. M., & Lambert, B. L. (2017) · Academic Emergency Medicine · doi:10.1111/acem.13273 · Cited by 55
Precision Population Medicine in Primary Care: The Sanford Chip Experience
Christensen, K. D., Bell, M., Zawatsky, C. L. B., Galbraith, L. N., Green, R. C., Hutchinson, A. M., Jamal, L., LeBlanc, J. L., Leonhard, J. R., Moore, M. M., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.626845 · Cited by 54
Van De Sijpe, G., Quintens, C., Walgraeve, K., Van Laer, E., Penny, J., De Vlieger, G., Schrijvers, R., De Munter, P., Foulon, V., Casteels, M., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01783-z · Cited by 54
A comparison of potential psychiatric drug interactions from six drug interaction database programs
Monteith, S., & Glenn, T. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.03.041 · Cited by 48
Automation to optimise physician treatment of individual patients: examples in psychiatry
Bauer, M., Monteith, S., Geddes, J., Gitlin, M. J., Grof, P., Whybrow, P. C., & Glenn, T. (2019) · The Lancet Psychiatry · doi:10.1016/s2215-0366(19)30041-0 · Cited by 44
McDaniel, R. B., Burlison, J. D., Baker, D. K., Hasan, M., Robertson, J., Hartford, C. M., Howard, S. C., Sablauer, A., & Hoffman, J. M. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv144 · Cited by 47
Muylle, K. M., Gentens, K., Dupont, A. G., & Cornu, P. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104393 · Cited by 45
Wan, P. K., Satybaldy, A., Huang, L., Holtskog, H., & Nowostawski, M. (2020) · Journal of Medical Internet Research · doi:10.2196/22013 · Cited by 43
“Bryant et al [38] pointed out that despite intensive efforts to reduce irrelevant alerts of commercial systems, overriding rates remain as high as reported over a decade ago.”
BackgroundScott, I. A., Sullivan, C. M., & Staib, A. N. (2018) · Australian Health Review · doi:10.1071/ah17153 · Cited by 47
Cornu, P., Steurbaut, S., Gentens, K., de Velde, R. V., & Dupont, A. G. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.05.005 · Cited by 37
“Because accreditation and legislation requirements in different countries boost the use of EHRs and CDSS, further attention should go to the development of CDSS with high enough specificity so that they are useful and support health care professionals [43,51,52].”
BackgroundKovačević, M., Kovačević, S. V., Radovanović, S., et al. (2019) · Current Medical Research and Opinion · doi:10.1080/03007995.2019.1647021 · Cited by 34
Heringa, M., Floor‐Schreudering, A., Tromp, P. C., Smet, P. A. G. M. D., & Bouvy, M. L. (2015) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.3915 · Cited by 31
Bykov, K., Schneeweiß, S., Glynn, R. J., Mittleman, M. A., & Gagne, J. J. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1376 · Cited by 30
“Electronic prescribing and pharmacy software generate an excessive number of DDI alerts, most of which are routinely overridden, although not always appropriately.(5,6) There are growing concerns that the current DDI decision support systems are failing to achieve their goal of reducing DDI- associated patient harm(4,…”
BackgroundA systematic review of drug allergy alert systems
de Manzanos, M. L. F., Leache, L., Gastaminza, G., Idoate, A., & Ortega, A. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104673 · Cited by 32
Mulder-Wildemors, L. G. M., Heringa, M., Floor‐Schreudering, A., Jansen, P. A. F., & Bouvy, M. L. (2019) · Drugs & Aging · doi:10.1007/s40266-019-00728-y · Cited by 30
Orenstein, E. W., Kandaswamy, S., Muthu, N., Chaparro, J. D., Hagedorn, P. A., Dziorny, A. C., Moses, A., Hernandez, S., Khan, A. N., Huth, H. B., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab179 · Cited by 27
Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis
Chien, S.-C., Chen, Y.-L., Chien, C., Chin, Y.-P., Yoon, C. H., Chen, C.-Y., Yang, H., & Li, Y. (2022) · Healthcare · doi:10.3390/healthcare10040601 · Cited by 30
Overriding Drug-Drug Interaction Alerts in Clinical Decision Support Systems: A Scoping Review
Zapata, L. V., Subbian, V., Boyce, R. D., Hansten, P. D., Horn, J. R., Gephart, S. M., Romero, A. V., & Malone, D. C. (2022) · Studies in health technology and informatics · doi:10.3233/shti220101 · Cited by 31
Murad, D. A., Tsugawa, Y., Elashoff, D. A., Baldwin, K., & Bell, D. S. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac191 · Cited by 28
“Visit is with patient’s PCP, n (%) No 14 706 (26) Yes 40 943 (74) Appointment length, n (%) 15 min or less 21 809 (39) 20–30 min 27 943 (50) Greater than 30 min 5897 (11) Encounter year, n (%) 2017 2205 (4) 2018 14 127 (25) 2019 21 969 (39) 2020 14 759 (27) 2021 2589 (5) Competing BPA alert count, n (%) 0 21 203 (38)…”
Schneider, K. L., Kastenmüller, K., Weckbecker, K., Bleckwenn, M., Böhme, M., & Stingl, J. (2018) · Drugs & Aging · doi:10.1007/s40266-018-0550-6 · Cited by 26
Felisberto, M., Lima, G. D. S., Celuppi, I. C., Fantonelli, M. D. S., Zanotto, W. L., Dias de Oliveira, J. M., Mohr, E. T. B., dos Santos, R. A., Scandolara, D. H., Cunha, C. L., et al. (2024) · Health Informatics Journal · doi:10.1177/14604582241263242 · Cited by 28
“Most studies demonstrated high methodological quality, 21,23 – 27, 32,33,35,”
InfluentialGrizzle, A. J., Horn, J. R., Collins, C. J., Schneider, J., Malone, D. C., Stottlemyer, B. A., & Boyce, R. D. (2018) · Journal of Medical Internet Research · doi:10.2196/11182 · Cited by 25
“These factors may contribute to the >90% override rate consistently reported for clinicians [4].”
Wang, L., Blackley, S. V., Blumenthal, K. G., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa042 · Cited by 24
“The study method was comprised of 5 steps: (1) allergen consolidation, (2) refinement of reaction value set, (3) reaction reconciliation from free-text entries, (4) development of dynamic reaction picklist,”
MethodPrevalence of Clinically Significant Drug-Drug Interactions Across US Children’s Hospitals
Antoon, J. W., Hall, M., Herndon, A. C., Carroll, A. R., Ngo, M.-L. D., Freundlich, K. L., Stassun, J. C., Frost, P. A., Johnson, D., Chokshi, S. B., et al. (2020) · PEDIATRICS · doi:10.1542/peds.2020-0858 · Cited by 26
Drug–Drug–Gene Interactions in Cardiovascular Medicine
Asiimwe, I. G., & Pirmohamed, M. (2022) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s338601 · Cited by 28
User-centered design of multi-gene sequencing panel reports for clinicians
Cutting, E. M., Banchero, M., Beitelshees, A. L., Cimino, J. J., Fiol, G. D., Gürses, A. P., Hoffman, M. A., Jeng, L. J. B., Kawamoto, K., Kelemen, M. D., et al. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.07.014 · Cited by 22
“Participants agreed that the way the GMA Genetic Test Report tab was structured to provide optional access to information resources along with concise information summaries could help to overcome issues with alert fatigue (or desensitization to frequent alerts) [38].”
BackgroundA Late Attempt to Involve End Users in the Design of Medication-Related Alerts: Survey Study
Baysari, M. T., Zheng, W. Y., Dort, B. A. V., Reid-Anderson, H., Gronski, M., & Kenny, E. (2020) · Journal of Medical Internet Research · doi:10.2196/14855 · Cited by 21
“Despite international efforts to improve medication-related alerts, alert override rates remain as high as they were over a decade ago [6].”
BackgroundKnight, A. M., Maygers, J., Foltz, K. A., John, I. S., Yeh, H. C., & Brotman, D. J. (2019) · Applied Clinical Informatics · doi:10.1055/s-0039-3400447 · Cited by 22
Hammar, T., Lidström, B., Petersson, G., Gustafson, Y., & Eiermann, B. (2015) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-015-0146-8 · Cited by 21
“high despite substantial alert optimization [44, 45].”
Ethics, Medicine, and Information Technology
Goodman, K. W. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139600330 · Cited by 40
Lower alert rates by clustering of related drug interaction alerts
Heringa, M., Siderius, H., Floor‐Schreudering, A., Smet, P. A. G. M. D., & Bouvy, M. L. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw049 · Cited by 19
“23 Therefore, there is a need for exploration of additional strategies.”
BackgroundBain, K. T., Knowlton, C. H., & Turgeon, J. (2017) · Clinics in Geriatric Medicine · doi:10.1016/j.cger.2017.01.009 · Cited by 21
Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation
Meslin, S. M. M., Zheng, W. Y., Day, R. O., Tay, E., & Baysari, M. T. (2018) · Applied Clinical Informatics · doi:10.1055/s-0038-1676039 · Cited by 20
Heringa, M., Floor‐Schreudering, A., Smet, P. A. G. M. D., & Bouvy, M. L. (2017) · Drugs & Aging · doi:10.1007/s40266-017-0497-z · Cited by 20
“%) are not uncommon in routinely used CDSSs [27–30].”
Generating Evidence of Clinical Outcomes of Drug–Drug Interactions
Bykov, K., & Gagne, J. J. (2017) · Drug Safety · doi:10.1007/s40264-016-0496-8 · Cited by 18
An Investigation of Drug–Drug Interaction Alert Overrides at a Pediatric Hospital
Humphrey, K. E., Jorina, M., Harper, M. B., Dodson, B. L., Kim, S. Y., & Ozonoff, A. (2018) · Hospital Pediatrics · doi:10.1542/hpeds.2017-0124 · Cited by 19
A systematic approach to optimize electronic health record medication alerts in a health system
Bhakta, S. B., Colavecchia, A. C., Haines, L., Varkey, D. A., & Garey, K. W. (2019) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxz012 · Cited by 20
Survey of psychiatrist use of digital technology in clinical practice
Bauer, R., Glenn, T., Monteith, S., Whybrow, P. C., & Bauer, M. (2020) · International Journal of Bipolar Disorders · doi:10.1186/s40345-020-00194-1 · Cited by 18
Monteith, S., Glenn, T., Gitlin, M. J., & Bauer, M. (2020) · Pharmacopsychiatry · doi:10.1055/a-1156-4193 · Cited by 19
“Alert fatigue remains a major issue as the majority of DDI alerts are overridden [86, 87].”
BackgroundClinician Perceptions of Timing and Presentation of Drug-Drug Interaction Alerts
Humphrey, K. E., Mirica, M., Phansalkar, S., Ozonoff, A., & Harper, M. B. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1714276 · Cited by 16
Monteith, S., & Glenn, T. (2021) · Human Psychopharmacology Clinical and Experimental · doi:10.1002/hup.2802 · Cited by 17
“Physicians override most DDI alerts, with override rates of about 55%–95% (Bryant et al., 2014; Isaac et al., 2009; Poly et al., 2020), including nearly 90% of alerts for the highest severity DDI (Edrees et al., 2020).”
BackgroundBetter specification of triggers to reduce the number of drug interaction alerts in primary care
Heringa, M., van der Heide, A., Floor‐Schreudering, A., Smet, P. A. G. M. D., & Bouvy, M. L. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.11.005 · Cited by 18
“generation to the most severe drug interactions, and context-specific alert generation) [27,28], to reach an approach customized for the specific setting.”
Cornu, P., Phansalkar, S., Seger, D. L., Cho, I., Pontefract, S. K., Robertson, A., Bates, D. W., & Slight, S. P. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.12.027 · Cited by 16
Muylle, K. M., Gentens, K., Dupont, A. G., & Cornu, P. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.104013 · Cited by 17
“The main problems are the low specificity of the alerts and their perceived lack of clinical importance [4,12,16,17].”
BackgroundWide variation and patterns of physicians’ responses to drug–drug interaction alerts
Cho, I., Lee, Y., Lee, J., & Bates, D. W. (2018) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzy102 · Cited by 16
Evaluation of Drug–Drug Interactions in Pharmacoepidemiologic Research
Chen, C., Nguyen, T. P. P., Hughes, J. E., Hennessy, S., Leonard, C. E., Miano, T. A., Douros, A., Gagne, J. J., & Bykov, K. (2025) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.70088 · Cited by 16
“This uncertainty makes DDI compendia unreliable [12–16] and leads to excessive DDI alerts in computerized provider order entry systems [17], the vast majority of which are routinely overridden [18–20].”
Bykov, K., Schneeweiß, S., Glynn, R. J., Mittleman, M. A., Bates, D. W., & Gagne, J. J. (2017) · Drug Safety · doi:10.1007/s40264-017-0556-8 · Cited by 14
Improving the specificity of drug-drug interaction alerts: Can it be done?
Reese, T., Wright, A. T., Liu, S., Boyce, R. D., Romero, A. V., Fiol, G. D., Kawamoto, K., & Malone, D. C. (2022) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxac045 · Cited by 15
Jiang, S., Mathias, P. C., Hendrix, N., Shirts, B. H., Tarczy-Hornoch, P. Z., Veenstra, D. L., Malone, D. C., & Devine, B. (2022) · The Pharmacogenomics Journal · doi:10.1038/s41397-022-00275-7 · Cited by 17
Clinician Response to Pharmacogenetic Clinical Decision Support Alerts
Lemke, L. K., Cicali, E. J., Williams, R., Nguyen, K. A., Cavallari, L. H., & Wiisanen, K. (2023) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3051 · Cited by 18
Electronic health records and patient safety: should we be discouraged?
Payne, T. H. (2015) · BMJ Quality & Safety · doi:10.1136/bmjqs-2015-004039 · Cited by 13
Self-citationOptimizing vitamin D naming conventions in computerized order entry to support high-value care
White, A. A., McKinney, C. M., Hoffman, N. G., & Sutton, P. R. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw038 · Cited by 13
Tukukino, C., & Wallerstedt, S. M. (2019) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/bcpt.13294 · Cited by 12
Role of Opioid-Involved Drug Interactions in Chronic Pain Management
Bain, K. T., & Knowlton, C. H. (2019) · Journal of Osteopathic Medicine · doi:10.7556/jaoa.2019.136 · Cited by 13
Nudging hospitals towards evidence‐based decision support for medication management
Westbrook, J. I., & Baysari, M. T. (2019) · The Medical Journal of Australia · doi:10.5694/mja2.50028 · Cited by 12
Gunn, L., Tunney, R. K., & Kelly, K. (2018) · Applied Clinical Informatics · doi:10.1055/s-0038-1626726 · Cited by 12
Baysari, M. T., Zheng, W. Y., Li, L., Westbrook, J. I., Day, R. O., Hilmer, S. N., Dort, B. A. V., Hargreaves, A., Kennedy, P. J., Monaghan, C., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-026034 · Cited by 14
“17 There is now little doubt that improving alert specificity is critical for reducing frequent interruptions to prescriber workflow (ie, too many alerts) and improving the effectiveness of computerised alerts to prevent errors.”
BackgroundMeaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework
Cánovas-Segura, B., Morales, A., Juárez, J. M., & Campos, M. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104397 · Cited by 13
Wan, Y.-K. J., Wright, M. C., McFarland, M. M., Dishman, D., Nies, M. A., Rush, A., Madaras-Kelly, K., Jeppesen, A., & Del Fiol, G. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad203 · Cited by 11
Schjøtt, J. D., & Aßmus, J. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0821-0 · Cited by 11
“A hospital study found that 93% of alerts from an electronic medical record and clinical decision system were overridden with no difference between experienced or unexperienced physicians [31].”
BackgroundModulators Influencing Medication Alert Acceptance: An Explorative Review
Bittmann, J. A., Haefeli, W. E., & Seidling, H. M. (2022) · Applied Clinical Informatics · doi:10.1055/s-0042-1748146 · Cited by 14
Colicchio, T. K., & Cimino, J. J. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1710310 · Cited by 11
Bassir, F., Varghese, S., Wang, L., Chin, Y. P., & Zhou, L. (2022) · Immunology and Allergy Clinics of North America · doi:10.1016/j.iac.2022.01.004 · Cited by 10
Bole, C. B., Nagode, K., Pišlar, M., Mrhar, A., Grabnar, I., & Vovk, T. (2023) · Medicina · doi:10.3390/medicina59020284 · Cited by 15
Karajizadeh, M., Zand, F., Vazin, A., Saeidnia, H. R., Lund, B. D., Tummuru, S. P., & Sharifian, R. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105135 · Cited by 11
Lofthus, D. M., Gadgil, J. Y., & de Lemos, J. A. (2015) · Clinical Chemistry · doi:10.1373/clinchem.2014.236406 · Cited by 11
Drug Interactions in Space: a Cause for Concern?
Berman, E., & Eyal, S. (2019) · Pharmaceutical Research · doi:10.1007/s11095-019-2649-9 · Cited by 10
Mo, Y., Karakas-Torgut, A., & Pham, A. Q. (2018) · Journal of Pharmacy Practice · doi:10.1177/0897190018788264 · Cited by 10
“Finally, previous studies suggest that override rates of DDI alerts are as high as 95%.(16) At least 223 DOAC-drug interacting alerts were overridden during the study period, but override rates were not fully investigated in this study.”
BackgroundHave Electronic Health Records Improved the Quality of Patient Care?
Krenn, L., & Schlossman, D. (2017) · PM&R · doi:10.1016/j.pmrj.2017.04.001 · Cited by 8
Colicchio, T. K., & Cimino, J. J. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104508 · Cited by 8
Habit and Automaticity in Medical Alert Override: Cohort Study
Wang, L., Goh, K. H., Yeow, A. Y. K., Poh, H. M. N., Li, K., Yeow, J. J. L., Tan, G. Y. H., & Soh, C. (2022) · Journal of Medical Internet Research · doi:10.2196/23355 · Cited by 9
“However, the ubiquitous use of such alerts has led to a significant number of alert dismissals [2-8].”
BackgroundWright, A. P., Embí, P. J., Nelson, S. D., Smith, J., Turchin, A., & Mize, D. L. E. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221119788 · Cited by 9
Health Information Technology in Long-Term Care: Potential for the Future
Little, M. O., Rantz, M., & Lynn, G. A. (2016) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2016.02.029 · Cited by 8
An Exploration of the Terminology of Clinical Cognition and Reasoning.
Cimino, J. J., Li, Z., & Weng, C. (2018) · PubMed · Cited by 8
Reese, T. J., Fiol, G. D., Morgan, K. L., Hurwitz, J. T., Kawamoto, K., Gómez-Lumbreras, A., Brown, M., Thiess, H., Vazquez, S. R., Nelson, S. D., et al. (2021) · JMIR Human Factors · doi:10.2196/28618 · Cited by 9
“Most electronic health records (EHRs) implement DDI clinical decision support functionality with underlying logic provided by drug knowledge base vendors, but DDI alerts continue to be overridden at rates as high as 90% [9-11].”
BackgroundDo user preferences align with human factors assessment scores of drug–drug interaction alerts?
Lowenstein, D., Zheng, W. Y., Burke, R. A., Kenny, E., Sandhu, A., Makeham, M., Westbrook, J. I., Day, R. O., & Baysari, M. T. (2019) · Health Informatics Journal · doi:10.1177/1460458219840210 · Cited by 7
Colicchio, T. K., Dissanayake, P. I., & Cimino, J. J. (2020) · PubMed Central · Cited by 7
Metadata
- Authors
- Bryant, A. D., Fletcher, G. S., & Payne, T. H.
- Affiliations
- University of Washington (US)
- Corresponding author
- Payne, T.H.
- Venue
- Applied Clinical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2014
- Volume
- 05
- Issue
- 03
- Pages
- 802-813
- ISSN
- 1869-0327 (ISSN-L)
- PMC
- PMC4187095
- OpenAlex
- W1155385988
- Semantic Scholar
- 030961ec3d58fe1888e09ba078dd667a6f9136e9
- Cited by
- 172
- References
- 28
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 5 · 2023: 14 · 2022: 21 · 2021: 13 · 2020: 20 · 2019: 25 · 2018: 14 · 2017: 18 · 2016: 15 · 2015: 12
- FWCI
- 27.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied Clinical Informatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
- Keywords
- drug interaction alerts, clinical decision support, alarm fatigue, meaningful use, drug-drug interactions, drug allergy alerts, hospital comparison
- MeSH terms
- Humans, Medication Errors, Washington, Incidence, Prevalence, Clinical Pharmacy Information Systems, Adverse Drug Reaction Reporting Systems, Evidence-Based Medicine, Electronic Prescribing, Pharmacovigilance, Meaningful Use, Drug-Related Side Effects and Adverse Reactions
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