BACKGROUND:The purpose of this study is to assess the rates of perioperative medication errors (MEs) and adverse drug events (ADEs) as percentages of medication administrations, to evaluate their ...root causes, and to formulate targeted solutions to prevent them.
METHODS:In this prospective observational study, anesthesia-trained study staff (anesthesiologists/nurse anesthetists) observed randomly selected operations at a 1,046-bed tertiary care academic medical center to identify MEs and ADEs over 8 months. Retrospective chart abstraction was performed to flag events that were missed by observation. All events subsequently underwent review by two independent reviewers. Primary outcomes were the incidence of MEs and ADEs.
RESULTS:A total of 277 operations were observed with 3,671 medication administrations of which 193 (5.3%; 95% CI, 4.5 to 6.0) involved a ME and/or ADE. Of these, 153 (79.3%) were preventable and 40 (20.7%) were nonpreventable. The events included 153 (79.3%) errors and 91 (47.2%) ADEs. Although 32 (20.9%) of the errors had little potential for harm, 51 (33.3%) led to an observed ADE and an additional 70 (45.8%) had the potential for patient harm. Of the 153 errors, 99 (64.7%) were serious, 51 (33.3%) were significant, and 3 (2.0%) were life-threatening.
CONCLUSIONS:One in 20 perioperative medication administrations included an ME and/or ADE. More than one third of the MEs led to observed ADEs, and the remaining two thirds had the potential for harm. These rates are markedly higher than those reported by retrospective surveys. Specific solutions exist that have the potential to decrease the incidence of perioperative MEs.
Adverse Drug Events in Ambulatory Care Gandhi, Tejal K; Weingart, Saul N; Borus, Joshua ...
New England journal of medicine/The New England journal of medicine,
04/2003, Volume:
348, Issue:
16
Journal Article
Peer reviewed
Open access
In this study of four adult primary care practices, 25 percent of patients who received a prescription had an adverse drug event. No events were fatal or life-threatening, but approximately 4 percent ...of patients had a serious adverse event. Many adverse events could have been prevented if a different medication had been chosen or could have been ameliorated by discontinuation of the drug when symptoms related to it developed.
In this study, 25 percent of patients had an adverse drug event.
Adverse drug-related events, defined as injuries due to drugs, occur frequently among inpatients.
1
–
4
In one study, 6.5 percent of hospitalized patients had an adverse drug event, and 28 percent of the events were preventable.
5
Adverse drug events often lead to hospital admission. A meta-analysis
6
estimated that in 1994 more than 1 million Americans were hospitalized because of adverse drug events, accounting for 4.7 percent of all admissions. Even though most prescribing occurs in outpatient settings, much less is known about outpatient adverse drug events than about inpatient events. Annual estimates of the proportion of outpatients with an adverse drug . . .
To define the types and numbers of inpatient clinical decision support alerts, measure the frequency with which they are overridden, and describe providers' reasons for overriding them and the ...appropriateness of those reasons.
We conducted a cross-sectional study of medication-related clinical decision support alerts over a 3-year period at a 793-bed tertiary-care teaching institution. We measured the rate of alert overrides, the rate of overrides by alert type, the reasons cited for overrides, and the appropriateness of those reasons.
Overall, 73.3% of patient allergy, drug-drug interaction, and duplicate drug alerts were overridden, though the rate of overrides varied by alert type (P < .0001). About 60% of overrides were appropriate, and that proportion also varied by alert type (P < .0001). Few overrides of renal- (2.2%) or age-based (26.4%) medication substitutions were appropriate, while most duplicate drug (98%), patient allergy (96.5%), and formulary substitution (82.5%) alerts were appropriate.
Despite warnings of potential significant harm, certain categories of alert overrides were inappropriate >75% of the time. The vast majority of duplicate drug, patient allergy, and formulary substitution alerts were appropriate, suggesting that these categories of alerts might be good targets for refinement to reduce alert fatigue.
Almost three-quarters of alerts were overridden, and 40% of the overrides were not appropriate. Future research should optimize alert types and frequencies to increase their clinical relevance, reducing alert fatigue so that important alerts are not inappropriately overridden.
To evaluate methods of identifying patients with systemic sclerosis (SSc) using International Classification of Diseases, Tenth Revision (ICD-10) codes (M34*), electronic health record (EHR) ...databases and organ involvement keywords, that result in a validated cohort comprised of true cases with high disease burden.
We retrospectively studied patients in a healthcare system likely to have SSc. Using structured EHR data from January 2016 to June 2021, we identified 955 adult patients with M34* documented 2 or more times during the study period. A random subset of 100 patients was selected to validate the ICD-10 code for its positive predictive value (PPV). The dataset was then divided into a training and validation sets for unstructured text processing (UTP) search algorithms, two of which were created using keywords for Raynaud's syndrome, and esophageal involvement/symptoms.
Among 955 patients, the average age was 60. Most patients (84%) were female; 75% of patients were White, and 5.2% were Black. There were approximately 175 patients per year with the code newly documented, overall 24% had an ICD-10 code for esophageal disease, and 13.4% for pulmonary hypertension. The baseline PPV was 78%, which improved to 84% with UTP, identifying 788 patients likely to have SSc. After the ICD-10 code was placed, 63% of patients had a rheumatology office visit. Patients identified by the UTP search algorithm were more likely to have increased healthcare utilization (ICD-10 codes 4 or more times 84.1% vs 61.7%, p < .001), organ involvement (pulmonary hypertension 12.7% vs 6% p = .011) and medication use (mycophenolate use 28.7% vs 11.4%, p < .001) than those identified by the ICD codes alone.
EHRs can be used to identify patients with SSc. Using unstructured text processing keyword searches for SSc clinical manifestations improved the PPV of ICD-10 codes alone and identified a group of patients most likely to have SSc and increased healthcare needs.
There have been growing concerns about the impact of drug allergy alerts on patient safety and provider alert fatigue. The authors aimed to explore the common drug allergy alerts over the last 10 ...years and the reasons why providers tend to override these alerts.
Retrospective observational cross-sectional study (2004-2013).
Drug allergy alert data (n = 611,192) were collected from two large academic hospitals in Boston, MA (USA).
Overall, the authors found an increase in the rate of drug allergy alert overrides, from 83.3% in 2004 to 87.6% in 2013 (P < .001). Alarmingly, alerts for immune mediated and life threatening reactions with definite allergen and prescribed medication matches were overridden 72.8% and 74.1% of the time, respectively. However, providers were less likely to override these alerts compared to possible (cross-sensitivity) or probable (allergen group) matches (P < .001). The most common drug allergy alerts were triggered by allergies to narcotics (48%) and other analgesics (6%), antibiotics (10%), and statins (2%). Only slightly more than one-third of the reactions (34.2%) were potentially immune mediated. Finally, more than half of the overrides reasons pointed to irrelevant alerts (i.e., patient has tolerated the medication before, 50.9%) and providers were significantly more likely to override repeated alerts (89.7%) rather than first time alerts (77.4%, P < .001).
These findings underline the urgent need for more efforts to provide more accurate and relevant drug allergy alerts to help reduce alert override rates and improve alert fatigue.
Background Food allergy prevalence is reported to be increasing, but epidemiological data using patients’ electronic health records (EHRs) remain sparse. Objective We sought to determine the ...prevalence of food allergy and intolerance documented in the EHR allergy module. Methods Using allergy data from a large health care organization's EHR between 2000 and 2013, we determined the prevalence of food allergy and intolerance by sex, racial/ethnic group, and allergen group. We examined the prevalence of reactions that were potentially IgE-mediated and anaphylactic. Data were validated using radioallergosorbent test and ImmunoCAP results, when available, for patients with reported peanut allergy. Results Among 2.7 million patients, we identified 97,482 patients (3.6%) with 1 or more food allergies or intolerances (mean, 1.4 ± 0.1). The prevalence of food allergy and intolerance was higher in females (4.2% vs 2.9%; P < .001) and Asians (4.3% vs 3.6%; P < .001). The most common food allergen groups were shellfish (0.9%), fruit or vegetable (0.7%), dairy (0.5%), and peanut (0.5%). Of the 103,659 identified reactions to foods, 48.1% were potentially IgE-mediated (affecting 50.8% of food allergy or intolerance patients) and 15.9% were anaphylactic. About 20% of patients with reported peanut allergy had a radioallergosorbent test/ImmunoCAP performed, of which 57.3% had an IgE level of grade 3 or higher. Conclusions Our findings are consistent with previously validated methods for studying food allergy, suggesting that the EHR's allergy module has the potential to be used for clinical and epidemiological research. The spectrum of severity observed with food allergy highlights the critical need for more allergy evaluations.
Health IT can play a major role in improving patient safety. Computerized physician order entry with decision support can alert providers to potential prescribing errors. However, too many alerts can ...result in providers ignoring and overriding clinically important ones.
To evaluate the appropriateness of providers' drug-drug interaction (DDI) alert overrides, the reasons why they chose to override these alerts, and what actions they took as a consequence of the alert.
A cross-sectional, observational study of DDI alerts generated over a three-year period between January 1st, 2009, and December 31st, 2011.
Primary care practices affiliated with two Harvard teaching hospitals. The DDI alerts were screened to minimize the number of clinically unimportant warnings.
A total of 24,849 DDI alerts were generated in the study period, with 40% accepted. The top 62 providers with the highest override rate were identified and eight overrides randomly selected for each (a total of 496 alert overrides for 438 patients, 3.3% of the sample).
Overall, 68.2% (338/496) of the DDI alert overrides were considered appropriate. Among inappropriate overrides, the therapeutic combinations put patients at increased risk of several specific conditions including: serotonin syndrome (21.5%, n=34), cardiotoxicity (16.5%, n=26), or sharp falls in blood pressure or significant hypotension (28.5%, n=45). A small number of drugs and DDIs accounted for a disproportionate share of alert overrides. Of the 121 appropriate alert overrides where the provider indicated they would "monitor as recommended", a detailed chart review revealed that only 35.5% (n=43) actually did. Providers sometimes reported that patients had already taken interacting medications together (15.7%, n=78), despite no evidence to confirm this.
We found that providers continue to override important and useful alerts that are likely to cause serious patient injuries, even when relatively few false positive alerts are displayed.
To estimate the national cost of ADEs resulting from inappropriate medication-related alert overrides in the U.S. inpatient setting.
We used three different regression models (Basic, Model 1, Model ...2) with model inputs taken from the medical literature. A random sample of 40 990 adult inpatients at the Brigham and Women's Hospital (BWH) in Boston with a total of 1 639 294 medication orders was taken. We extrapolated BWH medication orders using 2014 National Inpatient Sample (NIS) data.
Using three regression models, we estimated that 29.7 million adult inpatient discharges in 2014 resulted in between 1.02 billion and 1.07 billion medication orders, which in turn generated between 75.1 million and 78.8 million medication alerts, respectively. Taking the basic model (78.8 million), we estimated that 5.5 million medication-related alerts might have been inappropriately overridden, resulting in approximately 196 600 ADEs nationally. This was projected to cost between $871 million and $1.8 billion for treating preventable ADEs. We also estimated that clinicians and pharmacists would have jointly spent 175 000 hours responding to 78.8 million alerts with an opportunity cost of $16.9 million.
These data suggest that further optimization of hospitals computerized provider order entry systems and their associated clinical decision support is needed and would result in substantial savings. We have erred on the side of caution in developing this range, taking two conservative cost estimates for a preventable ADE that did not include malpractice or litigation costs, or costs of injuries to patients.
Abstract
Objective
The study sought to determine frequency and appropriateness of overrides of high-priority drug-drug interaction (DDI) alerts and whether adverse drug events (ADEs) were associated ...with overrides in a newly implemented electronic health record.
Materials and Methods
We conducted a retrospective study of overridden high-priority DDI alerts occurring from April 1, 2016, to March 31, 2017, from inpatient and outpatient settings at an academic health center. We studied highest-severity DDIs that were previously designated as “hard stops” and additional high-priority DDIs identified from clinical experience and literature review. All highest-severity alert overrides (n = 193) plus a stratified random sample of additional overrides (n = 371) were evaluated for override appropriateness, using predetermined criteria. Charts were reviewed to identify ADEs for overrides that resulted in medication administration. A chi-square test was used to compare ADE rate by override appropriateness.
Results
Of 16 011 alerts presented to providers, 15 318 (95.7%) were overridden, including 193 (87.3%) of the highest-severity DDIs and 15 125 (95.8%) of additional DDIs. Override appropriateness was 45.4% overall, 0.5% for highest-severity DDIs and 68.7% for additional DDIs. For alerts that resulted in medication administration (n = 423, 75.0%), 29 ADEs were identified (6.9%, 5.1 per 100 overrides). The rate of ADEs was higher with inappropriate vs appropriate overrides (9.4% vs 4.3%; P = .038).
Conclusions
The override rate was nearly 90% for even the highest-severity DDI alerts, indicating that stronger suggestions should be made for these alerts, while other alerts should be evaluated for potential suppression.
Background
Drug-drug interaction (DDI) alerts in electronic health records (EHRs) can help prevent adverse drug events, but such alerts are frequently overridden, raising concerns about their ...clinical usefulness and contribution to alert fatigue.
Objective
To study the effect of conversion to a commercial EHR on DDI alert and acceptance rates.
Design
Two before-and-after studies.
Participants
3277 clinicians who received a DDI alert in the outpatient setting.
Intervention
Introduction of a new, commercial EHR and subsequent adjustment of DDI alerting criteria.
Main Measures
Alert burden and proportion of alerts accepted.
Key Results
Overall interruptive DDI alert burden increased by a factor of 6 from the legacy EHR to the commercial EHR. The acceptance rate for the most severe alerts fell from 100 to 8.4%, and from 29.3 to 7.5% for medium severity alerts (
P
< 0.001). After disabling the least severe alerts, total DDI alert burden fell by 50.5%, and acceptance of Tier 1 alerts rose from 9.1 to 12.7% (
P
< 0.01).
Conclusions
Changing from a highly tailored DDI alerting system to a more general one as part of an EHR conversion decreased acceptance of DDI alerts and increased alert burden on users. The decrease in acceptance rates cannot be fully explained by differences in the clinical knowledge base, nor can it be fully explained by alert fatigue associated with increased alert burden. Instead, workflow factors probably predominate, including timing of alerts in the prescribing process, lack of differentiation of more and less severe alerts, and features of how users interact with alerts.
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