A systematic approach to optimize electronic health record medication alerts in a health system

Bhakta, Sunny B.; Colavecchia, A. Carmine; Haines, Linda; Varkey, Divya; Garey, Kevin W.

doi:10.1093/ajhp/zxz012

Cited by 14 publications

(31 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…systems (36), mainly because they are given the option to override those alerts with mild or insignificant clinical outcomes (35,36). Overriding alerts may become a habit for many pharmacists; in some clinical settings, the override rates could be as high as 71.9%, which may rise the tendency of a pharmacist or physician to override a harmful PDDI (37). Hence, the burning issue of drug interaction won't be easily fixed by the implementation of the computerized system, a multi-facetted approach is necessary to improve drug safety.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Physician's Knowledge of Potential Drug-Drug Interactions: An Online Survey in China

et al. 2021

View full text Add to dashboard Cite

Background: Drug interactions are the most common preventable cause of adverse drug reaction, which may result in drug toxicity or undesired therapeutic effect with harmful outcomes to patients. Given the rising use of combination therapies, the main objectives of this study were to estimate the degree to which physicians can identify potential drug-drug interactions (PDDIs) correctly and to describe the common source of information used by physicians when they need to check PDDIs.Methods: A cross-sectional survey utilizing a self-administered online questionnaire was conducted among physicians in China. Participants were asked to classify 20 drug pairs as “no interaction,” “may be used together with monitoring,” “contraindication,” and “not sure.” We also collected data on the physician's source of information and altitude toward the PDDIs. An ordinary least square regression model was performed to investigate the potential predictors of PDDI knowledge.Results: Eligible questionnaires were obtained from 618 physicians. The respondents classified correctly 6.7 out of 20 drug pairs, or 33.4% of the drug interactions investigated. The number of drug pairs recognized by respondents was ranged from 0 to 16. The percentage of physicians who recognized specific drug pairs ranged from 8.3% for no interactions between conjugated estrogens and raloxifene, to 64.0% for the interaction between dopamine and phenytoin. When the respondents want to check PDDI information, the most commonly used source of information was package inserts (n = 572, 92.6%), followed by the Internet or mobile Apps (n = 424, 68.6%), consultation with clinical pharmacists (n = 384, 62.1%), medical textbooks (n = 374, 60.5%), knowledge base in Chinese (n = 283, 45.8%), and other physicians (n = 366, 59.2%). In the multiple regression analysis, the significant predictors of a higher number of recognized drug pairs were years of practice and altitudes toward PDDIs.Conclusion: In this online survey accessing physician's ability to detect PDDIs, less than half of the drug pairs were recognized, indicating unsatisfactory level of knowledge about the clinically significant drug interactions. Continuing education and accessible electronic database can help physicians detecting PDDIs and improve drug safety.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessment of Physician's Knowledge of Potential Drug-Drug Interactions: An Online Survey in China

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Several methods are used by hospitals to monitor and evaluate alert effectiveness, including the establishment of review committees consisting of pharmacists and doctors [ 28 - 30 ], development of visual analytic dashboards [ 13 ], and collection of end user feedback [ 31 ]. A key finding from this study was that no participating hospital had a systematic program in place to gather data on the effectiveness of medication alerts in their CPOE system.…”

Section: Discussionmentioning

confidence: 99%

A Tool for Evaluating Medication Alerting Systems: Development and Initial Assessment

Zheng¹,

Dort²,

Marcilly³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

Background It is well known that recommendations from electronic medication alerts are seldom accepted or acted on by users. Key factors affecting the effectiveness of medication alerts include system usability and alert design. Thus, human factors principles that apply knowledge of human capabilities and limitations are increasingly used in the design of health technology to improve the usability of systems. Objective This study aims to evaluate a newly developed evidence-based self-assessment tool that allows the valid and reliable evaluation of computerized medication alerting systems. This tool was developed to be used by hospital staff with detailed knowledge of their hospital’s computerized provider order entry system and alerts to identify and address potential system deficiencies. In this initial assessment, we aim to determine whether the items in the tool can measure compliance of medication alerting systems with human factors principles of design, the tool can be consistently used by multiple users to assess the same system, and the items are easy to understand and perceived to be useful for assessing medication alerting systems. Methods The Tool for Evaluating Medication Alerting Systems (TEMAS) was developed based on human factors design principles and consisted of 66 items. In total, 18 staff members recruited across 6 hospitals used the TEMAS to assess their medication alerting systems. Data collected from participant assessments were used to evaluate the validity, reliability, and usability of the TEMAS. Validity was assessed by comparing the results of the TEMAS with those of prior in-house evaluations. Reliability was measured using Krippendorff α to determine agreement among assessors. A 7-item survey was used to determine usability. Results The participants reported mostly negative (n=8) and neutral (n=7) perceptions of alerts in their medication alerting system. However, the validity of the TEMAS could not be directly tested, as participants were unaware of any results from prior in-house evaluations. The reliability of the TEMAS, as measured by Krippendorff α, was low to moderate (range 0.26-0.46); however, participant feedback suggests that individuals’ knowledge of the system varied according to their professional background. In terms of usability, 61% (11/18) of participants reported that the TEMAS items were generally easy to understand; however, participants suggested the revision of 22 items to improve clarity. Conclusions This initial assessment of the TEMAS allowed the identification of its components that required modification to improve usability and usefulness. It also revealed that for the TEMAS to be effective in facilitating a comprehensive assessment of a medication alerting system, it should be completed by a multidisciplinary team of hospital staff from both clinical and technical backgrounds to maximize their knowledge of systems.

show abstract

“…Most studies selected for this systematic review were conducted in the United States (n = 4) [21,22,23,24]. The remaining studies were performed in various countries, including South Korea (n = 2) [25,26], Japan (n = 1) [27], Israel (n = 2) [28, 29], Germany (n = 1) [30], Switzerland (n = 1) [31], Belgium (n = 1) [32], Spain (n = 1) [33], Italy (n = 1) [34], Sweden (n = 1) [35], and the Kingdom of Saudi Arabia (n = 1) [36] (Table 1).…”

Section: Characteristics Of the Studiesmentioning

confidence: 99%

Physicians' acceptance of drug-drug interaction alerts from clinical decision support systems: A systematic review and meta-analysis

Felisberto,

Lima,

Celuppi

et al. 2023

Preprint

View full text Add to dashboard Cite

Background To reduce the risk of adverse drug events related to drug-drug interaction (DDI), healthcare systems worldwide are developing and implementing electronic health records with clinical decision support systems (CDSS). Primary studies have demonstrated that despite being useful, most of the alerts generated by these systems are ignored by prescribers. To provide more information about this issue, we conducted a systematic review and meta-analysis on the prevalence of DDI alerts generated by CDSS and alert overrides by physicians. Materials and methods The search strategy was implemented by applying the terms and MeSH headings and conducted in the MEDLINE/PubMed, EMBASE, Web of Science, Scopus, LILACS, and Google Scholar databases. Blinded reviewers screened 1873 records and 86 full studies, and 16 articles were included for analysis. Quality assessment was performed by JBI critical appraisal tools, and the meta-analysis was performed using the R software. Results The overall prevalence of alert generated by CDSS was 13% (CI95% 5–24%, p-value < 0.0001, I^2 = 100%), and the overall prevalence of alert override by physicians was 90% (CI95% 85–95%, p-value < 0.0001, I^2 = 100%). Only 2 of the 16 studies showed moderate or low methodological quality. Discussion This systematic review presents data obtained from primary studies showing a high rate of alert overrides, even after CDSS adjustments that significantly reduced the number of alerts. After analyzing the articles included in this review, it was clear that the CDSS alerts physicians about potential DDI should be developed with a focus on the user experience, thus increasing their confidence and satisfaction, which may increase patient clinical safety.

show abstract

A systematic approach to optimize electronic health record medication alerts in a health system

Cited by 14 publications

References 13 publications

Assessment of Physician's Knowledge of Potential Drug-Drug Interactions: An Online Survey in China

Assessment of Physician's Knowledge of Potential Drug-Drug Interactions: An Online Survey in China

A Tool for Evaluating Medication Alerting Systems: Development and Initial Assessment

Physicians' acceptance of drug-drug interaction alerts from clinical decision support systems: A systematic review and meta-analysis

Contact Info

Product

Resources

About