Recommendations to improve the usability of drug-drug interaction clinical decision support alerts

Payne, Thomas H.; Hines, Lisa; Chan, Raymond C. K.; Hartman, Seth; Kapusnik-Uner, Joan; Russ, Alissa L.; Chaffee, Bruce W.; Hartman, Christian; Tamis, Victoria; Galbreth, Brian; Glassman, Peter; Phansalkar, Shobha; Sijs, Heleen van der; Gephart, Sheila M.; Mann, Gordon; Strasberg, Howard R.; Grizzle, Amy J.; Brown, Mary Louise; Kuperman, Gilad J.; Steiner, Chris A.; Sullins, Amanda K.; Ryan, H. M.; Wittie, Michael; Malone, Daniel C.

doi:10.1093/jamia/ocv011

Cited by 168 publications

(174 citation statements)

References 41 publications

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“…The above issues reflect the lack of gold standard and consensus that have been called for by several author groups [21,22,41,42]. While implementation of the suggestions made to standardization of evaluation of evidence and clinical consequences may resolve some of the discrepancies the importance of transparency cannot be underestimated [21][22][23]43].…”

Section: Discussionmentioning

confidence: 99%

Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review

Kongsholm

Nielsen

Damkier

2015

Eur J Clin Pharmacol

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Section: Discussionmentioning

confidence: 99%

Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review

Kongsholm

Nielsen

Damkier

2015

Eur J Clin Pharmacol

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“…In fact, the problem of alert fatigue, in addition to creating user dissatisfaction, is that it causes users to ignore serious alerts and endangers patient safety [89,90]. Hence, many articles [89,91] focused on CDSS-related studies in other areas on assessing the quality of alerts provided in the CDSS and evaluating the effectiveness of these alerts. Effectiveness assessment of alerts designed in chemotherapy prescription CDSS is highly significant for continuous improvement of these systems and user satisfaction enhancement.…”

Section: Discussionmentioning

confidence: 99%

Specifications of Computerized Provider Order Entry and Clinical Decision Support Systems for Cancer Patients Undergoing Chemotherapy: A Systematic Review

et al. 2018

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Objective: Chemotherapy medication errors are catastrophic. The prescription phase in the chemotherapy process plays a key role in the creation of medication errors, and therefore the use of computerized physician order entry (CPOE) and clinical decision support system (CDS) systems is recommended to reduce chemotherapy medication errors. The purpose of this study was to carry out a systematic review on the specifications of the CPOE and CDS systems for chemotherapy prescription. Materials and Methods: A systematic review on articles published in English up to September 22, 2017, using the 3 databases PubMed, Embase, and Medline was conducted. Those articles that focused on the specifications of CPOE and CDSS in chemotherapy prescription were included in this review. Findings: Of the 2,471 articles identified, 58 articles met the inclusion criteria and were included in this study. Specifications related to chemotherapy CPOE systems were categorized into the following 6 groups: automation and facilitation of the chemotherapy prescription phase, hospital workflow support, documentation and reporting, drug safety, information security, and system communications. The specifications of chemotherapy CDSS were also divided into 4 categories: embedding chemotherapy protocols, automated dose calculations and adjustment, providing alerts/reminders at the time of prescribing, and guiding or asking the user to complete the important prescription parameters. In 12 articles, the chemotherapy prescription CDSS were designed and evaluated independently of the CPOE; 45 articles provided prescription chemotherapy CDSS as part of the CPOE system, and in 1 article CPOE was introduced with no CDSS. Conclusion: In complicated settings such as chemotherapy, simplification of the processes is more imperative. The use of chemotherapy CPOE, which includes specifications for helping the medical staff with their workload, encourages the professionals to use such systems and increases the likelihood for success of these systems.

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“…The survey provided optional responses for resources, keywords, and study types that were identified from the team's prior research on the information needs of professionals who search and synthesize PDDI evidence [11]. Questions about PDDI information search were organized according to the range of PDDI topics important for clinical decision making recently published by Payne et al [29]. Two of the survey questions provided open-ended responses that participants could use to provide additional comments as well as share search terms that they find useful.…”

Section: Methodsmentioning

confidence: 99%

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey (Preprint)

Grizzle¹,

Horn²,

Collins³

et al. 2018

Preprint

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Background: Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well-documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated. Objective: The purpose of this study was to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence. Methods: A convenience sample of 70 drug information experts, including compendia editors, knowledge base vendors, and clinicians were invited via email to complete a survey on identifying PDDI evidence. An online survey was created and included questions addressing: 1) development and conduct of searches; 2) resources used e.g., databases, compendia, search engines etc.; 3) types of keywords used to search for specific PDDI information; 4) study types included and excluded in searches; and 5) search terms used. Search strategy questions focused on six topics of PDDI information: 1) that a PDDI exists; 2) seriousness; 3) clinical consequences; 4) management options; 5) mechanism; and 6) health outcomes. Results: Twenty participants (response rate 29%) completed the survey. The majority (85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists with 60% having over ten years' experience. Over half (55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants develop (90%) and conduct (95%) search strategies without librarian assistance. PubMed (100%) and Google Scholar (55%) are most commonly searched for articles, followed by Google Web Search (35%) and EMBASE (15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20; 45%), Micromedex (8/20; 40%), Drugs@FDA (17/20; 85%), and DailyMed (13/20; 65%). Facts and Comparisons was the most commonly used compendia (8/20; 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify existence of PDDIs and determine their mechanism than for the other four attributes (seriousness, consequences, management, and health outcomes). With respect to types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not. Conclusions:The results suggest that drug information experts use various keyword strategies and various database and web resources depending on the PDDI evidence they are seeking to identify.

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Recommendations to improve the usability of drug-drug interaction clinical decision support alerts

Abstract: DDI clinical decision support alerts need major improvements. We provide recommendations for healthcare organizations and IT vendors to improve the clinician interface of DDI alerts, with the aim of reducing alert fatigue and improving patient safety.

Cited by 168 publications

References 41 publications

Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review

Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review

Specifications of Computerized Provider Order Entry and Clinical Decision Support Systems for Cancer Patients Undergoing Chemotherapy: A Systematic Review

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey (Preprint)

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