A Clinical Case of Electronic Health Record Drug Alert Fatigue: Consequences for Patient Outcome
ABSTRACT Despite advances in electronic medication order entry systems, it has been well established that clinicians override many drug allergy alerts generated by the electronic health record. The direct clinical consequences of overalerting clinicians in a pediatric setting have not been well demonstrated in the literature. We observed a patient in the PICU who experienced complications as a result of an extended series of non-evidence-based alerts in the electronic health record. Subsequently, evidence-based allergy alerting changes were made to the hospital's system. Incorporating clinical evidence in electronic drug allergy alerting systems remains challenging, especially in pediatric settings.
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ABSTRACT: Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine.Genet Med advance online publication 12 September 2013Genetics in Medicine (2013); doi:10.1038/gim.2013.127.Genetics in medicine: official journal of the American College of Medical Genetics 09/2013; DOI:10.1038/gim.2013.127 · 6.44 Impact Factor
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ABSTRACT: 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.Applied Clinical Informatics 01/2014; 5(3):802-13. DOI:10.4338/ACI-2013-12-RA-0103 · 0.39 Impact Factor
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ABSTRACT: Electronic health records (EHRs) have potential to improve quality and safety of healthcare. However, EHR users have experienced safety concerns from EHR design and usability features that are not optimally adapted for the complex work flow of real-world practice. Few strategies exist to address unintended consequences from implementation of EHRs and other health information technologies. We propose that organizations equipped with EHRs should consider the strategy of “proactive risk assessment” of their EHR-enabled healthcare system to identify and address EHR-related safety concerns. In this paper, we describe the conceptual underpinning of an EHR-related self-assessment strategy to provide institutions a foundation upon which they could build their safety efforts. With support from the Office of the National Coordinator for Health Information Technology (ONC), we used a rigorous, iterative process to develop a set of 9 self-assessment tools to optimize the safety and safe use of EHRs. These tools, referred to as the Safety Assurance Factors for EHR Resilience (SAFER) guides, could be used to self-assess safety and effectiveness of EHR implementations, identify specific areas of vulnerability, and create solutions and culture change to mitigate risks. A variety of audiences could conduct these assessments, including frontline clinicians or care teams in different practices, or clinical, quality, or administrative leaders within larger institutions. The guides use a multifaceted systems-based approach to assess risk and empower organizations to work with internal or external stakeholders (eg, EHR developers) on optimizing EHR functionality and using EHRs to drive improvements in the quality and safety of healthcare.The American journal of managed care 05/2014; 20(5):418-423. · 2.17 Impact Factor