Abstract:Background Increased adoption of electronic health records (EHR) with integrated clinical decision support (CDS) systems has reduced some sources of error but has led to unintended consequences including alert fatigue. The “pop-up” or interruptive alert is often employed as it requires providers to acknowledge receipt of an alert by taking an action despite the potential negative effects of workflow interruption. We noted a persistent upward trend of interruptive alerts at our institution and increasing reques… Show more
“…The invited paper by Chaparro et al, "Reducing Interruptive Alert Burden Using Quality Improvement Methodology," examines the value of managing CDS and alert fatigue using process improvement methods. 8 Another must-read is McGreevey et al's paper, "Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems," which describes the risks of alert fatigue that lead to feelings of burnout and presents actionable recommendations from four clinical informatics leaders from diverse health care organizations. 9 Lomotan et al's paper, "To Share is Human!…”
“…The invited paper by Chaparro et al, "Reducing Interruptive Alert Burden Using Quality Improvement Methodology," examines the value of managing CDS and alert fatigue using process improvement methods. 8 Another must-read is McGreevey et al's paper, "Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems," which describes the risks of alert fatigue that lead to feelings of burnout and presents actionable recommendations from four clinical informatics leaders from diverse health care organizations. 9 Lomotan et al's paper, "To Share is Human!…”
“…These types of errors could lead CDS to fire in cases where it should not, or not fire in cases where it should, which could lead users to make an error (or omission) that could lead to patient harm 9,24 and contribute to alert fatigue. 25,26 We developed a portable, effective software tool to identify these classes of logic errors. The results are easily interpretable and actionable.…”
Objective Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements.
Methods Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors.
Results Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback.
Discussion An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations.
Conclusion Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.
“…Although standardization is the foundation for promoting physician compliance, streamlining clinic flow, and improving system cost-effectiveness, [25][26][27] customization to user requirements including more refined firing criteria by subspecialty may benefit user engagement and sustainability. [28][29][30] Lack of such consideration may result in alert overlook or underutilization of referral. 31 An optimally sensitive and specific CDS system may need to be customized to accommodate each subspecialty, or even at the individual user level.…”
Section: Alert Standardization and Customizationmentioning
confidence: 99%
“…For long-term quality improvement use, the "hard stop" restriction could be eliminated to minimize workflow interruptions. 28,35 The second challenge involves the timing of the alert appearance during the patient encounter. Several studies have pointed to the importance of user satisfaction with workflow and usability as measures of effectiveness of CDS implementation.…”
The purpose of this study was to develop and evaluate an electronic health record (EHR) clinical decision support system to identify patients meeting criteria for low vision rehabilitation (LVR) referral. Methods: In this quality improvement project, we applied a user-centered design approach to develop an interactive electronic alert for LVR referral within the Johns Hopkins Wilmer Eye Institute. We invited 15 ophthalmology physicians from 8 subspecialties to participate in the design and implementation, and to provide user experience feedback. The three project phases incorporated development evaluation, feedback analysis, and system refinement. We report on the final alert design, firing accuracy, and user experiences. Results: The alert was designed as physician-centered and patient-specific. Alert firing relied on visual acuity and International Classification of Diseases (ICD)-10 diagnosis (hemianopia/quadrantanopia) criteria. The alert suppression considerations included age < 5 years, recent surgeries, prior LVR visit, and related alert actions. False positive rate (firing when alert should have been suppressed or when firing criteria not met) was 0.2%. The overall false negative rate (alert not firing when visual acuity or encounter diagnosis criteria met) was 5.6%. Of the 13 physicians who completed the survey, 8 agreed that the alert is easy to use, and 12 would consider ongoing usage. Conclusions: This EHR-based clinical decision support system shows reliable firing metrics in identifying patients with vision impairment and promising acceptance by ophthalmologist users to facilitate care and LVR referral. Translational Relevance: The use of real-time data offers an opportunity to translate ophthalmic guidelines and best practices into systematic action for clinical care and research purposes across subspecialties.
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