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Objectives: To examine changes in technology-related errors (TREs), their manifestations and underlying mechanisms at 3 time points after the implementation of computerized provider order entry (CPOE) in an electronic health record; and evaluate the clinical decision support (CDS) available to mitigate the TREs at 5-years post-CPOE., Materials and Methods: Prescribing errors (n = 1315) of moderate, major, or serious potential harm identified through review of 35 322 orders at 3 time points (immediately, 1-year, and 4-years post-CPOE) were assessed to identify TREs at a tertiary pediatric hospital. TREs were coded using the Technology-Related Error Mechanism classification. TRE rates, percentage of prescribing errors that were TREs, and mechanism rates were compared over time. Each TRE was tested in the CPOE 5-years post-implementation to assess the availability of CDS to mitigate the error., Results: TREs accounted for 32.5% (n = 428) of prescribing errors; an adjusted rate of 1.49 TREs/100 orders (95% confidence interval [CI]: 1.06, 1.92). At 1-year post-CPOE, the rate of TREs was 40% lower than immediately post (incident rate ratio [IRR]: 0.60; 95% CI: 0.41, 0.89). However, at 4-years post, the TRE rate was not significantly different to baseline (IRR: 0.80; 95% CI: 0.59, 1.08). "New workflows required by the CPOE" was the most frequent TRE mechanism at all time points. CDS was available to mitigate 32.7% of TREs., Discussion: In a pediatric setting, TREs persisted 4-years post-CPOE with no difference in the rate compared to immediately post-CPOE., Conclusion: Greater attention is required to address TREs to enhance the safety benefits of systems., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: To understand and synthesize factors influencing user acceptance of digital interventions used for antimicrobial prescribing and monitoring in hospitals., Materials and Methods: A meta-synthesis was conducted to identify qualitative studies that explored user acceptance of digital interventions for antimicrobial prescribing and/or monitoring in hospitals. Databases were searched and qualitative data were extracted and systematically classified using the unified theory of acceptance and use of technology (UTAUT) model., Results: Fifteen qualitative studies met the inclusion criteria. Eleven papers used interviews and four used focus groups. Most digital interventions evaluated in studies were decision support for prescribing (n = 13). Majority of perceptions were classified in the UTAUT performance expectancy domain in perceived usefulness and relative advantage constructs. Key facilitators in this domain included systems being trusted and credible sources of information, improving performance of tasks and increasing efficiency. Reported barriers were that interventions were not considered useful for all settings or patient conditions. Facilitating conditions was the second largest domain, which highlights the importance of users having infrastructure to support system use. Digital interventions were viewed positively if they were compatible with values, needs, and experiences of users., Conclusions: User perceptions that drive users to accept and utilize digital interventions for antimicrobial prescribing and monitoring were predominantly related to performance expectations and facilitating conditions. To ensure digital interventions for antimicrobial prescribing are accepted and used, we recommend organizations ensure systems are evaluated and benefits are conveyed to users, that utility meets expectations, and that appropriate infrastructure is in place to support use., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: To identify and summarize the current internal governance processes adopted by hospitals, as reported in the literature, for selecting, optimizing, and evaluating clinical decision support (CDS) alerts in order to identify effective approaches., Materials and Methods: Databases (Medline, Embase, CINAHL, Scopus, Web of Science, IEEE Xplore Digital Library, CADTH, and WorldCat) were searched to identify relevant papers published from January 2010 to April 2020. All paper types published in English that reported governance processes for selecting and/or optimizing CDS alerts in hospitals were included., Results: Eight papers were included in the review. Seven papers focused specifically on medication-related CDS alerts. All papers described the use of a multidisciplinary committee to optimize alerts. Other strategies included the use of clinician feedback, alert data, literature and drug references, and a visual dashboard. Six of the 8 papers reported evaluations of their CDS alert modifications following the adoption of optimization strategies, and of these, 5 reported a reduction in alert rate., Conclusions: A multidisciplinary committee, often in combination with other approaches, was the most frequent strategy reported by hospitals to optimize their CDS alerts. Due to the limited number of published processes, variation in system changes, and evaluation results, we were unable to compare the effectiveness of different strategies, although employing multiple strategies appears to be an effective approach for reducing CDS alert numbers. We recommend hospitals report on descriptions and evaluations of governance processes to enable identification of effective strategies for optimization of CDS alerts in hospitals., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Quantifying alert override has been the focus of much research in health informatics, with override rate traditionally viewed as a surrogate inverse indicator for alert effectiveness. However, relying on alert override to assess computerized alerts assumes that alerts are being read and determined to be irrelevant by users. Our research suggests that this is unlikely to be the case when users are experiencing alert overload. We propose that over time, alert override becomes habitual. The override response is activated by environmental cues and repeated automatically, with limited conscious intention. In this paper we outline this new perspective on understanding alert override. We present evidence consistent with the notion of alert override as a habitual behavior and discuss implications of this novel perspective for future research on alert override, a common and persistent problem accompanying decision support system implementation., (© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objectives: To compare the manifestations, mechanisms, and rates of system-related errors associated with two electronic prescribing systems (e-PS). To determine if the rate of system-related prescribing errors is greater than the rate of errors prevented., Methods: Audit of 629 inpatient admissions at two hospitals in Sydney, Australia using the CSC MedChart and Cerner Millennium e-PS. System related errors were classified by manifestation (eg, wrong dose), mechanism, and severity. A mechanism typology comprised errors made: selecting items from drop-down menus; constructing orders; editing orders; or failing to complete new e-PS tasks. Proportions and rates of errors by manifestation, mechanism, and e-PS were calculated., Results: 42.4% (n=493) of 1164 prescribing errors were system-related (78/100 admissions). This result did not differ by e-PS (MedChart 42.6% (95% CI 39.1 to 46.1); Cerner 41.9% (37.1 to 46.8)). For 13.4% (n=66) of system-related errors there was evidence that the error was detected prior to study audit. 27.4% (n=135) of system-related errors manifested as timing errors and 22.5% (n=111) wrong drug strength errors. Selection errors accounted for 43.4% (34.2/100 admissions), editing errors 21.1% (16.5/100 admissions), and failure to complete new e-PS tasks 32.0% (32.0/100 admissions). MedChart generated more selection errors (OR=4.17; p=0.00002) but fewer new task failures (OR=0.37; p=0.003) relative to the Cerner e-PS. The two systems prevented significantly more errors than they generated (220/100 admissions (95% CI 180 to 261) vs 78 (95% CI 66 to 91))., Conclusions: System-related errors are frequent, yet few are detected. e-PS require new tasks of prescribers, creating additional cognitive load and error opportunities. Dual classification, by manifestation and mechanism, allowed identification of design features which increase risk and potential solutions. e-PS designs with fewer drop-down menu selections may reduce error risk.

Objectives: To determine the frequency with which computerized alerts occur and the proportion triggered as a result of prescribers not utilizing e-prescribing system functions., Methods: An audit of electronic inpatient medication charts at a teaching hospital in Sydney, Australia, was conducted to identify alerts fired, to categorize the system functions used by prescribers, and to assess if use of short-cut system functions could have prevented the alerts., Results: Of the 2209 active orders reviewed, 600 (27.2%) triggered at least one alert. Therapeutic duplication alerts were the most frequent (n=572). One third of these (20.2% of all alerts) was 'technically preventable' and would not have fired if prescribers had used a short-cut system function to prescribe. Under-utilized system functions included the option to 'MODIFY' existing orders and use of the 'AND' function for concurrent orders. Pregnancy alerts, set for women aged between 12 and 55 years, were triggered for 43% of drugs ordered for this group., Conclusion: Developers of decision support systems should test the extent to which technically preventable alerts may arise when prescribers fail to use system functions as designed. Designs which aim to improve the efficiency of the prescribing process but which do not align with the cognitive processes of users may fail to achieve this desired outcome and produce unexpected consequences such as triggering unnecessary alerts and user frustration. Ongoing user training to support effective use of e-prescribing system functions and modifications to the mechanisms underlying alert generation are needed to ensure that prescribers are presented with fewer but more meaningful alerts.

Objective: To assess whether a low level of decision support within a hospital computerized provider order entry system has an observable influence on the medication ordering process on ward-rounds and to assess prescribers' views of the decision support features., Methods: 14 specialty teams (46 doctors) were shadowed by the investigator while on their ward-rounds and 16 prescribers from these teams were interviewed., Results: Senior doctors were highly influential in prescribing decisions during ward-rounds but rarely used the computerized provider order entry system. Junior doctors entered the majority of medication orders into the system, nearly always ignored computerized alerts and never raised their occurrence with other doctors on ward-rounds. Interviews with doctors revealed that some decision support features were valued but most were not perceived to be useful., Discussion and Conclusion: The computerized alerts failed to target the doctors who were making the prescribing decisions on ward-rounds. Senior doctors were the decision makers, yet the junior doctors who used the system received the alerts. As a result, the alert information was generally ignored and not incorporated into the decision-making processes on ward-rounds. The greatest value of decision support in this setting may be in non-ward-round situations where senior doctors are less influential. Identifying how prescribing systems are used during different clinical activities can guide the design of decision support that effectively supports users in different situations. If confirmed, the findings reported here present a specific focus and user group for designers of medication decision support.