Alert fatigue and errors caused by technology: A scoping review and introduction to the flow of cognitive processing model
Bibliographic record
Abstract
Technologies such as electronic health records (EHRs), embedded clinical decision support systems (CDSS) and computerized physician order entry (CPOE) systems are at the forefront of healthcare’s technological revolution. These health information technologies (HIT) pose great promise to improve patient safety, reduce medication errors and increase operational efficiencies in healthcare organizations. However, despite the perceived benefits that these complex technologies offer, their associated risks must not be overlooked or disregarded (Borycki et al., 2012). The objective of this article is to answer the following questions: 1) What is the nature of errors caused by technology (i.e., technology-induced errors) and alert fatigue in healthcare? 2) Is there a relationship between alert fatigue and technology-induced errors? 3) Do organizational strategies exist to address these problems and enhance patient safety? 4) Do technological recommendations exist to improve the current issues surrounding safety? To answer these questions a scoping review following the Arksey and O’Malley (2005) framework was conducted using the CINAHL®, Web of Science®, IEEE Xplore® and PubMed® databases. The search focused on English publications only, using the search terms “Alert Fatigue” and “Technology Errors.” Articles were iteratively assessed based on the inclusion and exclusion criteria, resulting in an inclusion of 36 articles in the final scoping review. Following this, a thematic analysis was conducted and the findings placed in a data extraction table. The results indicated that while HIT present a significant opportunity to streamline processes and reduce medication errors, there is a critical need to assess them from a patient safety and quality lens. Lastly, a novel conceptual tool was created, the Flow of Cognitive Processing Model. The model provides an iterative perspective and an insightful view into the cognitive realms of healthcare professionals in their interactions with HIT. By illustrating the complexities of healthcare providers from a humanistic lens, the model could guide HIT design, acquisitions and implementations to reduce alert fatigue and mitigate the introduction of technology-induced errors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.033 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".