Cognitive Analysis of Decision Support for Antibiotic Ordering in a Neonatal Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Clinical decision support systems (CDSS) are a method used to support prescribing accuracy when deployed within a computerized provider order entry system (CPOE). Divergence from using CDSS is exemplified by high alert override rates. Excessive cognitive load imposed by the CDSS may help to explain such high rates. OBJECTIVES: The aim of this study was to describe the cognitive impact of a CPOE-integrated CDSS by categorizing system use problems according to the type of mental processing required to resolve them. METHODS: A qualitative, descriptive design was used employing two methods; a cognitive walkthrough and a think-aloud protocol. Data analysis was guided by Norman's Theory of Action and a theory of cognitive distances which is an extension to Norman's theory. RESULTS: The most frequently occurring source of excess cognitive effort was poor information timing. Information presented by the CDSS was often presented after clinicians required the information for decision making. Additional sources of effort included use of language that was not clear to the user, vague icons, and lack of cues to guide users through tasks. CONCLUSIONS: Lack of coordination between clinician's task-related thought processes and those presented by a CDSS results in excessive cognitive work required to use the system. This can lead to alert overrides and user errors. Close attention to user's cognitive processes as they carry out clinical tasks prior to CDSS development may provide key information for system design that supports clinical tasks and reduces cognitive effort.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".