ArticleJournal of the Medical Library Association : JMLA2020
How searching under time pressure impacts clinical decision making.
Article in Journal of the Medical Library Association : JMLA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Improving decision accuracy using a clinical decision support system for medical students during history-taking: a randomized clinical trial.BMC medical education · 2023Trial
- Metacognitive processes, situational factors, and clinical decision-making in nursing education: a quantitative longitudinal study.BMC medical education · 2024Article
- The impact of clock timing on VDT visual search performance under time constraint.Frontiers in psychology · 2024Article
- PubMed's core clinical journals filter: redesigned for contemporary clinical impact and utility.Journal of the Medical Library Association : JMLA · 2023Article
- Self-reported knowledge, attitudes, practices and barriers in use of evidence-based medicine among resident physicians in Kenya: a mixed methods study.BMC medical education · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveClinicians encounter many questions during patient encounters that they cannot answer. While search systems (e.g., PubMed) can help clinicians find answers, clinicians are typically busy and report that they often do not have sufficient time to use such systems. The objective of this study was to assess the impact of time pressure on clinical decisions made with the use of a medical literature search system.
designIn stage 1, 109 final-year medical students and practicing clinicians were presented with 16 clinical questions that they had to answer using their own knowledge. In stage 2, the participants were provided with a search system, similar to PubMed, to help them to answer the same 16 questions, and time pressure was simulated by limiting the participant's search time to 3, 6, or 9 minutes per question.
resultsUnder low time pressure, the correct answer rate significantly improved by 32% when the participants used the search system, whereas under high time pressure, this improvement was only 6%. Also, under high time pressure, participants reported significantly lower confidence in the answers, higher perception of task difficulty, and higher stress levels.
conclusionsFor clinicians and health care organizations operating in increasingly time-pressured environments, literature search systems become less effective at supporting accurate clinical decisions. For medical search system developers, this study indicates that system designs that provide faster information retrieval and analysis, rather than traditional document search, may provide more effective alternatives.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.