Article... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics2016
Supporting novice clinicians cognitive strategies: System design perspective.
Article in ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Article
- Scoping review: the empowerment of Alzheimer's Disease caregivers with mHealth applications.NPJ digital medicine · 2021Article
- Detailed analysis of 'work as imagined' in the use of intravenous insulin infusions in a hospital: a hierarchical task analysis.BMJ open · 2021Article
- Pharmacogenomics cascade testing (PhaCT): a novel approach for preemptive pharmacogenomics testing to optimize medication therapy.The pharmacogenomics journal · 2021Review
- Artificial Intelligence-Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study.JMIR research protocols · 2020Article
- How good is our diagnostic intuition? Clinician prediction of bacteremia in critically ill children.BMC medical informatics and decision making · 2020Article
- Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design.JMIR research protocols · 2019Article
- Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition.Proceedings. IEEE International Conference on Healthcare Informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Infections occur among all clinical domains. The changing nature of microbes, viruses and infections poses a great threat to the overall well-being in medicine. Clinicians in the infectious disease (ID) domain deal with diagnostic as well as treatment uncertainty in their everyday practice. Our current health information technology (HIT) systems do not consider the level of clinician expertise into the system design process. Thus, information is presented to both novice and expert ID clinicians in identical ways. The purpose of this study was to identify the cognitive strategies novice ID clinicians use in managing complex cases to make better recommendations for system design. In the process, we interviewed 14 ID experts and asked them to give us a detailed description of how novice clinicians would have dealt with complex cases. From the interview transcripts, we identified four major themes that expert clinicians suggested about novices' cognitive strategies including: A) dealing with uncertainty, B) lack of higher macrocognition, C) oversimplification of problems through heuristics and D) dealing with peer pressure. Current and future innovative decision support tools embedded in the electronic health record that can match these cognitive strategies may hold the key to cognitively supporting novice clinicians. The results of this study may open up avenues for future research and suggest design directions for better healthcare systems.
Indexed as
Identifiers
What OpenQuestion holds
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.