ReviewCurrent opinion in critical care2014
Optimal data systems: the future of clinical predictions and decision support.
Review in Current opinion in critical care, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Dealing with information overload: a comprehensive review.Frontiers in psychology · 2023Review
- AI in the treatment of fertility: key considerations.Journal of assisted reproduction and genetics · 2020Article
- Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset.JMIR medical informatics · 2020Article
- A system uptake analysis and GUIDES checklist evaluation of the Electronic Asthma Management System: A point-of-care computerized clinical decision support system.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Clinical Note Creation, Binning, and Artificial Intelligence.JMIR medical informatics · 2017Article
- Perpetual and Virtual Patients for Cardiorespiratory Physiological Studies.Journal of pediatric intensive care · 2016Review
- The predictive value of early acute kidney injury for long-term survival and quality of life of critically ill patients.Critical care (London, England) · 2016Article
- Mortality profile across our Intensive Care Units: A 5-year database report from a Singapore restructured hospital.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2015Article
- Disrupting Electronic Health Records Systems: The Next Generation.JMIR medical informatics · 2015Article
- State of the art review: the data revolution in critical care.Critical care (London, England) · 2015Review
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
purpose of reviewThe purpose of the review is to describe the evolving concept and role of data as it relates to clinical predictions and decision-making. RECENT
findingsCritical care medicine is, as an especially data-rich specialty, becoming acutely cognizant not only of its historic deficits in data utilization but also of its enormous potential for capturing, mining, and leveraging such data into well-designed decision support modalities as well as the formulation of robust best practices. SUMMARY: Modern electronic medical records create an opportunity to design complete and functional data systems that can support clinical care to a degree never seen before. Such systems are often referred to as 'data-driven,' but a better term is 'optimal data systems' (ODS). Here we discuss basic features of an ODS and its benefits, including the potential to transform clinical prediction and decision support.
Indexed as
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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.