ReviewWorld journal of critical care medicine2013
Computerized decision support in adult and pediatric critical care.
Review in World journal of critical care medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review.Journal of medical Internet research · 2020Pooled it
- How do Clinicians Use Electronic Health Records for Respiratory Support Decisions? A Qualitative Study in Critical Care.ACI open · 2025Article
- A dynamic customized electronic health record rule based clinical decision support tool for standardized adult intensive care metrics.JAMIA open · 2024Article
- Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design.JMIR formative research · 2024Article
- Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction.Applied clinical informatics · 2022Article
- Barriers to and Facilitators for Acceptance of Comprehensive Clinical Decision Support System-Driven Care Maps for Patients With Thoracic Trauma: Interview Study Among Health Care Providers and Nurses.JMIR human factors · 2022Article
- Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery.Critical care explorations · 2021Article
- Observational
- Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.Methods of information in medicine · 2020Article
- Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study.JMIR medical informatics · 2020Article
- Article
- A Pediatric Intensive Care Unit Bedside Computer Clinical Decision Support Protocol for Hyperglycemia Is Feasible, Safe and Offers Advantages.Diabetes technology & therapeutics · 2017Article
- Clinical Decision Support for Pediatric Blood Product Prescriptions.Journal of pediatric intensive care · 2016Review
- Computerized Decision Support System for Traumatic Brain Injury Management.Journal of pediatric intensive care · 2016Review
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
No grant is acknowledged in the PubMed record.
Abstract
Computerized decision support (CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization of meaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.
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.