ReviewYonsei medical journal2022
Physician Knowledge Base: Clinical Decision Support Systems.
Review in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Implementation costs of hospital-based computerised decision support systems: a systematic review.Implementation science : IS · 2023Pooled it
- Continuous Glucose Monitoring-Driven Personalization of Cornstarch Therapy in Glycogen Storage Disease: A Retrospective Analysis.Yonsei medical journal · 2026Article
- Multimodal and Multidimensional Artificial Intelligence Technology in Obesity.Journal of obesity & metabolic syndrome · 2025Review
- A Knowledge-Enhanced Platform (MetaSepsisKnowHub) for Retrieval Augmented Generation-Based Sepsis Heterogeneity and Personalized Management: Development Study.Journal of medical Internet research · 2025Article
- Personalized Deep Learning Model for Clinical Target Volume on Daily Cone Beam Computed Tomography in Breast Cancer Patients.Advances in radiation oncology · 2024Article
- Perspectives of Decision Support System TeleRehab in the Management of Post-Stroke Telerehabilitation.Life (Basel, Switzerland) · 2024Article
- [Big data and artificial intelligence in anesthesia : Reality or fiction?]Die Anaesthesiologie · 2024Review
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
With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualitative medical data. As such, EMRs have become increasingly used in clinical decision support systems (CDSSs). While CDSSs aim to reduce medical errors normally occurring in the process of treating patients by physicians, technical maturity and the completeness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continuously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower investments. In current practice, computational systems already perform a wide variety of functions in medical settings to allow medical staff to focus on other tasks. However, no prior research has evaluated the potential effectiveness of future CDSSs nor analyzed possibilities for their further development. In this article, we evaluate CDSS technology with the consideration that medical staff also understand the core functions of such 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.