ArticleJAMIA open2020
Development of data dictionary for neonatal intensive care unit: advancement towards a better critical care unit.
Article in JAMIA open, 2020. 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.
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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.
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Who cites it
8 citing papers in PubMed.
- Building the future of ICU care: Is our digital foundation strong enough? A multicentre survey of Australian and New Zealand intensive care units.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025Article
- Review
- Development of a data dictionary on viral hepatitis for the Philippines.Oxford open digital health · 2024Article
- Creating a data dictionary for pediatric autonomic disorders.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2023Article
- Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities.Pediatric research · 2023Review
- Improving VLBW infant outcomes with big data analytics.Pediatric research · 2021Article
- Designing a bed-side system for predicting length of stay in a neonatal intensive care unit.Scientific reports · 2021Article
- Development and validation of high definition phenotype-based mortality prediction in critical care units.JAMIA open · 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCritical care units (CCUs) with extensive use of various monitoring devices generate massive data. To utilize the valuable information of these devices; data are collected and stored using systems like clinical information system and laboratory information management system. These systems are proprietary, allow limited access to their database and, have the vendor-specific clinical implementation. In this study, we focus on developing an open-source web-based meta-data repository for CCU representing stay of the patient with relevant details.
methodsAfter developing the web-based open-source repository named data dictionary (DD), we analyzed prospective data from 2 sites for 4 months for data quality dimensions (completeness, timeliness, validity, accuracy, and consistency), morbidity, and clinical outcomes. We used a regression model to highlight the significance of practice variations linked with various quality indicators.
resultsDD with 1555 fields (89.6% categorical and 11.4% text fields) is presented to cover the clinical workflow of a CCU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 88% completeness, 97% accuracy, 91% timeliness, and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicators and practice variations are strongly correlated (
conclusionThis study documents DD for standardized data collection in CCU. DD provides robust data and insights for audit purposes and pathways for CCU to target practice improvements leading to specific quality improvements.
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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.