Development of data dictionary for neonatal intensive care unit: advancement towards a better critical care unit
Autor: | Su Jin Cho, Jonathan P. Palma, Avneet Kaur, Ashish Kumar Pandey, Gautam Yadav, Praveen Kumar, Ritu Das, Satish Saluja, Ravneet Kaur, Shubham Gupta, Yao Sun, Harpreet Singh |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
medicine.medical_specialty
Data collection Computer science Health Informatics Audit electronic health record quality indicators Data dictionary neonate health neonatal intensive care unit Database Notes 03 medical and health sciences Management information systems 0302 clinical medicine Workflow 030225 pediatrics Data quality medicine Data analysis Medical physics 030212 general & internal medicine data dictionary data analytics Categorical variable |
Zdroj: | JAMIA Open |
ISSN: | 2574-2531 |
Popis: | Background Critical 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. Methods After 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. Results DD 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 (P Conclusion This 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. |
Databáze: | OpenAIRE |
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