ArticleDigital health
Scientometric review of health data quality (focusing on disease management).
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The correlation between health data quality and disease management is of utmost importance. Optimal data quality enhances decision making, minimizes medical errors, and boosts efficiency in disease management. Hence, current research is focused on mapping of co-occurrences and the relationship between health data quality and disease management. Methods: The present study employed a scientometric approach known as co-occurrence analysis to analyze the data extracted from the Web of Science. Our research community aimed to investigate the association between health data quality and disease management, encompassing all documents produced in this field from 1991 to September 2023. The data was meticulously scrutinized and recorded on 1 October 2023. To generate graphs and tables, we utilized Excel 2019 software utilized, while VOSviewer scientometric software was employed to create co-occurrence maps. Results: The top three countries with the highest scientific production in this field were the United States, England, and Australia. The study also uncovered three clusters centered on data quality, management, and public health. Co-occurrence maps drawn from the study showed that accuracy, coverage, and completeness of health data are related to the provision of healthcare and therapeutic interventions, as well as treatment outcomes. Furthermore, management was found to be associated with prevention, epidemic, prevalence, disease, and mortality rate. The study also revealed that data quality is linked to evaluation, validity, reliability, public health, and quality of life. Conclusion: It is clear from this research that accuracy, coverage, and completeness are crucial characteristics of data quality. These factors play a key role in the management of prevention, epidemics, and disease outbreaks, as well as and mortality rates. Evaluating data quality is also important as it can have a positive impact on the quality of life, public health, and health information technologies. This study has the potential to be very useful in improving the quality of health data, the clinical informationist performances, and in turn, promoting more effective disease management.
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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.