In a world of competing priorities, accurate production of information on the scale of the injury burden and the effectiveness of prevention-orientated interventions and policies is important; hence, data quality matters. This article surveys the literature about what is known about data quality in the injury field and developments to improve the quality and usability of information, particularly through triangulation of data sources, data linkage and unlocking the potential for more deeply phenotyped data through natural language processing.
- coding systems
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Contributors This article was written by RAL.
Funding The author has not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Commissioned; internally peer reviewed.