Database quality assessment in research in paramedicine: a scoping review

Abstract

Background: Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine.

Methods: This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form.

Results: Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness.

Conclusions: There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards.

Keywords: Data; Data collection; Data quality; Electronic health records; Emergency medical services; Medical records; Paramedicine; Prehospital.

Fig. 1

PRISMA-ScR flow diagram of study selection

Fig. 2

Geographic location of data quality assessment studies in research in paramedicine (n = 97), listing the number of studies by country (main panel), and by State (or national / military) among studies from the United States (inset)

Fig. 3

Topic (clinical area / population / situation) of data quality assessment studies in research in paramedicine, listing the number of areas (total = 111) among all studies (n = 97)

Fig. 4

The extent of data quality assessment studies in research in paramedicine, measured by A the level of data assessed, B the number of services included, C the number of types of linkages to other databases, and D, the number of records assessed. Each chart includes all studies (n = 97)