Database quality assessment in research in paramedicine: a scoping review.

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.

Frequent users of emergency departments: developing standard definitions and defining prominent risk factors.

STUDY OBJECTIVE: We identify factors that define frequent and highly frequent emergency department (ED) users. METHODS: Administrative health care records were used to define less frequent (1 to 6 visits), frequent (7 to 17 visits), and highly frequent (≥18 visits) ED users. Analyses were conducted to determine the most unique demographic, disease, and health care use features of these groups. RESULTS: Frequent users composed 9.9% of all ED visits, whereas highly frequent users composed 3.6% of visits. Compared with less frequent users, frequent users were defined most strongly by their substance abuse challenges and by their many visits to primary care and specialist physicians. Substance abuse also distinguished highly frequent from frequent ED users strongly; 67.3% versus 35.9% of these patient groups were substance abusers, respectively. Also, 70% of highly frequent versus only 17.8% of frequent users had a long history of frequent ED use. Last, highly frequent users did not use other health care services proportionally more than their frequent user counterparts, suggesting that these former patients use EDs as a main source of care. CONCLUSION: This research develops objective thresholds of frequent and highly frequent ED use. Although substance abuse is prominent in both groups, only highly frequent users seem to visit EDs in place of other health care services. Future analyses can investigate these patterns of health care use more closely, including how timely access to primary care affects ED use. Cluster analysis also has value for defining frequent user subgroups who may benefit from different yet equally effective treatment options.