Author:
Schneider Anna,Wagenknecht Andreas,Sydow Hanna,Riedlinger Dorothee,Holzinger Felix,Figura Andrea,Deutschbein Johannes,Reinhold Thomas,Pigorsch Mareen,Stasun Ulrike,Schenk Liane,Möckel Martin
Abstract
Abstract
Background
This analysis addresses the characteristics of two emergency department (ED) patient populations defined by three model diseases (hip fractures, respiratory, and cardiac symptoms) making use of survey (primary) and routine (secondary) data from hospital information systems (HIS). Our aims were to identify potential systematic inconsistencies between both data samples and implications of their use for future ED-based health services research.
Methods
The research network EMANET prospectively collected primary data (n=1442) from 2017-2019 and routine data from 2016 (n=9329) of eight EDs in a major German city. Patient populations were characterized using socio-structural (age, gender) and health- and care-related variables (triage, transport to ED, case and discharge type, multi-morbidity). Statistical comparisons between descriptive results of primary and secondary data samples for each variable were conducted using binomial test, chi-square goodness-of-fit test, or one-sample t-test according to scale level.
Results
Differences in distributions of patient characteristics were found in nearly all variables in all three disease populations, especially with regard to transport to ED, discharge type and prevalence of multi-morbidity. Recruitment conditions (e.g., patient non-response), project-specific inclusion criteria (e.g., age and case type restrictions) as well as documentation routines and practices of data production (e.g., coding of diagnoses) affected the composition of primary patient samples. Time restrictions of recruitment procedures did not generate meaningful differences regarding the distribution of characteristics in primary and secondary data samples.
Conclusions
Primary and secondary data types maintain their advantages and shortcomings in the context of emergency medicine health services research. However, differences in the distribution of selected variables are rather small. The identification and classification of these effects for data interpretation as well as the establishment of monitoring systems in the data collection process are pivotal.
Trial registration
DRKS00011930 (EMACROSS), DRKS00014273 (EMAAGE), NCT03188861 (EMASPOT)
Funder
German Ministry of Education and Research
Charité - Universitätsmedizin Berlin
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Epidemiology
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献