Relational Sampling for Data Quality Auditing and Decision Support

2006 ◽  
pp. 82-88
Author(s):  
Bruno Cortes ◽  
José Nuno Oliveira
2021 ◽  
Author(s):  
Maurice Henkel ◽  
Tobias Horn ◽  
Francois Leboutte ◽  
Pawel Trotsenko ◽  
Sarah G. Dugas ◽  
...  

Abstract Introduction Physicians spend more than half of their workday interacting with health information systems to care for their patients. Effective data management that provides physicians with comprehensive patient information from various information systems is required to ensure high quality clinical decision making.Objectives We evaluated the impact of a novel, CE-certified clinical decision support tool on physician’s effectiveness and satisfaction in the clinical decision-making process.Methods Using pre-therapeutic prostate cancer management cases, we compared physician’s expenditure of time, data quality, and user satisfaction in the decision-making process comparing the current standard with the software. Ten urologists from our department conducted the diagnostic work-up to the treatment decision for a total of 10 patients using both approaches.Results A significant reduction in the physician’s expenditure of time for the decision-making process by -59.9 % (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated data quality parameters completeness (Cohen's d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94).Conclusion The software demonstrated that effective data management can improve physician’s effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.


2017 ◽  
Vol 08 (03) ◽  
pp. 880-892 ◽  
Author(s):  
Rong Chen ◽  
Hans Blomqvist ◽  
Sabine Koch ◽  
Niclas Skyttberg

Summary Background: Computerized clinical decision support and automation of warnings have been advocated to assist clinicians in detecting patients at risk of physiological instability. To provide reliable support such systems are dependent on high-quality vital sign data. Data quality depends on how, when and why the data is captured and/or documented. Objectives: This study aims to describe the effects on data quality of vital signs by three different types of documentation practices in five Swedish emergency hospitals, and to assess data fitness for calculating warning and triage scores. The study also provides reference data on triage vital signs in Swedish emergency care. Methods: We extracted a dataset including vital signs, demographic and administrative data from emergency care visits (n=335027) at five Swedish emergency hospitals during 2013 using either completely paper-based, completely electronic or mixed documentation practices. Descriptive statistics were used to assess fitness for use in emergency care decision support systems aiming to calculate warning and triage scores, and data quality was described in three categories: currency, completeness and correctness. To estimate correctness, two further categories –plausibility and concordance –were used. Results: The study showed an acceptable correctness of the registered vital signs irrespectively of the type of documentation practice. Completeness was high in sites where registrations were routinely entered into the Electronic Health Record (EHR). The currency was only acceptable in sites with a completely electronic documentation practice. Conclusion: Although vital signs that were recorded in completely electronic documentation practices showed plausible results regarding correctness, completeness and currency, the study concludes that vital signs documented in Swedish emergency care EHRs cannot generally be considered fit for use for calculation of triage and warning scores. Low completeness and currency were found if the documentation was not completely electronic. Citation: Skyttberg N, Chen R, Blomqvist H, Koch S. Exploring Vital Sign Data Quality in Electronic Health Records with Focus on Emergency Care Warning Scores. Appl Clin Inform 2017; 8: 880–892 https://doi.org/10.4338/ACI-2017-05-RA-0075


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