How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments

Abstract

BackgroundVital sign data are important for clinical decision making in emergency care. Clinical Decision Support Systems (CDSS) have been advocated to increase patient safety and quality of care. However, the efficiency of CDSS depends on the quality of the underlying vital sign data. Therefore, possible factors affecting vital sign data quality need to be understood.This study aims to explore the factors affecting vital sign data quality in Swedish emergency departments and to determine in how far clinicians perceive vital sign data to be fit for use in clinical decision support systems. A further aim of the study is to provide recommendations on how to improve vital sign data quality in emergency departments.MethodsSemi-structured interviews were conducted with sixteen physicians and nurses from nine hospitals and vital sign documentation templates were collected and analysed. Follow-up interviews and process observations were done at three of the hospitals to verify the results. Content analysis with constant comparison of the data was used to analyse and categorize the collected data.ResultsFactors related to care process and information technology were perceived to affect vital sign data quality. Despite electronic health records (EHRs) being available in all hospitals, these were not always used for vital sign documentation. Only four out of nine sites had a completely digitalized vital sign documentation flow and paper-based triage records were perceived to provide a better mobile workflow support than EHRs. Observed documentation practices resulted in low currency, completeness, and interoperability of the vital signs. To improve vital sign data quality, we propose to standardize the care process, improve the digital documentation support, provide workflow support, ensure interoperability and perform quality control.ConclusionsVital sign data quality in Swedish emergency departments is currently not fit for use by CDSS. To address both technical and organisational challenges, we propose five steps for vital sign data quality improvement to be implemented in emergency care settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0305-4) contains supplementary material, which is available to authorized users.

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

“…Recent advances in machine learning (ML) and big data analytics have led to the emergence of a new generation of clinical decision support systems (CDSSs) designed to exploit the potentials of data-driven decision making in patient monitoring, particularly in the area of internal medicine, general practice, and remote monitoring of vital signs (Gálvez et al, 2013, Helldén et al, 2015 Lisboa & Taktak, 2006, Skyttberg, Vicente, Chen, Blomqvist, & Koch, 2016). Improved access to large and heterogeneous healthcare data and an integration of advanced computational procedures into CDSSs has enabled the real-time discovery of similarity metrics for patient stratification, development of predictive analytics for risk assessment, and selection of patient-specific therapeutic interventions at the time of decision-making (Brown, 2016, Dagliati et al, 2018, Farran, Channanath, Behbehani, & Thanaraj, 2013).…”

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“…For completeness, it is useful to refer to Ledikwe et al (2014) which shows how the characteristic is directly related to the presence of health guidelines outlined. For Skyttberg et al (2016) completeness is instead associated with the fullness of the compilation fields of electronic health records (EHR's). Finally, for Zozus, et al (2014) can be associated with four main elements: 1. completeness of the synthesis elements (for example the columns of a database), 2. the values inserted inside, 3. completeness of the values row and 4. the completeness understood as an opportunity to extract data by column and row by starting procedures to create operating percentages useful for healthcare.…”

“…Internal and external data coherence is considered in the case of Ledikwe et al (2014) as the data management system in various health districts in Botswana (and therefore internally) is compared but also using an external "Monitoring and Evaluation" (M & E) system at national level. Also in the study by Skyttberg et al (2016), it is possible to find elements of coherence, in this case, the consistency is compared between the databases and the paper documentation available to health personnel, the external consistency comes from the examination of 9 emergency departments in Sweden.…”

“…To this end the results belonging to the collections of Taylor & Francis (Bai et al, 2018), BioMed Central (Ledikwe et al, 2014), Springer (Mitsunaga et al, 2015), Journal of Health Management & Informatics (Firouraghi et al, 2018), BioMed Central (Skyttberg et al, 2016), Elsevier (Verma, 2014), Oxford University Press (Watson et al, 2017), American College of Healthcare Executives (Wills, 2014) and National Institutes of Health (NIH) Health Care Systems Research Collaboratory (Zozus M. N., et al, 2014).…”

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