The impact of data quality defects on clinical decision-making in the intensive care unit
Introduction
Clinical data, reflecting patients' clinical characteristics and health condition, support a broad range of decisions and treatments at the point of care. Data quality (DQ) defects in this data have long been a major concern, given their potentially severe impact on clinical decision making and treatment [1,2]. Understanding, assessing, and improving the quality of clinical DQ and exploring the hazardous effects of DQ defects has long been a major research target.
Many studies focused on data acquisition acquired during doctor–patient interactions that is recorded manually into electronic medical record systems [3,4]. Over the years, bedside data acquisition became automatic and continuous, using dedicated devices and computerized monitoring utilities [5,6]. Bedside vital signs monitoring devices acquire and collect vast amount of data, which are typically presented to the clinical staff in real time, and recorded to a database for further tracking and analysis of patients' condition. Studies also indicated negative effects related to low quality monitoring signals such as frequent false alarms (the "crying wolf" phenomenon), erroneous treatment decisions, and mistrust [7], [8], [9]. The nature of data acquisition in such scenarios mandates different DQ assessment methods, as well as different approaches toward understanding the possible hazardous impact of DQ defects. Although other domains try to asses data DQ in order to improve decision-making [10], to the best of our knowledge no such studies were done on the impact of data quality defects on clinical decision-making in intensive care units (ICU).
The goal of this study is to examine DQ defects in clinical data collected automatically by bedside sensors and devices, and to evaluate its effect on actual patient care decisions. It aims to shed light on the crucial effect DQ defects have on decision making.
Section snippets
Hypothesis
The study hypothesized that DQ defects in clinical data will significantly affect clinical decision making.
Methodology
This study focuses on bedside-monitoring data, which is acquired automatically and continually at high sampling frequency through multiple sensors attached to the patient, each handling a different form of clinical measurement – heart rate, blood pressure, blood-oxygen saturation, and many others. The study attempts to associate clinical decisions with the quality of clinical data acquired before a decision was made, focusing on two DQ dimensions [11], each reflecting a relevant quality
Data quality assessment
The true positive rate (TPR) reflects the ratio of the correctly identified invalid values, while the false positive rate (FPR) reflects the ratio of valid values that were tagged as invalid. Obviously, as shown in Table 1, the baseline method's TPR is always 1, as every sample that exceeded the limits was captured.
The impact of DQ defects on clinical decision making
The logistic regression results are presented in Tables 2 and 3.
In the five models estimated, logistic regression results partially or completely support the research hypothesis, and
Discussion
Data quality (DQ) is broadly recognized as a major concern in clinical information systems; however, so far research has not paid much attention to the unique DQ issues involved in automatic data collection of ICU vital signs. This study developed a method for assessment for such data by adapting a combination of analytical tools and time-series analysis techniques. These methods address two DQ dimensions: (a) completeness, reflecting the extent of non-missing values, and (b) validity,
Declaration of Competing Interest
All authors declare that they have no conflicts of interest to disclose.
Acknowledgement
We would like to thank the Tel-Aviv Medical Center staff members involved in our research for their time, immense support and contributions, and Prof. Yisrael Parmet for the statistical consultation.
This work was partially funded by the Israeli Ministry of Science, Technology and Space (#8767311).
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