The impact of data quality defects on clinical decision-making in the intensive care unit

Objective: Poor clinical data quality might affect clinical decision making and patient treatment. This study identifies quality defects in clinical data collected automatically by bedside monitoring devices in the Intensive Care Unit (ICU) and examines their effect on clinical decisions. Methods: Real-world data collected from 7688 patients admitted to the general ICU in a tertiary referral hospital over seven years was retrospectively analyzed. Data quality defect detection methods that use time-series analysis techniques identified two types of data quality defects: (a) completeness: the extent of non-missing values, and (b) validity: the extent of non-extreme values within the continuous range of values. Data quality defects were compared to five scenarios of medication and procedure prescriptions that are common in ICU settings: Blood-pressure reduction, blood-pressure elevation, anesthesia medications, intubation procedures, and muscle relaxant medications. Results: Results from a logistic regression revealed a strong connection between data quality and the clinical interventions examined: lower validity level increased the likelihood of prescription decisions for all five scenarios, and lower completeness level increased the likelihood of prescription decisions for some scenarios. Discussion: The results highlight the possible effect of data quality defects on physicians' decisions. Lower validity of certain key clinical parameters, and in some scenarios lower completeness, correlated with stronger tendency to prescribe medications or perform invasive procedures. Conclusions: Data quality defects in clinical data affect decision making even without practitioners’ awareness. Thus, it is important to emphasize these effects to ICU staff, as well as to medical device manufacturers.