Data quality of bedside monitoring in an intensive care unit

Abstract

Computerized record keeping promises complete, accurate and legible documentation. Reliable measurements are a prerequisite to fulfill these expectations. We analyzed the physiological variables provided by bedside monitoring devices in 657 bedside visits performed by an experienced Intensive Care nurse during 75 Intensive Care rounds. We registered which variables were displayed. If a variable was displayed, we assessed whether it could be used for documentation or should be rejected. If a value was rejected the reason was registered as: the measurement was not intended (superfluous display), the current clinical situation did not allow proper measurement, or other reasons. Basic variables (vital signs and respiration related variables) were displayed in more then 90%, specific variables (e.g. intracranial pressure) were displayed in less than 50% of the situations. Displayed variables were superfluous on an average of 11% because measurement was not intended. Variables like heart rate, temperature, airway pressure, minute volume of ventilation, arrhythmia, pulmonary arterial pressure, non-invasive blood pressure, and intracranial pressure provide high quality measured values (acceptance of more than 90%). Invasive arterial pressure, central venous pressure, respiration rate and oxygen saturation (via pulse oximetry) provided lower quality values with a rejection rate higher than 10%. Inappropriate sensor technology to match the clinical environment seems to be the root cause. In future the request for automatic documentation will increase. In order to avoid additional staff workload and to ensure reliable documentation, sensor technology especially related to respiration rate, blood pressure measurements, and pulse oximetry should be improved.

Computer assisted data analysis in intensive care: the ICDEV project-development of a scientific database system for intensive care

“…Therefore the quality of automatically recorded and stored data points (actual values or means) remains questionnable. Friesdorf et al showed that less than 85 % of invasively measured blood pressure values were reliable at the time of assessment, similar results were seen for other 157 monitoring data [28]. Thus, in our experience, data recorded without validation can (at present) hardly be used for automated calculations (e.g.…”

The association between vital signs and major hemorrhagic injury is significantly improved after controlling for sources of measurement variability

“…This was consistent with prior reports that unreliable vital sign data are all too typical in clinical practice [1,2,[4][5][6]. To deal with missing data, we used an ensemble classifier for multivariate classification, which was significantly better than univariate classification.…”

“…In terms of the external validity of the study, the issues that we studied have been previously recognized [1,2,[4][5][6]. This report offers a novel, quantitative analysis of their magnitude of effect in actual prehospital practice.…”

“…Significant discrepancies have been reported between different methods of measuring noninvasive BP [3]. Similarly, respiratory rate (RR) measurement is prone to technical error, whether measured by a clinician [4] or by a bedside monitor via impedance pneumography (IP) [5]. In one report, both triage nurses' measurements of RR and electronic measurement of RR revealed poor sensitivity for bradypnea and tachypnea, and the authors referred to RR as "the vexatious vital" [4].…”

Clinician blood pressure documentation of stable intensive care patients: An intelligent archiving agent has a higher association with future hypotension

“…Continual physiologic monitoring, e.g., within the Intensive Care Unit (ICU), poses a work burden on caregivers who must regularly document the data. Although recent computing capabilities make it technologically feasible to automatically record voluminous, continuous physiologic data, it may not be desirable: automatically archived data may be overly polluted with the measurement errors and artifacts that are known to corrupt physiologic data [1][2][3][4][5][6][7][8][9][10].…”