Quantifying Mobility in the ICU: Comparison of Electronic Health Record Documentation and Accelerometer-Based Sensors to Clinician-Annotated Video - PubMed (original) (raw)
Quantifying Mobility in the ICU: Comparison of Electronic Health Record Documentation and Accelerometer-Based Sensors to Clinician-Annotated Video
Sarina Fazio et al. Crit Care Explor. 2020.
Abstract
To compare the accuracy of electronic health record clinician documentation and accelerometer-based sensors with a gold standard dataset derived from clinician-annotated video to quantify early mobility activities in adult ICU patients.
Design: Prospective, observational study.
Setting: Medical ICU at an academic hospital.
Patients: Adult ICU patients (n = 30) were each continuously monitored over a median of 24.4 hours, yielding 711.5 hours of video, electronic health record, and sensor data.
Interventions: None.
Measurements and main results: Electronic health record documentation estimated ambulation (intraclass correlation coefficient, 0.89; 95% CI, 0.78-0.95), sitting out-of-bed (intraclass correlation coefficient, 0.85; 95% CI, 0.72-0.93), and turning events (intraclass correlation coefficient, 0.87; 95% CI, 0.75-0.94) with excellent agreement but underestimated the number of standing, transferring, and pregait activities performed per patient. The accelerometer-based sensor had excellent agreement with video annotation for estimating duration of time spent supine (intraclass correlation coefficient, 0.99; CI, 0.97-0.99) and sitting/standing upright (intraclass correlation coefficient, 0.92; CI, 0.82-0.96) but overestimated ambulation time.
Conclusions: Our results show that electronic health record documentation and sensor-based technologies accurately capture distinct but complimentary metrics for ICU mobility measurement. Innovations in artifact detection, standardization of clinically relevant mobility definitions, and electronic health record documentation enhancements may enable further use of these technologies to drive critical care research and technology leveraged data-driven ICU models of care.
Keywords: early mobility; electronic health records; fitness trackers; informatics; intensive care units.
Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.
Conflict of interest statement
The authors have disclosed that they do not have any conflicts of interest.
Figures
Figure 1.
Consort diagram for participant screening, enrollment, data collection, and analysis. EHR = electronic health record.
Figure 2.
Visual depiction of agreement in activity frequency between video and electronic health record (EHR) estimations per patient, according to the Bland–Altman method. Each value represents the difference in activity frequency estimates between the two methods (EHR minus Video) against the mean of the two methods. Points above the _y_-axis zero line indicate overestimation by the EHR, and points below the _x_-axis zero line indicate underestimation. The dotted lines represent the upper and lower limits of agreement (LoA) (± 1.96
sd
).
References
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