Eight physiological variables-tidal volume, breathing rate, end-tidal carbon dioxide fraction, oxygen fraction in the anesthetic circuit, oxygen saturation by pulse oximetry, systolic and diastolic blood pressure, and heart rate-recorded on-line by a commercially available automated system were compared with the same variables recorded on handwritten anesthesia records. We quantified the differences between the automated and handwritten records generated from the same 30 patients (2,412 minutes of general anesthesia for elective eye surgical procedures). Considering the design of the study, we claim that the differences between both records were caused by the incompleteness or inaccuracy of the handwirtten records, except in two instances. The amounts of missing or erroneous data for these eight physiological variables were expressed as fraction ("error fractions") of the time being recorded, designated EFm and EFe, respectively. For the first five variables the EFm on the handwritten records ranged between 0.23 and 0.31, and the EFc ranged between 0.01 and 0.06. For the last three variables the EFm range was 0.08 to 0.13, and the EFe range was 0.05 to 0.11. Most of these missing or erroneous data occurred during the period of induction (first 15 minutes) and at the end of the case (last 10 minutes). The EFm and EFe during induction had increased to 0.62 and 0.26, respectively, and to 0.76 and 0.06, respectively, at the end of the case. Erroneous data were observed on the automated records for the tidal volume during induction (EFe=0.0044) and for the oxygen fraction during maintenance (EFe=0.0024). The effect of averaging by the recordkeeper is discussed. The results of this study indicate the clinical relevance of automated record keeping. © 1988 Little, Brown and Company.
CITATION STYLE
Lerou, J. G. C., Dirksen, R., van Daele, M., Nijhuis, G. M. M., & Crul, J. F. (1988). Automated charting of physiological variables in anesthesia: A quantitative comparison of automated versus handwritten anesthesia records. Journal of Clinical Monitoring, 4(1), 37–47. https://doi.org/10.1007/BF01618106
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