Factors related to monitoring during admission of acute patients

Thomas Schmidt, Camilla N Bech, Mikkel Brabrand, Uffe Kock Wiil, Annmarie Lassen, Thomas Schmidt, Camilla N Bech, Mikkel Brabrand, Uffe Kock Wiil, Annmarie Lassen

Abstract

Understanding the use of patient monitoring systems in emergency and acute facilities may help to identify reasons for failure to identify risk patients in these settings. Hence, we investigate factors related to the utilization of automated monitoring for patients admitted to an acute admission unit by introducing monitor load as the proportion between monitored time and length of stay. A cohort study of patients admitted and registered to patient monitors in the period from 10/10/2013 to 1/10/2014 at the acute admission unit of Odense University Hospital in Denmark. Admissions with at least one measurement were analyzed using quantile regression by looking at the impact of distance from nursing office, number of concurrent patients, wing type (medical/surgical), age, sex, comorbidities, and severity conditioned on how much patients were monitored during their admissions. We registered 11,848 admissions, of which we were able to link patient monitor readings to 3149 (26.6 %) with 50 % being monitored <1.4 % of total admission time. Distance from nursing office had little influence on patients monitored <10 % of their admission time. But for other patients, being positioned further away from the office reduced the level of monitoring. Higher levels of severity were related to higher degrees of monitoring, but being admitted to the surgical wing reduce how much patients were monitored, and periods with many concurrent patients lead to a small increase in monitoring. We found a significant variation concerning how much patients were monitored during admission to an acute admission unit. Our results point to potential patient safety improvements in clinical procedures, and advocate an awareness of how patient monitoring systems are utilized.

Keywords: Computerized decision support; Emergency departments; Patient monitoring.

Conflict of interest statement

The authors have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Quantile plot for the response variable—illustrating the distribution of monitor load by its quantile distribution
Fig. 2
Fig. 2
Univariate regression plot of Distance from nursing office and registered Monitor load. a Ordinary Least Squares (mean ased) linear regression. b Mean linear regression, Median (Q50), 20th Quantile and 80th Quantile linear regression
Fig. 3
Fig. 3
Quantile regression process plots for exposures—showing the regression coefficients for the quantiles of exposure variables and the intercept when controlling for all factors

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Source: PubMed

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