A substitution method to improve completeness of events documentation in anesthesia records


AIMS are optimized to find and display data and curves about one specific intervention but is not retrospective analysis on a huge volume of interventions. Such a system present two main limitation; (1) the transactional database architecture, (2) the completeness of documentation. In order to solve the architectural problem, data warehouses were developed to propose architecture suitable for analysis. However, completeness of documentation stays unsolved. In this paper, we describe a method which allows determining of substitution rules in order to detect missing anesthesia events in an anesthesia record. Our method is based on the principle that missing event could be detected using a substitution one defined as the nearest documented event. As an example, we focused on the automatic detection of the start and the end of anesthesia procedure when these events were not documented by the clinicians. We applied our method on a set of records in order to evaluate; (1) the event detection accuracy, (2) the improvement of valid records. For the year 2010–2012, we obtained event detection with a precision of 0.00 (−2.22; 2.00) min for the start of anesthesia and 0.10 (0.00; 0.35) min for the end of anesthesia. On the other hand, we increased by 21.1 % the data completeness (from 80.3 to 97.2 % of the total database) for the start and the end of anesthesia events. This method seems to be efficient to replace missing “start and end of anesthesia” events. This method could also be used to replace other missing time events in this particular data warehouse as well as in other kind of data warehouses.

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The authors declare that they have no conflict of interest.

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Correspondence to Antoine Lamer.

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Lamer, A., De Jonckheere, J., Marcilly, R. et al. A substitution method to improve completeness of events documentation in anesthesia records. J Clin Monit Comput 29, 741–747 (2015). https://doi.org/10.1007/s10877-015-9661-3

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  • AIMS
  • Data completeness
  • Substitution rule
  • Data warehouse