A substitution method to improve completeness of events documentation in anesthesia records
- 169 Downloads
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.
KeywordsAIMS Data completeness Substitution rule Data warehouse
Conflict of interest
The authors declare that they have no conflict of interest.
- 2.Pitt EA. Application of data mining techniques in the prediction of coronary artery disease: use of anaesthesia time-series and patient risk factor data (Thesis). Queensland University of Technology; 2009.Google Scholar
- 6.Kimball R. The data warehouse lifecycle toolkit: expert methods for designing, developing, and deploying data warehouses. Hoboken: Wiley; 1998.Google Scholar
- 10.Walsh M, Devereaux PJ, Garg AX, Kurz A, Turan A, Rodseth RN, Cywinski J, Thabane L, Sessler DI. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013;119:507–15.CrossRefPubMedGoogle Scholar
- 18.Müller H. Problems, methods and challenges in comprehensive data cleansing (technical report no. HUB-IB-164). Humboldt-Universität zu Berlin, Institut für Informatik; 2003.Google Scholar
- 19.Weil G, Motamed C, Eghiaian A, Guye ML, Bourgain JL. The use of a clinical database in an anesthesia unit: focus on its limits. J Clin Monit Comput. 2014;29:1–5.Google Scholar
- 21.BOW Médical [WWW Document], n.d. http://www.bowmedical.com/. Accessed 7.5.14.