Journal of Clinical Monitoring and Computing

, Volume 29, Issue 1, pp 163–167 | Cite as

The use of a clinical database in an anesthesia unit: focus on its limits

  • Grégoire Weil
  • Cyrus Motamed
  • Alexandre Eghiaian
  • Marie Laurence Guye
  • Jean Louis Bourgain
Original Research


Anesthesia information management system (AIMS) can be used a part of quality assurance program to improve patient care, however erroneous or missing data entries may lead to misinterpretation. This study assesses the accuracy of information extracted for six consecutive years from a database linked to an automatic anesthesia record-keeping system. An observational study was conducted on a database linked AIMS system. The database was filled in real time during surgical/anesthesia procedure and in the post-anesthesia care unit. The following items: name of the anesthetist, duration of anesthesia, duration of monitoring, ventilatory status upon arrival in postoperative care unit, pain scores, nausea and vomiting scores, pain medication (morphine) and anti nausea and vomiting drug consumption (ondansetron) were extracted and analysed in order to determine exhaustivity (percentage of missing data) and accuracy of the database. The analysis covered 55,946 anaesthetic procedures. The rate of missing data was initially high upon installation but decreased over time. It was limited to 5 % after 3 years for items such as start of anesthesia or name of the anesthetist. However exhaustivity/completeness of some other variable, such as nausea and vomiting started as low as 50 % to reach 20 % at 2008. After cross analysing pain and post-operative nausea and vomiting scores with related medication consumption, (morphine and ondansetron) we conclude that missing data was due to omission of a zero score rather than human error. The follow-up of quality assurance program may use data from AIMS provided that missing or erroneous values be mentioned and their impact on calculations accurately analysed.


AIMS Anesthesia department Quality assurance program Exhaustivity Accurracy 


Conflict of interest

There is no conflict of interest and it was funded by internal source of the Department of Anesthesia.


  1. 1.
    Wright MC, Phillips-Bute B, Mark JB, Stafford-Smith M, Grichnik KP, Andregg BC, et al. Time of day effects on the incidence of anesthetic adverse events. Qual Saf Health Care. 2006;15(4):258–63.PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Benn J, Arnold G, Wei I, Riley C, Aleva F. Using quality indicators in anaesthesia: feeding back data to improve care. Br J Anaesth. 2012;109(1):80–91.PubMedCrossRefGoogle Scholar
  3. 3.
    Block FE Jr, Reynolds KM, McDonald JS. The Diatek Arkive “Organizer” patient information management system: experience at a university hospital. J Clin Monit Comput. 1998;14(2):89–94.PubMedCrossRefGoogle Scholar
  4. 4.
    Devitt JH, Rapanos T, Kurrek M, Cohen MM, Shaw M. The anesthetic record: accuracy and completeness. Can J Anaesth. 1999;46(2):122–8.PubMedCrossRefGoogle Scholar
  5. 5.
    Sanborn KV, Castro J, Kuroda M, Thys DM. Detection of intraoperative incidents by electronic scanning of computerized anesthesia records. Comparison with voluntary reporting. Anesthesiology. 1996;85(5):977–87.PubMedCrossRefGoogle Scholar
  6. 6.
    Motamed C, Bourgain JL. Impact of a quality assurance program on the use of neuromuscular monitoring and reversal of muscle relaxants. Ann Fr Anesth Reanim. 2009;28(4):297–301.PubMedCrossRefGoogle Scholar
  7. 7.
    Bothner U, Georgieff M, Schwilk B. Building a large-scale perioperative anaesthesia outcome-tracking database: methodology, implementation, and experiences from one provider within the German quality project. Br J Anaesth. 2000;85(2):271–80.PubMedCrossRefGoogle Scholar
  8. 8.
    Quinzio L, Junger A, Gottwald B, Benson M, Hartmann B, Jost A, et al. User acceptance of an anaesthesia information management system. Eur J Anaesthesiol. 2003;20(12):967–72.PubMedCrossRefGoogle Scholar
  9. 9.
    Junger A, Hartmann B, Benson M, Schindler E, Dietrich G, Jost A, et al. The use of an anesthesia information management system for prediction of antiemetic rescue treatment at the postanesthesia care unit. Anesth Analg. 2001;92(5):1203–9.PubMedCrossRefGoogle Scholar
  10. 10.
    Vigoda MM, Gencorelli F, Lubarsky DA. Changing medical group behaviors: increasing the rate of documentation of quality assurance events using an anesthesia information system. Anesth Analg. 2006;103(2):390–5 (table of contents).PubMedCrossRefGoogle Scholar
  11. 11.
    Pickles A. Missing data, problems and solutions. In: Kempf-Leonard K, editor. Encyclopedia of social measurement. Amsterdam: Elsevier; 2005. p. 689–94.CrossRefGoogle Scholar
  12. 12.
    Spring SF, Sandberg WS, Anupama S, Walsh JL, Driscoll WD, Raines DE. Automated documentation error detection and notification improves anesthesia billing performance. Anesthesiology. 2007;106(1):157–63.PubMedCrossRefGoogle Scholar
  13. 13.
    Fasting S, Gisvold SE. Data recording of problems during anaesthesia: presentation of a well-functioning and simple system. Acta Anaesthesiol Scand. 1996;40(10):1173–83.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Grégoire Weil
    • 1
  • Cyrus Motamed
    • 1
  • Alexandre Eghiaian
    • 1
  • Marie Laurence Guye
    • 1
  • Jean Louis Bourgain
    • 1
  1. 1.Service d’AnesthésieInstitut Gustave RoussyVillejuif CedexFrance

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