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

Abstract

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.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    Haux R. Health information systems—past, present, future. Int J Med Informatics. 2006;75:268–81.

    Article  Google Scholar 

  2. 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.

  3. 3.

    Chazard E, Ficheur G, Bernonville S, Luyckx M, Beuscart R. Data mining to generate adverse drug events detection rules. IEEE Trans Inf Technol Biomed. 2011;15:823–30.

    Article  PubMed  Google Scholar 

  4. 4.

    Douglas JR, Ritter MJ. Implementation of an Anesthesia Information Management System (AIMS). Ochsner J. 2011;11:102–14.

    PubMed Central  PubMed  Google Scholar 

  5. 5.

    Nunez CM. Advanced techniques for anesthesia data analysis. Semin Anesth Perioper Med Pain. 2004;23:121–4.

    Article  Google Scholar 

  6. 6.

    Kimball R. The data warehouse lifecycle toolkit: expert methods for designing, developing, and deploying data warehouses. Hoboken: Wiley; 1998.

    Google Scholar 

  7. 7.

    Wisniewski MF, Kieszkowski P, Zagorski BM, Trick WE, Sommers M, Weinstein RA. Development of a Clinical Data Warehouse for Hospital Infection Control. J Am Med Inform Assoc. 2003;10:454–62.

    PubMed Central  Article  PubMed  Google Scholar 

  8. 8.

    De Mul M, Alons P, van der Velde P, Konings I, Bakker J, Hazelzet J. Development of a clinical data warehouse from an intensive care clinical information system. Comput Methods Programs Biomed. 2012;105:22–30.

    Article  PubMed  Google Scholar 

  9. 9.

    Taffé P, Sicard N, Pittet V, Pichard S, Burnand B, ADS Study Group. The occurrence of intra-operative hypotension varies between hospitals: observational analysis of more than 147,000 anaesthesia. Acta Anaesthesiol Scand. 2009;53:995–1005.

    Article  PubMed  Google Scholar 

  10. 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.

    Article  PubMed  Google Scholar 

  11. 11.

    Komatsu R, You J, Mascha EJ, Sessler DI, Kasuya Y, Turan A. Anesthetic induction with etomidate, rather than propofol, is associated with increased 30-day mortality and cardiovascular morbidity after noncardiac surgery. Anesth Analg. 2013;117:1329–37.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Bréant C, Borst F, Nkoulou R, Irion O, Geissbuhler A. Closing the loop: bringing decision support clinical data at the clinician desktop. Stud Health Technol Inform. 2007;129:890–4.

    PubMed  Google Scholar 

  13. 13.

    Jang J, Yu SH, Kim C-B, Moon Y, Kim S. The effects of an electronic medical record on the completeness of documentation in the anesthesia record. Int J Med Inform. 2013;82:702–7.

    Article  PubMed  Google Scholar 

  14. 14.

    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;85:977–87.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013;20:144–51.

    PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    Fox C, Levitin A, Redman T. The notion of data and its quality dimensions. Inf Process Manage. 1994;30:9–19.

    Article  Google Scholar 

  17. 17.

    Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform. 2013;46:830–6.

    Article  PubMed  Google Scholar 

  18. 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.

  19. 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.

  20. 20.

    Devitt JH, Rapanos T, Kurrek M, Cohen MM, Shaw M. The anesthetic record: accuracy and completeness. Can J Anesth. 1999;46:122–8.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    BOW Médical [WWW Document], n.d. http://www.bowmedical.com/. Accessed 7.5.14.

  22. 22.

    Lamer A, Jeanne M, Vallet B, Ditilyeu G, Delaby F, Tavernier B, Logier R. Development of an anesthesia data warehouse: preliminary results. IRBM. 2013;34:376–8.

    Article  Google Scholar 

  23. 23.

    Spring SF, Sandberg WS, Anupama S, Walsh JL, Driscoll WD, Raines DE. Automated documentation error detection and notification improves anesthesia billing performance. Anesthesiology. 2007;106:157–63.

    Article  PubMed  Google Scholar 

  24. 24.

    Sandberg WS, Sandberg EH, Seim AR, Anupama S, Ehrenfeld JM, Spring SF, Walsh JL. Real-time checking of electronic anesthesia records for documentation errors and automatically text messaging clinicians improves quality of documentation. Anesth Analg. 2008;106:192–201.

    Article  PubMed  Google Scholar 

Download references

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Antoine Lamer.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • AIMS
  • Data completeness
  • Substitution rule
  • Data warehouse