Organizational Performance and Regulatory Compliance as Measured by Clinical Pertinence Indicators Before and After Implementation of Anesthesia Information Management System (AIMS)

  • Clark K. Choi
  • Darlene Saberito
  • Changa Tyagaraj
  • Kalpana TyagarajEmail author
Original Paper


Previous studies have suggested that electronic medical records (EMR) can lead to a greater reduction of medical errors and better adherence to regulatory compliance than paper medical records (PMR). In order to assess the organizational performance and regulatory compliance, we tracked different clinical pertinence indicators (CPI) in our anesthesia information management system (AIMS) for 5 years. These indicators comprised of the protocols from the Surgical Care Improvement Project (SCIP), elements of performance (EP) from The Joint Commission (TJC), and guidelines from the Centers for Medicare and Medicaid Services (CMS). A comprehensive AIMS was initiated and the CPI were collected from October 5, 2009 to December 31, 2010 (EMR period) and from January 1, 2006 to October 4, 2009 (PMR period). Fourteen CPI were found to be common between the EMR and PMR periods. Based on the statistical analysis of the 14 common CPI, there was a significant increase (p < 0.001) in overall compliance after the introduction of EMR compared to the PMR period. The increase in overall compliance was significantly progressive (p = 0.013) from year to year over 2006 and 2010. Of the 14 CPI, Documentation of a) medication doses, and b) monitoring of postoperative physiological status, mental status, and pain scores showed significant improvement (p < 0.001) during the EMR period compared to the PMR period.


Anesthesia electronic medical record (EMR) Regulatory compliance Clinical Pertinence Indicators (CPI) Surgical Care Improvement Project (SCIP) The Joint Commission (TJC) Anesthesia Information Management System (AIMS) 



The authors would like to thank Murali Pagala, Ph.D. for providing statistical analysis of the data and critically reading the manuscript.


  1. 1.
    Edsall, D. W., Deshane, P., Giles, C., et al., Computerized patient anesthesia records: less time and better quality than manually produced anesthesia records. J. Clin. Anesth. 5(4):275–283, 1993.CrossRefGoogle Scholar
  2. 2.
    Tang, P. C., LaRosa, M. P., and Gorden, S. M., Use of computer-based records, completeness of documentation, and appropriateness of documented clinical decisions. J. Am. Med. Inform. Assoc. 6(3):245–251, 1999.CrossRefGoogle Scholar
  3. 3.
    Devitt, J. H., Rapanos, T., Kurrek, M., Cohen, M. M., and Shaw, M., The anesthetic record: accuracy and completeness. Can. J. Anaesth. 46(2):122–128, 1999.CrossRefGoogle Scholar
  4. 4.
    Roukema, J., Los, R. K., Bleeker, S. E., et al., Paper versus computer: feasibility of an electronic medical record in general pediatrics. Pediatrics 117(1):15–21, 2006.CrossRefGoogle Scholar
  5. 5.
    Blumenthal, D., Launching HITECH. N. Engl. J. Med. 362(5):382–385, 2010.CrossRefGoogle Scholar
  6. 6.
    Classen, D. C., and Bates, D. W., Finding the meaning in meaningful use. N. Engl. J. Med. 365(9):855–858, 2011.CrossRefGoogle Scholar
  7. 7.
    Vigoda, M. M., and Lubarsky, D. A., The medicolegal importance of enhancing timeliness of documentation when using an anesthesia information system and the response to automated feedback in an academic practice. Anesth. Analg. 103(1):131–136, 2006.CrossRefGoogle Scholar
  8. 8.
    Driscoll, W. D., Columbia, M. A., and Peterfreund, R. A., An observational study of anesthesia record completeness using an anesthesia information management system. Anesth. Analg. 104(6):1454–1461, 2007.CrossRefGoogle Scholar
  9. 9.
    Muravchick, S., Caldwell, J. E., Epstein, R. H., et al., Anesthesia information management system implementation: a practical guide. Anesth. Analg. 107(5):1598–1608, 2008.CrossRefGoogle Scholar
  10. 10.
    Jang, J., Yu, S. H., Kim, C. B., Moon, Y., and Kim, S., The effects of an electronic medical record on the completeness of documentation in the anesthesia record. Int. J. Med. Inform. 82(8):702–707, 2013.CrossRefGoogle Scholar
  11. 11.
    Avidan, A., and Weissman, C., Context-sensitive mandatory data-entry fields for data completeness and accuracy in anesthesia information management systems. Can. J. Anaesth. 60(3):325–326, 2013.CrossRefGoogle Scholar
  12. 12.
    CMS Manual System. §482.52 Condition of participation: Anesthesia services. Available at: Accessed: 8/9/13
  13. 13.
    Driscoll, W. D., Columbia, M. A., and Peterfreund, R. A., Awareness during general anesthesia: analysis of contributing causes aided by automatic data capture. J. Neurosurg. Anesthesiol. 19(4):268–272, 2007.CrossRefGoogle Scholar
  14. 14.
    Reich, D. L., Wood, R. K., Jr., Mattar, R., et al., Arterial blood pressure and heart rate discrepancies between handwritten and computerized anesthesia records. Anesth. Analg. 91(3):612–616, 2000.CrossRefGoogle Scholar
  15. 15.
    Wrightson, W. A., A comparison of electronic and handwritten anaesthetic records for completeness of information. Anaesth. Intensive Care. 38(6):1052–1058, 2010.Google Scholar
  16. 16.
    Wax, D. B., Beilin, Y., Levin, M., et al., The effect of an interactive visual reminder in an anesthesia information management system on timeliness of prophylactic antibiotic administration. Anesth. Analg. 104(6):1462–1466, 2007.CrossRefGoogle Scholar
  17. 17.
    Schwann, N. M., Bretz, K. A., Eid, S., et al., Point-of-care electronic prompts: an effective means of increasing compliance, demonstrating quality, and improving outcome. Anesth. Analg. 113(4):869–876, 2011.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Clark K. Choi
    • 1
  • Darlene Saberito
    • 2
  • Changa Tyagaraj
    • 2
  • Kalpana Tyagaraj
    • 2
    Email author
  1. 1.Department of Anesthesiology and Perioperative MedicineThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of AnesthesiologyMaimonides Medical CenterBrooklynUSA

Personalised recommendations