Journal of Medical Systems

, Volume 31, Issue 5, pp 319–327 | Cite as

Is the Availability of Hospital IT Applications Associated with a Hospital’s Risk Adjusted Incidence Rate for Patient Safety Indicators: Results from 66 Georgia Hospitals

  • Steven D. Culler
  • Jonathan N. Hawley
  • Vi Naylor
  • Kimberly J. Rask
Article

Abstract

This study examines the associations between the availability of IT applications in a hospital and that hospital’s risk adjusted incidence rate per 1,000 hospitalizations for Agency for Healthcare Research and Quality’s (AHRQ) 15 Patient Safety Indicators (PSIs). The study population consists of a convenience sample of 66 community hospitals in Georgia that completed a Hospital IT survey by December 2003 and provided data to Georgia Hospital Discharge Data Set during 2004. AHRQ’s PSI software was used to estimate risk adjusted incidence rates. Differences in means, Pearson correlation coefficients, and multivariate regression analysis were used to determine if the availability of IT applications were associated with better PSI outcomes. This study finds very little statistically significant correlation between the availability of IT applications and risk adjusted PSI incident rate per 1,000 hospitalizations. In the multivariate regression models, the overall availability of IT applications in a hospital was significantly and negatively associated with the risk adjusted incident rate for only postoperative hemorrhage or hematoma. The count of functional applications available was negatively associated with postoperative hemorrhage or hematoma and foreign body left during procedure, while the count of technological devices was only associated with postoperative hemorrhage or hematoma. This study finds that the overall number of functional applications and technological devices available in a hospital is not associated with improved risk adjusted PSI outcomes. Future research is needed to examine if specific IT applications in specific clinical areas of the hospital are associated with improved PSI outcomes.

Keywords

Hospital information technology Patient safety indicators Risk adjusted outcomes 

References

  1. 1.
    Institute of Medicine, To Err is human: Building a safer health system. National Academy of Sciences: National Academy Press, Washington, DC, 2000.Google Scholar
  2. 2.
    Patient safety indicators, version 2.1, revision, 3a. Agency for Healthcare Research and Quality, Rockville, MD, 2005. http://www.qualityindicators.ahrq.gov/psi-download.htm.
  3. 3.
    Remus, D., and Fraser, I., Guidance for using the AHRQ quality indicators for hospital-level public reporting or payment. US Department of Health and Human Services Agency for Healthcare Research and Quality: Rockville, MD, 2004.Google Scholar
  4. 4.
    Gaba, D., Structural and organizational issues in patient safety: A comparison of health care to other high-hazard industries. Calif. Manag. Rev. 43(1):83–102, 2000.Google Scholar
  5. 5.
    Birkmeyer, J., Birkmeyer, C., Wennberg, D., and Young, M., Leapfrog safety standards: potential benefits of universal adoption. The Leapfrog Group: Washington, DC, 2000.Google Scholar
  6. 6.
    Massachusetts Technology Collaborative in partnership with New England Healthcare Institute. Advanced technologies to lower health care cost and improve quality. Massachusetts Technology Park Corporation, 2003.Google Scholar
  7. 7.
    Institute of Medicine. Crossing the quality chasm: A new health system for the 21st century: National Academy Press, Washington, DC, 2001.Google Scholar
  8. 8.
    Bates, D., Cohen, M., Leape, L., Overhage, J., Shabot, M., Sheridan, T., Reducing the frequency of errors in medicine using information technology. J. Am. Med. Inform. Assoc. 8:299–308, 2001.Google Scholar
  9. 9.
    Johnston, D., Pan, E., Walker, J., Bates, D. W., and Middleton, B., The value of computerized provider order entry in ambulatory settings: book & cd. Center for IT Leadership, 2003. http://www.citl.org.Google Scholar
  10. 10.
    Bates, D., and Gawande, A., Patient safety: Improving safety with information technology. New Engl. J. Med. 348(25):2526–2534, 2003.CrossRefGoogle Scholar
  11. 11.
    Potts, A., Barr, F., Gregory, D., Wright, L., Patel, N., Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics 113(1):59–60, 2004.CrossRefGoogle Scholar
  12. 12.
    Oren, E., Shaffer, E., and Guglielmo, B., Impact of emerging technologies on medication errors and adverse drug events. Am. J. Health-Sys. Pharm. 60(14):1447–1458, 2003.Google Scholar
  13. 13.
    Pare’, G., and Sicotte, C., Information technology sophistication in health care: An instrument validation study among Canadian hospitals. International Journal of Medical Informatics 63(3):205–233, 2001.CrossRefGoogle Scholar
  14. 14.
    Culler, S. D., Atherly, A., Walczak, S., Davis, A., Hawley, J., Rask, K., Naylor, V., and Thorpe, K., A comparison of urban and rural differences in the availability of hospital IT applications: Results from a survey of Georgia hospitals. J. Rural Health 22(3):242–247, 2006.CrossRefGoogle Scholar
  15. 15.
    Health grades quality study patient safety in American hospitals. Health Grades, Inc., 2004. http://www.healthgrades.com.
  16. 16.
    Health grades second annual patient safety in American hospital report. Health Grades, Inc, 2005. http://www.healthgrades.com.
  17. 17.
    Burke, D., Wang, B., Wang, T., and Diana, M., Exploring hospitals’ adoption of information technology. J. Med. Syst. 26(4):349–355, 2002.CrossRefGoogle Scholar
  18. 18.
    Brook, R., Menachemi, N., Burke, D., and Clawson, A., Patient safety-related information technology utilization in urban and rural hospitals. J. Med. Syst. 29(2):103–109, 2005.CrossRefGoogle Scholar
  19. 19.
    Menachemi, N., Burke, D., Clawson, A., and Brook, R., Information technologies in Florida’s rural hospitals: Does system affiliation matter? J. Rural Health 21(3):263–268, 2005.CrossRefGoogle Scholar
  20. 20.
    Nebeker, J. R., Hoffman, J. M., Weir, C. R., Bennett, C. L., and Hurdle, J. F., High rates of adverse drug events in a highly computerized hospital. Arch. Intern. Med. 165:1111–1116, 2005.CrossRefGoogle Scholar
  21. 21.
    Shulman, R., Singer, M., Goldstone, J., and Bellingan, G., Medication errors: A prospective cohort study of hand-written and computerized physician order entry in the intensive care unit. Crit. Care 9:R516–R521, 2005.CrossRefGoogle Scholar
  22. 22.
    Quan, H., Parson, G. A., and Ghali, W. A., Validity of information on comorbidity derived from ICD-9-CM administrative data. Med. Care 40:675–685, 2002.CrossRefGoogle Scholar
  23. 23.
    Romano, P. S., Chan, B. K., Schembri, M. E., and Rainwater, G. A., Can administrative data be used to compare postoperative complication rates across hospitals? Med. Care 40:856–867, 2002.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Steven D. Culler
    • 1
    • 4
  • Jonathan N. Hawley
    • 2
  • Vi Naylor
    • 3
  • Kimberly J. Rask
    • 1
    • 2
  1. 1.Rollins School of Public HealthEmory UniversityAtlantaUSA
  2. 2.Emory Center on Health Outcomes and QualityEmory UniversityAtlantaUSA
  3. 3.Georgia Hospital AssociationMariettaUSA
  4. 4.Department of Health Policy and Management, Rollins School of Public HealthEmory UniversityAtlantaUSA

Personalised recommendations