Healthc Inform Res.  2013 Mar;19(1):56-62. 10.4258/hir.2013.19.1.56.

Measure of Clinical Information Technology Adoption

• Lee J
• Park YT

• KMID: 2166664
• DOI: http://doi.org/10.4258/hir.2013.19.1.56

Abstract

OBJECTIVES
The objective of this study was to create a new measure for clinical information technology (IT) adoption as a proxy variable of clinical IT use.
METHODS
Healthcare Information and Management Systems Society (HIMSS) data for 2004 were used. The 18 clinical IT applications were analyzed across 3,637 acute care hospitals in the United States. After factor analysis was conducted, the clinical IT adoption score was created and evaluated.
RESULTS
Basic clinical IT systems, such as laboratory, order communication/results, pharmacy, radiology, and surgery information systems had different adoption patterns from advanced IT systems, such as cardiology, radio picture archiving, and communication, as well as computerized practitioner order-entry. This clinical IT score varied across hospital characteristics.
CONCLUSIONS
Different IT applications have different adoption patterns. In creating a measure of IT use among various IT components in hospitals, the characteristics of each type of system should be reflected. Aggregated IT adoption should be used to explain technology acquisition and utilization in hospitals.

Keyword

Medical Informatics; Health Information Technology; Clinical Informatics; Medical Information Science

MeSH Terms

Adoption
Cardiology
Delivery of Health Care
Humans
Information Systems
Medical Informatics
Pharmacy
Proxy
United States

Reference

1. Mekhjian HS, Kumar RR, Kuehn L, Bentley TD, Teater P, Thomas A, et al. Immediate benefits realized following implementation of physician order entry at an academic medical center. J Am Med Inform Assoc. 2002. 9(5):529–539.

2. Parente ST, Van Horn RL. Valuing hospital investment in information technology: does governance make a difference? Health Care Financ Rev. 2006. 28(2):31–43.

3. Burke DE, Wang BB, Wan TT, Diana ML. Exploring hospitals' adoption of information technology. J Med Syst. 2002. 26(4):349–355.

4. McCullough JS. The adoption of hospital information systems. Health Econ. 2008. 17(5):649–664.

5. Fonkych K, Taylor R. The state and pattern of health information technology adoption. 2005. Santa Monica (CA): Rand Corporation.

6. Borzekowski R. Measuring the cost impact of hospital information systems: 1987-1994. J Health Econ. 2009. 28(5):938–949.

7. Parente ST, McCullough JS. Health information technology and patient safety: evidence from panel data. Health Aff (Millwood). 2009. 28(2):357–360.

8. Lee J, McCullough JS, Town RJ. NBER Working paper no. 18025. The impact of health information technology on hospital productivity. 2012. Cambridge (MA): National Bureau of Economic Research.

9. McCullough JS, Casey M, Moscovice I, Prasad S. The effect of health information technology on quality in U.S. hospitals. Health Aff (Millwood). 2010. 29(4):647–654.

10. Jha AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, et al. Use of electronic health records in U.S. hospitals. N Engl J Med. 2009. 360(16):1628–1638.

11. Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, Bates DW. Effects of computerized physician order entry on prescribing practices. Arch Intern Med. 2000. 160(18):2741–2747.

12. Wang SJ, Middleton B, Prosser LA, Bardon CG, Spurr CD, Carchidi PJ, et al. A cost-benefit analysis of electronic medical records in primary care. Am J Med. 2003. 114(5):397–403.

13. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005. 293(10):1223–1238.

14. Amarasingham R, Plantinga L, Diener-West M, Gaskin DJ, Powe NR. Clinical information technologies and inpatient outcomes: a multiple hospital study. Arch Intern Med. 2009. 169(2):108–114.

15. Furukawa MF, Raghu TS, Shao BB. Electronic medical records, nurse staffing, and nurse-sensitive patient outcomes: evidence from California hospitals, 1998-2007. Health Serv Res. 2010. 45(4):941–962.

16. HIMSS Analytics [Internet]. c2013. cited at 2013 Mar 18. Chicago (IL): HIMSS Analytics;Available from: http://www.himssanalytics.org/.

17. Garrett-Mayer E. Statistics in psychosocial research [Internet]. c2006. cited at 2013 Mar 18. Baltimore (MD): The John Hopkins University;Available from: http://ocw.jhsph.edu/courses/statisticspsychosocialresearch/pdfs/lecture8.pdf.

18. Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: the use of factor analysis for instrument development in health care research. 2003. Thousand Oaks (CA): Sage Publications.

19. Zuur AF, Tuck ID, Bailey N. Dynamic factor analysis to estimate common trends in fisheries time series. Can J Fish Aquat Sci. 2003. 60(5):542–552.

20. Walden EA, Browne GJ. Sequential adoption theory: a theory for understanding herding behavior in early adoption of novel technologies. J Assoc Inf Syst. 2009. 10(1):31–62.

21. Rogers EM. Diffusion of innovation. 2003. 5th ed. New York (NY): Free Press.