Measure of Clinical Information Technology Adoption
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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.
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.
참고문헌 (21)
- Mekhjian HS, "Immediate benefits realized following implementation of physician order entry at an academic medical center", J Am Med Inform Assoc, 9(5), pp.529~539 (2002)
- Parente ST, "Valuing hospital investment in information technology: does governance make a difference?", Health Care Financ Rev, 28(2), pp.31~43 (2006)
- Burke DE, "Exploring hospitals' adoption of information technology", J Med Syst, 26(4), pp.349~355 (2002)
- McCullough JS, "The adoption of hospital information systems", Health Econ, 17(5), pp.649~664 (2008)
- Fonkych K, "The state and pattern of health information technology adoption" (2005), Rand Corporation
- Borzekowski R, "Measuring the cost impact of hospital information systems: 1987-1994", J Health Econ, 28(5), pp.938~949 (2009)
- Parente ST, "Health information technology and patient safety: evidence from panel data", Health Aff (Millwood), 28(2), pp.357~360 (2009)
- The impact of health information technology on hospital productivity, "Lee J", National Bureau of Economic Research, ~, pp.NBER Working paper no. 18025 (2012)
- McCullough JS, "The effect of health information technology on quality in U.S. hospitals", Health Aff (Millwood), 29(4), pp.647~654 (2010)
- Jha AK, "Use of electronic health records in U.S. hospitals", N Engl J Med, 360(16), pp.1628~1638 (2009)
- Teich JM, "Effects of computerized physician order entry on prescribing practices", Arch Intern Med, 160(18), pp.2741~2747 (2000)
- Wang SJ, "A cost-benefit analysis of electronic medical records in primary care", Am J Med, 114(5), pp.397~403 (2003)
- Garg AX, "Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review", JAMA, 293(10), pp.1223~1238 (2005)
- Amarasingham R, "Clinical information technologies and inpatient outcomes: a multiple hospital study", Arch Intern Med, 169(2), pp.108~114 (2009)
- Furukawa MF, "Electronic medical records, nurse staffing, and nurse-sensitive patient outcomes: evidence from California hospitals, 1998-2007", Health Serv Res, 45(4), pp.941~962 (2010)
- "HIMSS Analytics" (2013)[URL]
- "Statistics in psychosocial research" (2006)[URL]
- Pett MA, "Making sense of factor analysis: the use of factor analysis for instrument development in health care research" (2003), Sage Publications
- Zuur AF, "Dynamic factor analysis to estimate common trends in fisheries time series", Can J Fish Aquat Sci, 60(5), pp.542~552 (2003)
- Walden EA, "Sequential adoption theory: a theory for understanding herding behavior in early adoption of novel technologies", J Assoc Inf Syst, 10(1), pp.31~62 (2009)
- Rogers EM, "Diffusion of innovation" (2003), Free Press