The objective was to examine the relationship between hospital adoption of information technology (IT) and hospital-acquired venous thromboembolism (VTE). Inpatients aged 65 years and older who were discharged from hospitals in California, Florida, and New York were analyzed. A cross-sectional study design was used to perform secondary data analyses. The association between implementing IT applications (ie, electronic clinical documentation, electronic lab orders, medication orders written electronically) in the patients’ hospitals (independent variables) and hospital-acquired VTE (the dependent variable) were studied using a hierarchical logistic model. Electronic clinical documentation was associated with lower odds of hospital-acquired VTE (odds ratio [OR] = 0.835, P < .001). An even larger reduction in the odds was found among patients of hospitals that implemented electronic lab orders (OR = 0.627, P < .001). However, the implementation of electronic medication orders did not show such an association.
US Department of Health and Human Services. Surgeon general’s call to action to prevent deep vein thrombosis and pulmonary embolism 2008. http://www.surgeongeneral.gov/topics/deepvein. Accessed August 3, 2010.
2.
AndersonFAJrWheelerHBGoldbergRJ. A population-based perspective of the hospital incidence and case-fatality rates of deep vein thrombosis and pulmonary embolism: the Worcester DVT Study. Arch Intern Med. 1991;151:933-938.
3.
GeertsWHBergqvistDPineoGF; American College of Chest Physicians. Prevention of venous thromboembolism: American College of Chest Physicians evidence-based clinical practice guidelines (8th edition). Chest. 2008;133:381-453.
4.
HeitJASilversteinMDMohrDNPettersonTMO’FallonWMMeltonLJIII. Risk factors for deep vein thrombosis and pulmonary embolism: a population-based case-control study. Arch Intern Med. 2000;160:809-815.
5.
SilversteinMDHeitJAMohrDNPettersonTMO’FallonWMMeltonLJIII. Trends in the incidence of deep vein thrombosis and pulmonary embolism: a 25-year population-based study. Arch Intern Med. 1998;158:585-593.
6.
GoldhaberSZTapsonVF. A prospective registry of 5,451 patients with ultrasound-confirmed deep vein thrombosis. Am J Cardiol. 2004;93:259-262.
7.
ShojaniaKGDuncanBWMcDonaldKMWachterRMMarkowitzAJ. Making health care safer: a critical analysis of patient safety practices. Evid Rep Technol Assess (Summ). 2001;43:i-x, 1-668.
8.
TapsonVFHyersTMWaldoAL. Antithrombotic therapy practices in US hospitals in an era of practice guidelines. Arch Intern Med. 2005;165:1458-1464.
9.
Centers for Medicare and Medicaid Services. Medicare program: changes to the hospital inpatient prospective payment systems and fiscal year 2009 rates. www.cms.hhs.gov/AcuteInpatientPPS/downloads/CMS-1390-F.pdf. Accessed October 6, 2010.
BahlVHuHHenkePKWakefieldTWCampbellDAJrCapriniJA. A validation study of a retrospective venous thromboembolism risk scoring method. Ann Surg. 2010;251: 344-350.
12.
CapriniJA. Thrombosis risk assessment as a guide to quality patient care. Dis Mon. 2005;51:70-78.
13.
DurieuxPNizardRRavaudPMounierNLepageE. A clinical decision support system for prevention of venous thromboembolism: effect on physician behavior. JAMA. 2000;283:2816-2821.
14.
DexterPRPerkinsSOverhageJMMounierNLepageE. A computerized reminder system to increase the use of preventive care for hospitalized patients. N Engl J Med. 2001;345:965-970.
15.
KucherNKooSQuirozR. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med. 2005;352:969-977.
16.
JhaAFerrisTDonelanK. How common are electronic health records in the United States? A summary of the evidence. Health Aff (Millwood). 2006;25:w496-w507.
17.
JhaAKDesRochesCMCampbellEG. Use of electronic health records in U.S. hospitals. N Engl J Med. 2009;360:1628-1638.
18.
Medicare and Medicaid Programs. Electronic health record incentive program; final rule. Fed Regist. 2010;75:44314-44588.
19.
American Hospital Association. AHA Annual Survey Database™ Fiscal Year 2007 [CD-ROM]. Chicago, IL: American Hospital Association; 2008.
20.
American Hospital Association. Hospital EHR Adoption Database™ Fiscal Year 2007 [CD-ROM]. Chicago, IL: American Hospital Association; 2009.
BahlVThompsonMKauTYHuHCampbellDAJr. Do the AHRQ patient safety indicators flag conditions that are present at the time of hospital admission?Med Care. 2008;46:516-522.
23.
GlanceLGDickAWOslerTMMukamelDB. Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data?Health Serv Res. 2005;41:231-251.
24.
StukenborgGJWagnerDPHarrellFEJr. Hospital discharge abstract data on comorbidity improved the prediction of death among patients hospitalized with aspiration pneumonia. J Clin Epidemiol. 2004;57:522-532.
25.
Florida Center for Health Information and Policy Analysis. Statistical Brief Issue No.1: Injury and Present on Admission Codes in 2007 Inpatient discharges in Florida. Tallahassee, FL: Florida Agency for Health Care Administration; 2009.
Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project (HCUP): Clinical Classification Software (CCS) for ICD-9-CM. www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed July 20, 2010.
29.
DeitelzweigSBLinJJohnsonBHSchulmanKL. Venous thromboembolism in the US: does race matter?J Thromb Thrombolysis. 2011;31:133-138.
30.
HooperWC. Venous thromboembolism in African-Americans: a literature-based commentary. Thromb Res. 2010;125:12-18.
31.
KlatskyALArmstrongMAPoggiJ. Risk of pulmonary embolism and/or deep venous thrombosis in Asian-Americans. Am J Cardiol. 2000;85:1334-1337.
32.
KucherNTapsonVFQuirozR. Gender differences in the administration of prophylaxis to prevent deep venous thrombosis. Thromb Haemost. 2005;93:284-288.
33.
ZhanCMillerMR. Excess length of stay, charges, and mortality attributable to medical injuries during hospitalization. JAMA. 2003;290:1868-1874.
RomanoPSGeppertJJDaviesSMillerMRElixhauserAMcDonaldKM. A national profile of patient safety in U.S. hospitals. Health Aff (Millwood). 2003;22:154-166.
36.
RaetzmanSStrangesECoffeyRM. Patient safety in hospitals in 2004: toward understanding variation across states. HCUP Methods Series Report # 2008-02. http://www.hcup-us.ahrq.gov/reports/methods.jsp. Accessed October 1, 2010.
37.
Birman-DeychEWatermanADYanYNilasenaDSRadfordMJGageBF. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care. 2005;43:480-485.
38.
JohnstonTCCooryMDScottIDuckettS. Should we add clinical variables to administrative data? The case of risk-adjusted case fatality rates after admission for acute myocardial infarction. Med Care. 2007;45:1180-1185.
