Associations between the concurrent use of clinical decision support and computerized provider order entry and the rates of appropriate prescribing at discharge
10. November 2011
accepted: 10. Mai 2012
16. Dezember 2017 (online)
Introduction: Electronic health record systems used in conjunction with clinical decision support (CDS) or computerized provider order entry (CPOE) have shown potential in improving quality of care, yet less is known about the effects of combination use of CDS and CPOE on prescribing rates at discharge.
Objectives: This study investigates the effectiveness of combination use of CDS and CPOE on appropriate drug prescribing rates at discharge for AMI or HF patients.
Methods: Combination use of CDS and CPOE is defined as hospitals self-reporting full implementation across all hospital units of CDS reminders, CDS guidelines, and CPOE. Appropriate prescribing rates of aspirin, ACEI/ARBs, or beta blockers are defined using quality measures from Hospital Compare. Multivariate linear regressions are used to test for differences in mean appropriate prescribing rates between hospitals reporting combination use of CDS and CPOE, compared to those reporting the singular use of one or the other, or the absence of both. Covariates include hospital size, region, and ownership status.
Results: Approximately 10% of the sample reported full implementation of both CDS and CPOE, while 7% and 17% reported full use of only CPOE or only CDS, respectively. Hospitals reporting full use of CDS only reported between 0.2% (95% CI 0.04 – 1.0) and 1.6% (95% CI 0.6 – 2.6) higher appropriate prescribing rates compared to hospitals reporting use of neither system. Rates of prescribing by hospitals reporting full use of both CPOE and CDS did not significantly differ from the control group.
Conclusions: Although associations found between full implementation of CDS and appropriate prescribing rates suggest that clinical decision tools are sufficient compared to basic EHR systems in improving prescribing at discharge, the modest differences raise doubt about the clinical relevance of the findings. Future studies need to continue investigating the causal nature and clinical relevance of these associations.
- 1 Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003; 348 (025) 2526-2534.
- 2 Eslami S, de Keizer NF, Abu-Hanna A. The impact of computerized physician medication order entry in hospitalized patients –a systematic review. Int J Med Inform 2008; 77 (06) 365-376.
- 3 Mahoney CD, Beard-Collins CM, Coleman R, Amaral JF, Cotter CM. Effects of an integrated clinical information system on medication safety in a multihospital setting. Am J Health-Syst Pharm 2007; 64: 1969-1977.
- 4 Shamliyan TA, Duval S, Du J, Kane RL. Just what the doctor ordered. Review of the evidence of the impact of computerized physician order entry system on medication errors. Health Serv Res 2008; 43 1 Pt 1 32-53.
- 5 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 (02) 108-114.
- 6 DesRoches CM, Campbell EG, Vogeil C, Zheng J, Rao SR, Shields AE. et al. Electronic health records’ limited successes suggest more targeted uses. Health Aff (Millwood) 2010; 29 (04) 639-646.
- 7 Jones SS, Heaton P, Friedberg MW, Schneider EC. Today’s ‘meaningful use’ standard for medication orders by hospitals may save few lives; later stages may do more. Health Aff (Millwood) 2011; 30 (010) 2005-2012.
- 8 McCullough JS, Casey M, Moscovice I, Prasad S. The effect of health information technology o quality in U. S. hospitals. Health Aff (Millwood) 2010; 29 (04) 647-654.
- 9 Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD. et al. Incidence of adverse drug events and potential adverse drug events: Implications for prevention. ADE Prevention Study Group. JAMA 1995; 274 (01) 29-34.
- 10 Kuperman GJ, Teich JM, Gandhi TK, Bates DW. Patient safety and computerized medication ordering at Brigham and Women’s Hospital. Jt Comm J Qual Improv 2001; 27 (010) 509-521.
- 11 Zlabek JA, Wickus JW, Mathiason MA. Early cost and safety benefits of an inpatient electronic health record. J Am Med Inform Assoc 2011; 18 (02) 169-172.
- 12 Garg AX, Adhikari NKJ, 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 (010) 1223-1238.
- 13 Dexheimer JW, Talbot TR, Sanders DL, Rosenbloom ST, Aronsky D. Prompting clinicians about preventive care measures: A systematic review of randomized controlled trials. J Am Med Inform Assoc 2008; 15 (03) 311-320.
- 14 American Hospital Association.. 2008 Hospital EHR Adoption Database [supplement to FY2007 AHA Annual Survey Database]. Chicago: Health Forum,; 2009
- 15 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 (016) 1628-1638.
- 16 Department of Health and Human Services.. Hospital Compare. http://www.hospitalcompare.hhs.gov/sta ticpages/for-professionals/poc/data-collection.aspx. Accessed May 1, 2011.
- 17 US Department of Health and Human Services ‘Hospital Compare: Information for Professionals on Data Collection‘.. http://www.hospitalcompare.hhs.gov/staticpages/for-professionals/poc/data-collection.aspx. Accessed 10/20/11.
- 18 The Joint Commission (TJC).. 2010 ORYX Performance Measure Reporting Guidelines for Hospitals and Guidelines for Measure Selections. http://www.jointcommission.org/assets/1/18/2010_ORYX_Performance_Measure_Reporting_Requirements.pdf Accessed 02/21/12.
- 19 U.S Department of Health and Human Services ‘Hospital Compare: Technical Appendix’.. http://www.hos pitalcompare.hhs.gov/staticpages/for-professionals/poc/technical-appendix.aspx. Accessed 02/21/12.
- 20 Blumenthal D, DesRoches C, Donelan K. et al. Health information technology in the United States: the information base for progress. Princeton, NJ: Robert Wood Johnson Foundation,; 2006