Quality Outcomes in the Surgical Intensive Care Unit after Electronic Health Record Implementation

Summary

Background: The electronic health record (EHR) is increasingly viewed as a means to provide more coordinated, patient-centered care. Few studies consider the impact of EHRs on quality of care in the intensive care unit (ICU) setting.

Objectives: To evaluate key quality measures of a surgical intensive care unit (SICU) following implementation of the Epic EHR system in a tertiary hospital.

Methods: A retrospective chart review was undertaken to record quality indicators for all patients admitted to the SICU two years before and two years after EHR implementation. Data from the twelve-month period of transition to EHR was excluded. We collected length of stay, mortality, central line associated blood stream infection (CLABSI) rates, Clostridium difficile (C. diff.) colitis rates, readmission rates, and number of coded diagnoses. To control for variation in the patient population over time, the case mix indexes (CMIs) and APACHE II scores were also analyzed.

Results: There was no significant difference in length of stay, C. diff. colitis, readmission rates, or case mix index before and after EHR. After EHR implementation, the rate of central line blood stream infection (CLABSI) per 1 000 catheter days was 85% lower (2.16 vs 0.39; RR, 0.18; 95% CI, 0.05 to 0.61, p < .005), and SICU mortality was 28% lower (12.2 vs 8.8; RR, 1.35; 95% CI, 1.06 to 1.71, p < .01). Moreover, after EHR there was a significant increase in the average number of coded diagnoses from 17.8 to 20.8 (p < .000).

Conclusions: EHR implementation was statistically associated with reductions in CLABSI rates and SICU mortality. The EHR had an integral role in ongoing quality improvement endeavors which may explain the changes in CLABSI and mortality, and this invites further study of the impact of EHRs on quality of care in the ICU.

Keywords

Electronic health records - quality improvement - critical care - surgical intensive care

 

Conflicts of interest

The authors of this study report no conflicts of interest.

• References

• 1 Health expenditure, total (% of GDP) [internet].. Washington (DC): The World Bank Group; 2015 [cited 2015 Mar 1]. Available from: http://data.worldbank.org/indicatorSH. XPD. TOTL. ZS?order=wbapi_data_value_2011+wbapi_data_value+wbapi_data_value-last&sort=desc> .
• 2 McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, Kerr EA. The Quality of Health Care Delivered to Adults in the United States. New Engl J Med 2003; 348: 2635-2645.
• 3 Blumenthal D, Tavenner M. The ‘Meaningful Use’ Regulation for Electronic Health Records. New Engl J Med 2010; 363: 501-504.
• 4 Moukheiber Z. The Staggering Cost Of An Epic Electronic Health Record Might Not Be Worth It. 2012 Jun 8 [cited 2015 Mar 31]. In: Forbes Business [Internet]. New York: Forbes. Available from: <http://www. forbes.com/sites/zinamoukheiber/2012/06/18/the-staggering-cost-of-an-epic-electronic-health-record-might-not-be-worth-it/ ;.
• 5 Mandl KD, Kohane IS. Escaping the EHR Trap –The Future of Health IT. New Engl J Med 2012; 366: 2240-2242.
• 6 Institute of Medicine.. Key Capabilities of an Electronic Health Record System: Letter Report. National Academies Press; 2003
• 7 Himmelstein DU, Wright A, Woolhandler S. Hospital computing and the costs and quality of care: a national study. Am J Med 2010; 123: 40-46.
• 8 Linder JA, Ma J, Bates DW, Middleton B, Stafford RS. Electronic health record use and the quality of ambulatory care in the United States. Archives of Internal Medicine 2007; 167: 1400-1405.
• 9 Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med 2011; 171: 897.
• 10 Keyhani S, Hebert PL, Ross JS, Federman A, Zhu CW, Siu AL. Electronic Health Record Components and the Quality of Care. Medical Care 2008; 46: 1267-1272.
• 11 Jones SS, Heaton PS, Rudin RS, Schneider EC. Unraveling the IT Productivity Paradox –Lessons for Health Care. New Engl J Med 2012; 366: 2243-2245.
• 12 Reed M, Huang J, Brand R, Graetz I, Neugebauer R, Fireman B, Jaffe M, Ballard DW, Hsu J. Implementation of an outpatient electronic health record and emergency department visits, hospitalizations, and office visits among patients with diabetes. JAMA 2013; 310: 1060-1065.
• 13 Cebul RD, Love TE, Jain AK, Hebert CJ. Electronic Health Records and Quality of Diabetes Care. New Engl J Med 2011; 365: 825-833.
• 14 Sankilampi U, Saari A, Laine T, Miettinen PJ, Dunkel L. Use of electronic health records for automated screening of growth disorders in primary care. JAMA 2013; 310: 1071-1072.
• 15 Garg AX, Adhikari NKJ, McDonald H. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes. JAMA 2005; 293: 1223-1238.
• 16 Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B, Goldhaber SZ. Electronic alerts to prevent venous thromboembolism among hospitalized patients. New Engl J Med 2005; 352: 969-977.
• 17 Beccaro MAD, Jeffries HE, Eisenberg MA, Harry ED. Computerized Provider Order Entry Implementation: No Association With Increased Mortality Rates in an Intensive Care Unit. Pediatrics 2006; 118: 290-295.
• 18 Han YY, Carcillo JA, Venkataraman ST, Clark RS, Watson RS, Nguyen TC, Bayir H, Orr RA. Unexpected Increased Mortality After Implementation of a Commercially Sold Computerized Physician Order Entry System. Pediatrics 2005; 116: 1506-1512.
• 19 van Rosse F, Rademaker CM, van Vught AJ, Egberts AC, Bollen CW. The effect of computerized physician order entry on medication prescription errors and clinical outcome in pediatric and intensive care: a systematic review. Pediatrics 2009; 123: 1184-1190.
• 20 Friedberg MW, Coltin KL, Safran DG, Dresser M, Zaslavsky AM, Schneider EC. Associations between structural capabilities of primary care practices and performance on selected quality measures. Annals of Internal Medicine 2009; 151: 456-463.
• 21 Hebert C, Du H, Peterson LR, Robicsek A. Electronic Health Record–Based Detection of Risk Factors for Clostridium difficile Infection Relapse. Infection Control and Hospital Epidemiology 2013; 34: 407-414.
• 22 Pronovost P, Berenholtz S, Dorman T, Lipsett PA, Simmonds T, Haraden C. Improving communication in the ICU using daily goals. Journal of Critical Care 2003; 18: 71-75.
• 23 Kahn KL, Weinberg DA, Leuschner KJ, Gall EM, Siegel S, Mendel P. The National Response for Preventing Healthcare-associated Infections: Data and Monitoring. Medical Care 2014; 52: S25-S32.
• 24 Kumar A, Roberts D, Wood KE, Light B, Parrillo JE, Sharma S, Suppes R, Feinstein D, Zanotti S, Taiberg L, Gurka D, Kumar A, Cheang M. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Critical care medicine 2006; 34: 1589-1589.
• 25 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: 108-114.
• 26 O’Grady NP, Alexander M, Burns LA, Dellinger EP, Garland J, Heard SO, Lipsett PA, Masur H, Mermel LA, Pearson ML, Raad II, Randolph AG, Rupp ME, Saint S. Healthcare Infection Control Practices Advisory Committee (HICPAC). Guidelines for the prevention of intravascular catheter–related infections. Clinical infectious diseases 2011; 52 (09) e162-e193.