American Journal of Infection Control 42 (2014) e33-e36

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American Journal of Infection Control

American Journal of Infection Control

journal homepage: www.ajicjournal.org

Major article

Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting John Shepard MBA, MHA a, *, Eric Hadhazy MS b, John Frederick BA c, Spencer Nicol BA d, Padmaja Gade BS a, Andrew Cardon BA d, Jorge Wilson BA, MS a, Yohan Vetteth BA a, Sasha Madison MPH, CIC e a

Department of Clinical Business Analytics, Stanford Hospital and Clinics, Stanford, CA Department of Quality, Patient Safety, and Effectiveness, Stanford Hospital and Clinics, Stanford, CA c Department of Hospital Epidemiology, Veterans Administration, New York, NY d Health Catalyst, Salt Lake City, UT e Infection Prevention and Control Department, Stanford Hospital and Clinics, Stanford, CA b

Key Words: Infection control surveillance Electronic surveillance Automated surveillance CAUTI CAUTI surveillance Cost reduction

Background: Streamlining health careeassociated infection surveillance is essential for health care facilities owing to the continuing increases in reporting requirements. Methods: Stanford Hospital, a 583-bed adult tertiary care center, used their electronic medical record (EMR) to develop an electronic algorithm to reduce the time required to conduct catheter-associated urinary tract infection (CAUTI) surveillance in adults. The algorithm provides inclusion and exclusion criteria, using the National Healthcare Safety Network definitions, for patients with a CAUTI. The algorithm was validated by trained infection preventionists through complete chart review for a random sample of cultures collected during the study period, September 1, 2012, to February 28, 2013. Results: During the study period, a total of 6,379 positive urine cultures were identified. The Stanford Hospital electronic CAUTI algorithm identified 6,101 of these positive cultures (95.64%) as not a CAUTI, 191 (2.99%) as a possible CAUTI requiring further validation, and 87 (1.36%) as a definite CAUTI. Overall, use of the algorithm reduced CAUTI surveillance requirements at Stanford Hospital by 97.01%. Conclusions: The electronic algorithm proved effective in increasing the efficiency of CAUTI surveillance. The data suggest that CAUTI surveillance using the National Healthcare Safety Network definitions can be fully automated. Copyright Ó 2014 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

State and national mandates require the reporting of select health careeassociated infections (HAIs) to the Center of Disease Control and Prevention’s (CDC) National Health Safety Network (NHSN).1-3 Catheter-associated urinary tract infections (CAUTIs) are HAIs reported for both accreditation purposes and regulatory requirements.4-6 CAUTIs are considered the most common HAI in the United States, associated with increased health care costs and mortality,7 and identifying CAUTIs is costly and time-consuming for infection prevention departments.1,6,8-11

Given the time and costs associated with HAI surveillance, many facilities are looking to their electronic medical record (EMR) to provide a more efficient process. The purpose of this study was to develop an accurate and completely automated electronic algorithm, using our EPIC 2012 EMR, which eliminates the cost of CAUTI surveillance. The primary study outcome was the number of urine cultures in which the electronic algorithm could automatically identify the presence or absence of a CAUTI. METHODS

* Address correspondence to John Shepard, MBA, MHA, 940 Oakes St, East Palo Alto, CA 94303. E-mail address: [email protected] (J. Shepard). This study was funded through hospital quality improvement initiatives. Conflict of interest: None to report.

Stanford Hospital, a 583-bed tertiary care center, assigned a multidisciplinary team of front-line providers, infection preventionists, and clinical informaticists to create an automated electronic algorithm and data reporting system to increase the

0196-6553/$36.00 - Copyright Ó 2014 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ajic.2013.12.005

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Fig 1. Stanford Hospital electronic CAUTI algorithm ruling and code descriptions.

J. Shepard et al. / American Journal of Infection Control 42 (2014) e33-e36

efficiency of CAUTI surveillance and reporting. Using the CDC definitions published in January 2013,12 the team designed an algorithm that provides inclusion and exclusion criteria for patients meeting the criteria for a CAUTI. The team visually displayed the algorithm logic using Microsoft PowerPoint, and the algorithm was coded in Microsoft Management Studio, using standard query language (SQL) to extract the data from the Epic 2012 Clarity database. Approximately 10 iterations of the algorithm design were performed before the algorithm was deemed suitable for testing. The team identified that all patients with a CAUTI must have a positive urine culture, and that it is possible to have more than 1 CAUTI in a single patient admission. Given these parameters, the automated electronic algorithm was designed to review all positive urine cultures collected in the hospital and to assign each urine culture to 1 of 2 categories, definite CAUTI or not a CAUTI. Unfortunately, the Epic 2012 EMR used at Stanford Hospital could not capture all of the data required for a completely automated algorithm. The EMR did not capture electronically if a patient had urinary urgency with no other recognized cause except urinary tract infection (UTI), frequency with no other recognized cause except the UTI, dysuria with no other recognized cause except the UTI, suprapubic tenderness with no other recognized cause except the UTI, or costovertebral angle pain or tenderness. These data elements are required for the use of a completely automated algorithm for CAUTI surveillance.12 Given the limitations of the data, the team tailored the automated algorithm to be compatible with the Stanford Hospital EMR. For this study, the revised algorithmdthe Stanford Hospital Electronic CAUTI Algorithm (SHECA)dwas used to review all of the positive urine cultures collected in the hospital between September 1, 2012, and February 28, 2013. On review, the SHECA placed each positive urine culture in 1 of 3 categories: definite CAUTI, not a CAUTI, and possible CAUTI. The cultures deemed a possible CAUTI were those cultures that infection preventionists at Stanford Hospital still needed to conduct a chart review on, because the SHECA did not have sufficient data to identify the culture as a definite CAUTI or not a CAUTI. These results were then used to evaluate the change in the efficiency of CAUTI surveillance from use of the SHECA. To validate the algorithm, 2 clinical informaticists conducted a retrospective electronic chart review to identify all positive urine cultures reported by the Stanford Hospital’s laboratory between September 1, 2012, and February 28, 2013. A random sample of approximately 150 positive urine cultures was then blindly reviewed by both the SHECA and 2 infection preventionists to identify whether they met the NHSN definition of a CAUTI. The infection preventionists and clinical informaticists then reviewed a random sample of positive urine cultures from every algorithm score, to ensure that each patient had the appropriate algorithm score and that the algorithm score was accurately designating the patient as having or not having a CAUTI. No discrepancies in results from the SHECA and the validation team were identified. This study was ruled exempt by the internal review board. Details of the algorithm design are available on request. RESULTS Using the SHECA, we retrospectively analyzed all positive urine cultures collected at Stanford Hospital between September 1, 2012, and February 28, 2013. During this period, 6,379 positive urine cultures were recorded; of these, the SHECA identified 6,101 (95.64%) as not a CAUTI because the positive culture did not meet the NHSN criteria; 191 (2.99%) as a possible CAUTI because there were insufficient data to verify whether the culture was or was not a CAUTI; and 87 (1.36%) as a definite CAUTI (Fig 1). Overall, the

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electronic algorithm reduced CAUTI surveillance requirements at Stanford Hospital by 97.01%. DISCUSSION Infection prevention surveillance is a very time-consuming and resource-intensive process. It is estimated that 45% of infection preventionists’ time is spent on surveillance and analysis.13 With the ever-increasing reporting requirements for infection prevention departments2,14,15 comes the need to maximize the efficiency of surveillance and analysis. As a result of the mass adoption of EMRs in recent years, hospitals now have the ability to leverage their data to increase the efficiency of HAI surveillance. During the course of this study, we made minor changes to our Epic 2012 EMR, but we were not able to make all of the changes needed to implement the automated surveillance algorithm. For this reason, we were able to reduce the level of CAUTI surveillance only by approximately 97%. However, our data suggest that health care facilities could reconfigure their EMRs to implement a completely automated electronic algorithm for CAUTI surveillance. Limitations of this study include the level of configurability in our facility’s EMR. To fully test the reliability of our automated algorithm, we would need to implement the algorithm at another facility that has an appropriately configured EMR. Given this limitation, we were unable to verify whether our algorithm could completely automate CAUTI surveillance. Future studies should investigate the development and implementation of similar algorithms for other HAIs. CONCLUSION Our study suggests that health care facilities, regardless of the EMR in use, available data, or available funds, can effectively leverage their current EMR infrastructure by investing in low-cost solutions to greatly increase the efficiency and effectiveness of HAI surveillance. The health care environment is requiring hospitals to reduce costs and improve quality; infection prevention departments are focused on achieving both. However, growing surveillance requirements hinders infection preventionists from devoting time to valuable clinical interventions. The design provided by our team allows for facilities of all levels to increase the efficiency of HAI surveillance, freeing up more time for implementing much-needed quality interventions. References 1. Stevenson KB, Khan Y, Dickman J, Gillenwater T, Kulich P, Myers C, et al. Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections. Am J Infect Control 2008;36:15564. 2. Julian KG, Brumbach AM, Chicora MK, Houlihan C, Riddle AM, Umberger T, et al. First year of mandatory reporting of healthcare-associated infections, Pennsylvania: an infection control-chart abstractor collaboration. Infect Control Hosp Epidemiol 2006;27:926-30. 3. Reagan J, Hacker C. Laws pertaining to healthcare-associated infections: a review of 3 legal requirements. Infect Control Hospital Epidemiol 2012;33:75-80. 4. Meddings JA, Reichert H, Rogers MA, Saint S, Stephansky J, McMahon LF. Effect of nonpayment for hospital-acquired, catheter-associated urinary tract infection: a statewide analysis. Ann Intern Med 2012;157:305-12. 5. Gould CV, Umscheid CA, Agarwal RK, Kuntz G, Pegues DA. Healthcare Infection Control Practices Advisory Committee. Guideline for prevention of catheterassociated urinary tract infections 2009. Infect Control Hosp Epidemiol 2010; 31:319-26. 6. Wright MO, Kharasch M, Beaumont JL, Peterson LR, Robicsek A. Reporting catheter-associated urinary tract infections: denominator matters. Infect Control Hosp Epidemiol 2011;32:635-40. 7. Klevens RM, Edwards JR, Richards CL Jr, Horan TC, Gaynes RP, Pollock DA, et al. Estimating health careeassociated infections and deaths in US hospitals, 2002. Public Health Rep 2007;122:160-6.

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8. Sherman ER, Heydon KH, St John KH, Teszner E, Rettig SL, Alexander SK, et al. Administrative data fail to accurately identify cases of healthcare-associated infection. Infect Control Hosp Epidemiol 2006;27:332-7. 9. Tambyah PA, Knasinski V, Maki DG. The direct costs of nosocomial catheterassociated urinary tract infection in the era of managed care. Infect Control Hosp Epidemiol 2002;23:27-31. 10. Haley RW, Culver DH, White JW, Morgan WM, Emori TG, Munn VP, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985;121: 182-205. 11. Wright SB, Huskins WC, Dokholyan RS, Goldmann DA, Platt R. Administrative databases provide inaccurate data for surveillance of long-term central venous catheter-associated infections. Infect Control Hosp Epidemiol 2003; 24:946-9.

12. Centers for Disease Control and Prevention. April 2013 CDC/NHSN protocol corrections, clarification, and additions: catheter-associated urinary tract infection (CAUTI) event. Available from: http://www.cdc.gov/nhsn/pdfs/ pscManual/7pscCAUTIcurrent.pdf. Accessed January 7, 2014. 13. Stone PW, Dick A, Pogorzelska M, Horan TC, Furuya EY, Larson E. Staffing and structure of infection prevention and control programs. Am J Infect Control 2009;37:351-7. 14. McKibben L, Horan TC, Tokars JI, Fowler G, Cardo DM, Pearson ML, et al. Guidance on public reporting of healthcare-associated infections: recommendations of the Healthcare Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol 2005;26:580-7. 15. Stone PW, Horan TC, Shih HC, Mooney-Kane C, Larson E. Comparisons of health careeassociated infections identification using two mechanisms for public reporting. Am J Infect Control 2007;35:145-9.

Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting.

Streamlining health care-associated infection surveillance is essential for health care facilities owing to the continuing increases in reporting requ...
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