- Email: [email protected]
Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs
Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs Helen Halpin, ScM, PhD,a Stephen M. Shortell, PhD, MBA, MPH,a Arnold Milstein, MD, MPH,b and Megan Vanneman, MPHa Berkeley and Palo Alto, California
Background: This research analyzes the relationship between hospital use of automated surveillance technology (AST) for identification and control of hospital-acquired infections (HAI) and implementation of evidence-based infection control practices. Our hypothesis is that hospitals that use AST have made more progress implementing infection control practices than hospitals that rely on manual surveillance. Methods: A survey of all acute general care hospitals in California was conducted from October 2008 through January 2009. A structured computer-assisted telephone interview was conducted with the quality director of each hospital. The final sample includes 241 general acute care hospitals (response rate, 83%). Results: Approximately one third (32.4%) of California’s hospitals use AST for monitoring HAI. Adoption of AST is statistically significant and positively associated with the depth of implementation of evidence-based practices for methicillin-resistant Staphylococcus aureus and ventilator-associated pneumonia and adoption of contact precautions and surgical care infection practices. Use of AST is also statistically significantly associated with the breadth of hospital implementation of evidence-based practices across all 5 targeted HAI. Conclusion: Our findings suggest that hospitals using ASTcan achieve greater depth and breadth in implementing evidenced-based infection control practices. Key Words: Hospital-acquired infections; hospitals; infection control; automated technology; surveillance. Copyright ª 2011 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved. (Am J Infect Control 2011;39:270-6.)
Accurate and timely infection surveillance is critical in the control and prevention of hospital-acquired infections (HAI). Within the context of HAI, surveillance is defined as the ongoing and systematic collection, analysis, interpretation, and dissemination of data that are essential for prevention and control of HAIs. Such data are important in estimating the scope, spread, and location of an infection in a hospital, monitoring change over time, evaluating and improving prevention and control measures, and evaluating and improving hospital policy and practices and for facility planning and public reporting.1,2 From the School of Public Health, University of California, Berkeley, CAa; and Stanford University Medical School, Stanford University, Palo Alto, CA.b Address correspondence to Helen Halpin, ScM, PhD, Professor of Health Policy, Director, Center for Health and Public Policy Studies, University of California, Berkeley, Berkeley, CA 94720-7360. E-mail: [email protected].
Conflicts of interest: None to report. 0196-6553/$36.00 Copyright ª 2011 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.ajic.2010.10.037
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However, until recently, hospital infection surveillance methods have relied almost exclusively on the manual review of laboratory data and patient records, which are time-consuming, costly, and subject to human error.3 In addition, manual surveillance limits the amount of time infection control staff have for educational and quality improvement efforts. Whereas some hospitals began experimenting with computerization of hospital microbiology data to produce epidemiologic reports as early as the 1970s, it was not until the 1990s that commercial vendors began to market and sell automated surveillance technology (AST) to hospitals for infection control.4 There are presently several leading vendors offering AST products available on the market, including the following: CareFusion’s MedMined, AICE software, Hospira’s Theradoc, Premier’s SafetySurveillor—Infection Control, MEDITECH’s HealthCare Information System, and MIDAS1 Care Management—Infection Control. In addition, many hospitals have adopted stand-alone automated systems developed by private consultants or have developed their own in-house systems. Automated surveillance of HAI requires the linking of several databases and development of analytic software to detect and track infection trends in real time.
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These systems may use different databases but they generally include 1 or more of the following5: Laboratory information systems (for microbiology culture data and identification of organisms); Admissions-transfer-discharge databases (primarily for patient demographics, location, and diagnosis codes); Pharmacy databases (ordering and administration of antimicrobial agents); and Electronic medical records (notes from history taking and physical examination and other clinician entries in the record). The evidence on the effectiveness and efficiency of using AST for identifying HAI is strong, with high levels of sensitivity and specificity, particularly for systems that combine pharmacy and admissions-transferdischarge data (sensitivity, 59%-96%; specificity, 95%99%).6 A review of the evidence from research suggests that AST increases access to timely and important information, saving time spent on surveillance activities by infection control staff, reducing errors, and enhancing the hospital’s surveillance capacity.7,8 The AST systems that have been found to be the most effective for hospitals combine laboratory, discharge, and pharmacy databases.3 AST can produce alerts for sentinel events or organisms and produce routine and ad hoc data reports to support hospital infection control. The purpose of this research is to identify the characteristics of hospitals in California that have adopted AST and to analyze the relationship between use of AST and the depth of implementation of evidencebased infection prevention and control practices. It is our hypothesis that hospitals using AST will have made more progress in the implementation of their infection prevention and control programs than hospitals that still rely only on manual surveillance.
METHODS Baseline study of California’s acute care hospitals To test our hypothesis, we conducted a baseline study of all general, acute care hospitals in California, funded by the Blue Shield of California Foundation, from October 2008 through January 19, 2009, just prior to the implementation of the new state mandatory reporting requirements for HAI. The baseline study collected information on patient safety policies and procedures to reduce HAI and on relevant process and outcome measures at general, acute care hospitals licensed by the state. A structured, computer-assisted telephone interview was conducted
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with the quality director of each hospital (or their designate) to gain a better understanding of how quality and patient safety processes are being implemented to prevent HAI. Each interview was scheduled in advance and completed in 45 minutes on average.
Survey sample In October 2008, a list of all hospitals licensed to operate in California was obtained from the California Office of Statewide Health Planning and Development. Those hospitals that were identified as general, acute care hospitals with an average length of stay of less than 30 days and with 20 or more beds were deemed eligible to be in the sample. Hospitals that are specialty hospitals (eg, psychiatric, pediatric, surgery only, and others) were not included. The final universe was 320 general, acute care hospitals. Multiple attempts were made to schedule an interview with the quality director of each hospital. However, 13 hospitals were determined not to be eligible because the facility had closed (2), their phone number was a duplicate in the sample (4), their number was not in service (3), or there was no one on staff designated as having responsibility for quality (4). Of the total eligible universe of 303 hospitals, 241 hospitals completed the survey for a final response rate 79.5%.
Questionnaire A computer-assisted telephone interview survey instrument was developed in consultation with the California Hospital Association and with the full endorsement of the California Department of Public Health, the California Institute for Health Systems Performance, the Integrated Healthcare Association, the Pacific Business Group on Health, and the UC Berkeley and UCLA Schools of Public Health, with funding provided through a grant from the Blue Shield of California Foundation. The questionnaire included items addressing whether it had adopted AST (0,1) where 1 5 use AST and 0 5 do not use AST; the extent to which each hospital had adopted formal, written evidencebased practices for infection control; and the extent to which they were applying the practices and monitoring adherence to the practice. The hospitals were asked about infection control practices for specific organisms and infection portals such as methicillinresistant Staphylococcus aureus (MRSA), Chlostridium difficile, catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and central line-associated bloodstream infections (CLABSI) as well as several broadly applicable infection control processes such as appropriate hand hygiene, use of contact precautions, and compliance with the surgical care improvement project (SCIP).
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Implementation scales For each of the 5 HAI and each of the 3 broadly applicable infection control processes, a 0 to 3 point global implementation scale was created to assess depth of implementation based on the hospitals’ answers to the following 3 questions: Does your hospital have formal written evidencebased practices to prevent HAI for (X)? Has your hospital implemented these written evidencebased practices for (X) to prevent HAI? Does your hospital assess compliance with these written evidence-based practices for (X) to prevent HAI? To allow analysis of breadth, as well as depth, of implementation of the practices, we also created three 0 to 5 point global implementation scales to assess the breadth of implementation across the 5 targeted HAI, with each scale measuring 1 of the following: the hospital has formally written, evidence-based practices for each HAI; the hospital is implementing these practices for each HAI; and the hospital is assessing its compliance with evidence-based practices across the 5 HAIs.
Statistical analysis We first generated descriptive statistics on hospital adoption of AST and developed the global scales to measure both the depth and breadth of the implementation. We then conducted a multivariate analysis of the hospital characteristics that were hypothesized to be associated with adoption of AST. We then conducted bivariate analysis and ran multivariate models to assess the relationship between implementation of HAI prevention and control practices, as measured by our global implementation scales and use of AST. A separate model was estimated for each of the 5 HAI-specific implementation scales (MRSA, C difficile, CLABSI, CAUTI, and VAP) and for the 3 process scales (hand hygiene, contact precautions, and SCIP), as well as the 3 scales measuring breadth of implementation across the 5 targeted HAI. Finally, we examined whether the relationship between use of ASTand implementation of evidence-based guidelines for infection control varied for hospitals using custom-built systems compared with those using commercial systems.
RESULTS Findings We found that approximately one third (32.4%) of surveyed hospitals have adopted AST for HAI. Of the hospitals that indicate they are using AST for monitoring HAI, the use of specific systems was reported as follows
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(n 5 78): 23, a custom system developed at the hospital; 20, AICE; 14, CareFusion’s MedMined; 7, MIDAS1 Care Management—Infection Control; 6, MEDITECH— HealthCare Information System; 4, Premier’s SafetySurveillor—Infection Control; 3, another commercial system (not specified); and 1, Hospira’s Theradoc. We also asked respondent hospitals not currently using AST whether they planned to acquire AST in the next year, and an additional 58 hospitals (24.1%) answered affirmatively. In the multivariate model we ran identifying the characteristics of surveyed hospitals that are associated with adoption of AST (data not shown), we found that the only statistically significant variables were the number of patient safety collaboratives in which a hospital had participated (odds ratio, 1.36; 95% confidence interval: 1.06-1.76) and hospital teaching status (odds ratio, 1.82; 95% confidence interval: 1.004-3.28), where both factors were positively associated with adoption of AST. The other factors examined, which were found to have no statistically significant association with adoption of AST, include ownership (for-profit, non-profit, government), location (urban/rural/suburban), Disproportionate Share Hospital (DSH), number of beds, total margin, funding for infection control staff training, and organizational resources for patient safety. Table 1 shows the depth and breadth of implementation of formal, written evidence-based practices for each of the 5 HAI and 3 infection control processes. The HAI with the greatest degree of implementation of infection control practices are CLABSI and VAP, with mean implementation scales statistically significantly higher than the other HAI. The HAI with the lowest degree of implementation of infection control practices are CAUTI and C difficile, with mean implementation scales statistically significantly lower than the other HAI. The infection control process with the greatest degree of implementation is hand hygiene, with a mean implementation scale statistically significantly higher than contact precautions or SCIP. Whereas fewer than half of California’s hospitals have adopted, are implementing, or are assessing compliance with formal, written evidence-based practices for all 5 HAI, more than 88% of hospitals are implementing these practices for at least 3 of the 5. In examining the means for the implementation scales, the extent to which hospitals are assessing compliance with evidence-based practices is statistically significantly lower than the adoption and implementation of them. The adoption and implementation of the 3 broad infection control practice scales is much greater than that observed for the 5 organism and device-specific HAI implementation scales, with more than three fourths of the hospitals reporting adoption, implementation, and assessing compliance with evidence-based practices
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Table 1. Global implementation scales measuring adoption of evidence-based practices to prevention HAI, California Hospitals, 2008
Implementation scale by HAI (0-3) MRSA Chlostridium difficile CLABSI VAP CAUTI Implementation scale by infection control practice (0-3) SCIP Contact precautions Hand hygiene Implementation scale across 5 HAI (0-5) Adopted written, evidence-based practices Implementing practices Assessing compliance Implementation scales across infection control processes (0-3) Adopted written, evidence-based practices Implementing practices Assessing compliance with practices
Mean
95% CI
2.23 1.89 2.83 2.74 1.80
2.07-2.39 1.72-2.06 2.67-2.99 2.64-2.84 1.63-1.97
2.68 2.76 2.91
2.57-2.79 2.68-2.84 2.85-2.97
3.98 3.86 3.64
3.83-4.13 3.70-4.02 3.47-3.81
2.85 2.82 2.68
2.79-2.92 2.76-2.88 2.60-2.76
AST, automated surveillance technology; CAUTI, catheter-associated urinary tract infections; CI, confidence interval; CLABSI, central line-associated bloodstream infections; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus; SCIP, surgical care improvement project; VAP, ventilator-associated pneumonia.
Table 2. Bivariate relationships between adoption of AST and monitoring compliance with evidence-based infection control practices Monitor compliance with evidence-based practices HAI MRSA VAP CLABSI CAUTI Chlostridium difficile Infection control practices Contact precautions Hand hygiene SCIP
Use automated surveillance technology, %
Does not use AST, %
x2 Test (P value)
85 96 97 59 67
66 88 92 50 53
9.32 (.002) 4.10 (.04) 2084 (.09) 1.81 (.18) 3.70 (.05)
91 100 91
82 95 82
3.62 (.06) 4.11 (.04) 3.62 (.06)
AST, automated surveillance technology; CAUTI, catheter-associated urinary tract infections; CLABSI, central line-associated bloodstream infections; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus; SCIP, surgical care improvement project; VAP, ventilator-associated pneumonia.
for hand hygiene, contact precautions, and SCIP. In examining the means for the practice implementation scales, the extent to which evidence-based practices have been adopted is statistically significantly higher than their implementation and assessing compliance with them. In addition, the extent to which hospitals are assessing compliance with evidence-based practices is statistically significantly lower than their adoption and implementation. Table 2 presents the results of the bivariate analyses of the proportion of surveyed hospitals that are assessing compliance with evidence-based practices for the 5 HAI and 3 broad infection control processes as a function of their use of AST. A higher proportion of surveyed hospitals that use AST report that they are assessing compliance with evidence-based practices for all of the measures, and the differences were statistically
significant (P , .05) for MRSA, VAP, and hand hygiene and fell just short of statistical significance for C difficile (P 5 .05) and contact precautions and SCIP (P 5 .06). Table 3 presents the results from the multivariate models assessing the relationship between AST adoption and the implementation scales for HAI and infection control processes, controlling for number of patient safety collaboratives and teaching status. We find that adoption of AST is statistically significant and positively associated with the global measures of implementation of evidence-based practices for MRSA, VAP, SCIP, and contact precautions. In addition, use of AST is statistically significant and positively associated the breadth of implementation across all 5 HAI, measured in terms of adoption of formally written, evidence-based practices, implementing these practices, and assessing compliance with them.
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Table 3. Multivariate results of the relationship between ATS adoption and hospital implementation of HAI prevention and control practices, California, 2008* Global implementation scales Implementation of evidence-based practices by HAI MRSA implementation scale Chlostridium difficile implementation scale CLABSI implementation scale CAUTI implementation scale VAP implementation scale Implementation across 5 HAI Adoption of formal, written evidence-based practices Implementing practices Assessing compliance with practices Implementation of infection control practices SCIP scale Contact precaution scale Hand hygiene scale
b Coefficient
P value
.170 .120 .043 .085 .133
.01 .07 .51 .21 .04
.170 .158 .176
.01 .02 .01
.134 .145 .095
.04 .03 .16
AST, automated surveillance technology; CAUTI, catheter-associated urinary tract infections; CLABSI, central line-associated bloodstream infections; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus; SCIP, surgical care improvement project; VAP, ventilator-associated pneumonia. *Controlling for the number of patient safety collaboratives in which each hospital participated and the teaching status of the hospital.
Table 4. Differences in implementation of evidence-based practices in hospitals using commercial versus custom automated electronic surveillance systems, 2008 Custom AST system (n 5 23)
Commercial AST system (n 5 55)
Mean
Mean
P value
2.83 3.00 4.48
2.46 2.82 4.02
.071 .058 .049
Implementation scales Implementation of evidence-based guidelines for MRSA (0-3) Surgical care improvement project implementation (0-3) Implementation of evidence-based practices across 5 HAI (0-5)
AST, automated surveillance technology; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus.
Table 4 presents the results from our analysis of differences in implementation as a function of using a custom-built AST system compared with a commercial system. We had limited power to detect statistically significant differences, and present results that are significant at the P , .10 level. We found that, in nearly all cases, the implementation scales for hospitals using custom-built systems were higher than hospitals using commercial systems and were statistically significant at the P , .05 level for implementation of surgical care improvement practices and at the P , .1 level for implementation of evidence-based guidelines for MRSA and across all 5 HAI.
Limitations The most important limitation of this research is that it was conducted using cross-sectional data that measure statistical association, not causality. Although every effort was made to control for potential confounding in the analysis, we cannot completely rule out the possibility that hospitals that choose to adopt AST are already further along in their implementation
of infection control practices. However, our finding that funding for infection control staff training, organizational resources for patient safety, bed size, and hospital margin are not associated with acquisition of AST suggests that this is probably not the case. In addition, we cannot rule out that there may be other hospital characteristics that were not measured in our study that explain both adoption of AST and implementation of HAI practices. In addition, our implementation scales are global measures indicating whether each surveyed hospital (1) has adopted written, evidence-based HAI-specific prevention practices; (2) is implementing these practices; and (3) is assessing their hospital’s compliance with these practices. These measures are not detailed accounts of the specific steps and processes outlined in each of the practice guidelines. However, a recent study published by Stulberg et al on the relationship between compliance with SCIP measures and surgical site infections found that adherence measured through a global all-or-none composite infection prevention score was associated with a lower probability of developing a postoperative infection, whereas individual
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SCIP measures were not.9 However, we do not know whether the global implementation measures of infection control in our study are associated with reductions in infection rates over time as the Stulberg et al study9 would suggest. Finally, the experiences of hospitals in California may not be representative of general, acute care hospitals in the rest of the country. We do not know whether hospitals in other states have adopted AST at the same rate as California hospitals, nor do we know whether the characteristics of hospitals that adopt AST are the same as those we identified in California. Thus, we cannot generalize our findings to other states.
DISCUSSION Our research finds a strong and statistically significant association between adoption of AST by California’s general acute care hospitals and global measures of hospital implementation (depth and breadth) of selected evidence-based practices for the prevention of HAI. This finding supports our initial hypothesis that hospitals that use AST are doing a better job implementing evidence-based practices to prevent and control HAI compared with hospitals that still rely on manual surveillance. In particular, we found that implementation of evidence-based practices was greatest for the 2 infections that have defined ‘‘bundles’’ to guide prevention, compared with CAUTI, C difficile, and MRSA, which do not at this time. We were also interested to learn that, in general, hospitals that had developed and implemented their own custom-built AST systems had made greater progress in implementing evidence-based practices compared with hospitals that had purchased commercial systems, although small sample sizes limited our ability to draw firm conclusions. Additional research is needed to determine whether these findings are achieved through less time spent on manual data collection and clerical work and more time spent on training and education of front-line staff, monitoring performance, and making changes in care practices.10 Additional research is also needed to better understand whether use of AST is predictive of future performance in reducing HAI and the exact mechanism by which this occurs. Most states (33) have enacted laws mandating that hospitals report infection rate data on specific HAI.11 In addition, several states have adopted policies that make available or provide incentives for hospitals to adopt AST. For example, New Jersey’s law requires the state to examine ways to incentivize hospitals to increase the adoption of automated surveillance systems to support the quality and quantity of data necessary to develop a strong HAI prevention program.
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Pennsylvania’s law goes much further.12 Governor Rendell originally sought to mandate computerized infection tracking systems for all hospitals statewide, but the hospitals fought back, arguing that the cost would hurt small hospitals. Under a compromise, the law allows hospitals to opt out of the requirement if they can demonstrate they lack the resources or technological capability. Approximately 20% of the state’s hospitals have indicated they will not install computerized systems, primarily because of budgetary constraints.13 In addition, California state law requires CDPH to investigate the use of automated infection reporting databases in hospitals, with a report of the findings to the legislature. Thus, our research is expected to be of immediate use to state policy makers. The Association for Professionals in Infection Control and Epidemiology supports the use of automated surveillance technologies as an essential part of infection prevention and control activities.14,15 Although there are many benefits to using AST, there are also many challenges, including the differences in the nosocomial infection markers identified by AST systems and the standardized infection rates required for reporting, the need to validate the data, and the need to have systems in place to act upon the data in a timely and effective manner, and the initial investment of time and resources to get systems up and running.16 In conclusion, our results suggest that hospitals that use AST may offer advantages over those that do not by having adopted more effective practices to prevent HAI. This potential advantage may also become important in California as the state begins public disclosure of HAI rates in January 2011. Hospitals may even want to use their adoption of AST as a marketing tool to consumers and physicians, demonstrating their commitment to continuously monitor and improve infection control hospital wide, potentially providing them with a competitive advantage in the marketplace. These advantages are also significant in an era where Medicare will no longer pay for the additional costs attributable to specific HAI and where more and more states are requiring hospitals to report specific HAI rates to the public.17,18 In 2007, the Centers for Medicare and Medicaid Services identified 2 HAI for nonpayment by Medicare, and 2 more were added in 2008. As of October 1, 2008, hospitals no longer receive Medicare payment for incremental medical services used to treat these infections if they were not present on admission.19 Although only a minority of hospitals presently use AST, changes in state mandatory reporting regulations and the Centers for Medicare and Medicaid Services reimbursement policy may act as additional incentives for more widespread adoption of AST in US hospitals.
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References 1. Koo D. Public Health Surveillance Overview. Centers for Disease Control and Prevention. Available from: www.cdc.gov/ncphi/disss/ nndss/phs/overview.htm. Accessed January 8, 2010. 2. Burke J. Surveillance, reporting, automation and interventional epidemiology. Infect Control Hosp Epidemiol 2003;24:10-2. 3. Klompas M, Yokoe DS. Automated surveillance of health care associate infections. Clin Infect Dis 2006;48:1268-75. 4. Wright M. Automated surveillance and infection control: toward a better tomorrow. Am J Infect Control 2008;36(Suppl 1):S1-6. 5. Hebden JN, Wright M, Fuss EP, Standiford HC. Leveraging surveillance technology to benefit the practice and profession of infection control. Am J Infect Control 2008;36(Suppl 1):S7-S11. 6. Leal J, Laupland K. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69:220-9. 7. Porkorny L, Rovira A, Martin-Baranera M, Gimeno C, Alonso-Tarres C, Vilarasau J. Automated detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol 2007;28:1030-5. 8. Chalfine A. Highly sensitive and efficient computer-assisted system for routine surveillance of surgical site infections. Infect Control Hosp Epidemiol 2006;27:794-801. 9. Stulberg JJ, Delaney CP, Neuhauser DV, Aron DC, Fu P, Koroukian SM. Adherence to surgical care improvement project measures and the association with postoperative infections. JAMA 2010;303:2479-85. 10. Grota PG, Stone PW, Jordan S, Pogorzelska M, Larson E. Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction. Am J Infect Control 2010;38:509-14.
American Journal of Infection Control May 2011 11. Halpin HA, Milstein A, Shortell SM, Vannemen M, Rosenberg J. Mandatory public reporting of hospital acquired infection rates: a report from California. Health Aff 2010;30:723-9. 12. Pennsylvania. The Health-Care Associated Infection Prevention Act of 2007: a preliminary guide to Act 52. The Hospital & Healthsystem Association of Pennsylvania. 13. Associated Press. Nabbing infections with technology: sweeping Pa. law aims to reduce illnesses contracted during hospital stays. December 30, 2008. Available from: http://www.msnbc.msn.com/id/284366 86/. Accessed October 15, 2010. 14. Greene LR, Cain TA, Khoury R, Krystofiak SP, Patrick M, Streed S. APIC position paper: the importance of surveillance technologies in the prevention of health care-associated infections. Am J Infect Control 2009;37:510-3. 15. Atreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ; Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention and control. Am J Infect Control 2008;36(Suppl 3):S37-45. 16. Hebden J. Leveraging surveillance technology to benefit the practice and profession of infection control. Am J Infect Control 2008;36:S7-S11. 17. Stone PW, Glied SA, McNair PD, Matthes N, Cohen B, Landers TF, et al. CMS changes in reimbursement for HAI: setting a research agenda. Med Care 2010;48:433-9. 18. National Conference of State Legislatures. State laws relating to hospital acquired infections. Available from: http://www.ncsl.org/Issues Research/Health/HospitalacquiredInfectionsHomepage/tabid/14084/ Default.aspx. Accessed January 10, 2008. 19. Stone PW. Changes in Medicare reimbursement for hospital-acquired conditions including infections. Am J Infect Control 2009;37:A17-8.
Background: This research analyzes the relationship between hospital use of automated surveillance technology (AST) for identification and control of hospital-acquired infections (HAI) and implementation of evidence-based infection control practices. Our hypothesis is that hospitals that use AST have made more progress implementing infection control practices than hospitals that rely on manual surveillance. Methods: A survey of all acute general care hospitals in California was conducted from October 2008 through January 2009. A structured computer-assisted telephone interview was conducted with the quality director of each hospital. The final sample includes 241 general acute care hospitals (response rate, 83%). Results: Approximately one third (32.4%) of California’s hospitals use AST for monitoring HAI. Adoption of AST is statistically significant and positively associated with the depth of implementation of evidence-based practices for methicillin-resistant Staphylococcus aureus and ventilator-associated pneumonia and adoption of contact precautions and surgical care infection practices. Use of AST is also statistically significantly associated with the breadth of hospital implementation of evidence-based practices across all 5 targeted HAI. Conclusion: Our findings suggest that hospitals using ASTcan achieve greater depth and breadth in implementing evidenced-based infection control practices. Key Words: Hospital-acquired infections; hospitals; infection control; automated technology; surveillance. Copyright ª 2011 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved. (Am J Infect Control 2011;39:270-6.)
Accurate and timely infection surveillance is critical in the control and prevention of hospital-acquired infections (HAI). Within the context of HAI, surveillance is defined as the ongoing and systematic collection, analysis, interpretation, and dissemination of data that are essential for prevention and control of HAIs. Such data are important in estimating the scope, spread, and location of an infection in a hospital, monitoring change over time, evaluating and improving prevention and control measures, and evaluating and improving hospital policy and practices and for facility planning and public reporting.1,2 From the School of Public Health, University of California, Berkeley, CAa; and Stanford University Medical School, Stanford University, Palo Alto, CA.b Address correspondence to Helen Halpin, ScM, PhD, Professor of Health Policy, Director, Center for Health and Public Policy Studies, University of California, Berkeley, Berkeley, CA 94720-7360. E-mail: [email protected].
Conflicts of interest: None to report. 0196-6553/$36.00 Copyright ª 2011 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.ajic.2010.10.037
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However, until recently, hospital infection surveillance methods have relied almost exclusively on the manual review of laboratory data and patient records, which are time-consuming, costly, and subject to human error.3 In addition, manual surveillance limits the amount of time infection control staff have for educational and quality improvement efforts. Whereas some hospitals began experimenting with computerization of hospital microbiology data to produce epidemiologic reports as early as the 1970s, it was not until the 1990s that commercial vendors began to market and sell automated surveillance technology (AST) to hospitals for infection control.4 There are presently several leading vendors offering AST products available on the market, including the following: CareFusion’s MedMined, AICE software, Hospira’s Theradoc, Premier’s SafetySurveillor—Infection Control, MEDITECH’s HealthCare Information System, and MIDAS1 Care Management—Infection Control. In addition, many hospitals have adopted stand-alone automated systems developed by private consultants or have developed their own in-house systems. Automated surveillance of HAI requires the linking of several databases and development of analytic software to detect and track infection trends in real time.
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These systems may use different databases but they generally include 1 or more of the following5: Laboratory information systems (for microbiology culture data and identification of organisms); Admissions-transfer-discharge databases (primarily for patient demographics, location, and diagnosis codes); Pharmacy databases (ordering and administration of antimicrobial agents); and Electronic medical records (notes from history taking and physical examination and other clinician entries in the record). The evidence on the effectiveness and efficiency of using AST for identifying HAI is strong, with high levels of sensitivity and specificity, particularly for systems that combine pharmacy and admissions-transferdischarge data (sensitivity, 59%-96%; specificity, 95%99%).6 A review of the evidence from research suggests that AST increases access to timely and important information, saving time spent on surveillance activities by infection control staff, reducing errors, and enhancing the hospital’s surveillance capacity.7,8 The AST systems that have been found to be the most effective for hospitals combine laboratory, discharge, and pharmacy databases.3 AST can produce alerts for sentinel events or organisms and produce routine and ad hoc data reports to support hospital infection control. The purpose of this research is to identify the characteristics of hospitals in California that have adopted AST and to analyze the relationship between use of AST and the depth of implementation of evidencebased infection prevention and control practices. It is our hypothesis that hospitals using AST will have made more progress in the implementation of their infection prevention and control programs than hospitals that still rely only on manual surveillance.
METHODS Baseline study of California’s acute care hospitals To test our hypothesis, we conducted a baseline study of all general, acute care hospitals in California, funded by the Blue Shield of California Foundation, from October 2008 through January 19, 2009, just prior to the implementation of the new state mandatory reporting requirements for HAI. The baseline study collected information on patient safety policies and procedures to reduce HAI and on relevant process and outcome measures at general, acute care hospitals licensed by the state. A structured, computer-assisted telephone interview was conducted
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with the quality director of each hospital (or their designate) to gain a better understanding of how quality and patient safety processes are being implemented to prevent HAI. Each interview was scheduled in advance and completed in 45 minutes on average.
Survey sample In October 2008, a list of all hospitals licensed to operate in California was obtained from the California Office of Statewide Health Planning and Development. Those hospitals that were identified as general, acute care hospitals with an average length of stay of less than 30 days and with 20 or more beds were deemed eligible to be in the sample. Hospitals that are specialty hospitals (eg, psychiatric, pediatric, surgery only, and others) were not included. The final universe was 320 general, acute care hospitals. Multiple attempts were made to schedule an interview with the quality director of each hospital. However, 13 hospitals were determined not to be eligible because the facility had closed (2), their phone number was a duplicate in the sample (4), their number was not in service (3), or there was no one on staff designated as having responsibility for quality (4). Of the total eligible universe of 303 hospitals, 241 hospitals completed the survey for a final response rate 79.5%.
Questionnaire A computer-assisted telephone interview survey instrument was developed in consultation with the California Hospital Association and with the full endorsement of the California Department of Public Health, the California Institute for Health Systems Performance, the Integrated Healthcare Association, the Pacific Business Group on Health, and the UC Berkeley and UCLA Schools of Public Health, with funding provided through a grant from the Blue Shield of California Foundation. The questionnaire included items addressing whether it had adopted AST (0,1) where 1 5 use AST and 0 5 do not use AST; the extent to which each hospital had adopted formal, written evidencebased practices for infection control; and the extent to which they were applying the practices and monitoring adherence to the practice. The hospitals were asked about infection control practices for specific organisms and infection portals such as methicillinresistant Staphylococcus aureus (MRSA), Chlostridium difficile, catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and central line-associated bloodstream infections (CLABSI) as well as several broadly applicable infection control processes such as appropriate hand hygiene, use of contact precautions, and compliance with the surgical care improvement project (SCIP).
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Implementation scales For each of the 5 HAI and each of the 3 broadly applicable infection control processes, a 0 to 3 point global implementation scale was created to assess depth of implementation based on the hospitals’ answers to the following 3 questions: Does your hospital have formal written evidencebased practices to prevent HAI for (X)? Has your hospital implemented these written evidencebased practices for (X) to prevent HAI? Does your hospital assess compliance with these written evidence-based practices for (X) to prevent HAI? To allow analysis of breadth, as well as depth, of implementation of the practices, we also created three 0 to 5 point global implementation scales to assess the breadth of implementation across the 5 targeted HAI, with each scale measuring 1 of the following: the hospital has formally written, evidence-based practices for each HAI; the hospital is implementing these practices for each HAI; and the hospital is assessing its compliance with evidence-based practices across the 5 HAIs.
Statistical analysis We first generated descriptive statistics on hospital adoption of AST and developed the global scales to measure both the depth and breadth of the implementation. We then conducted a multivariate analysis of the hospital characteristics that were hypothesized to be associated with adoption of AST. We then conducted bivariate analysis and ran multivariate models to assess the relationship between implementation of HAI prevention and control practices, as measured by our global implementation scales and use of AST. A separate model was estimated for each of the 5 HAI-specific implementation scales (MRSA, C difficile, CLABSI, CAUTI, and VAP) and for the 3 process scales (hand hygiene, contact precautions, and SCIP), as well as the 3 scales measuring breadth of implementation across the 5 targeted HAI. Finally, we examined whether the relationship between use of ASTand implementation of evidence-based guidelines for infection control varied for hospitals using custom-built systems compared with those using commercial systems.
RESULTS Findings We found that approximately one third (32.4%) of surveyed hospitals have adopted AST for HAI. Of the hospitals that indicate they are using AST for monitoring HAI, the use of specific systems was reported as follows
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(n 5 78): 23, a custom system developed at the hospital; 20, AICE; 14, CareFusion’s MedMined; 7, MIDAS1 Care Management—Infection Control; 6, MEDITECH— HealthCare Information System; 4, Premier’s SafetySurveillor—Infection Control; 3, another commercial system (not specified); and 1, Hospira’s Theradoc. We also asked respondent hospitals not currently using AST whether they planned to acquire AST in the next year, and an additional 58 hospitals (24.1%) answered affirmatively. In the multivariate model we ran identifying the characteristics of surveyed hospitals that are associated with adoption of AST (data not shown), we found that the only statistically significant variables were the number of patient safety collaboratives in which a hospital had participated (odds ratio, 1.36; 95% confidence interval: 1.06-1.76) and hospital teaching status (odds ratio, 1.82; 95% confidence interval: 1.004-3.28), where both factors were positively associated with adoption of AST. The other factors examined, which were found to have no statistically significant association with adoption of AST, include ownership (for-profit, non-profit, government), location (urban/rural/suburban), Disproportionate Share Hospital (DSH), number of beds, total margin, funding for infection control staff training, and organizational resources for patient safety. Table 1 shows the depth and breadth of implementation of formal, written evidence-based practices for each of the 5 HAI and 3 infection control processes. The HAI with the greatest degree of implementation of infection control practices are CLABSI and VAP, with mean implementation scales statistically significantly higher than the other HAI. The HAI with the lowest degree of implementation of infection control practices are CAUTI and C difficile, with mean implementation scales statistically significantly lower than the other HAI. The infection control process with the greatest degree of implementation is hand hygiene, with a mean implementation scale statistically significantly higher than contact precautions or SCIP. Whereas fewer than half of California’s hospitals have adopted, are implementing, or are assessing compliance with formal, written evidence-based practices for all 5 HAI, more than 88% of hospitals are implementing these practices for at least 3 of the 5. In examining the means for the implementation scales, the extent to which hospitals are assessing compliance with evidence-based practices is statistically significantly lower than the adoption and implementation of them. The adoption and implementation of the 3 broad infection control practice scales is much greater than that observed for the 5 organism and device-specific HAI implementation scales, with more than three fourths of the hospitals reporting adoption, implementation, and assessing compliance with evidence-based practices
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Table 1. Global implementation scales measuring adoption of evidence-based practices to prevention HAI, California Hospitals, 2008
Implementation scale by HAI (0-3) MRSA Chlostridium difficile CLABSI VAP CAUTI Implementation scale by infection control practice (0-3) SCIP Contact precautions Hand hygiene Implementation scale across 5 HAI (0-5) Adopted written, evidence-based practices Implementing practices Assessing compliance Implementation scales across infection control processes (0-3) Adopted written, evidence-based practices Implementing practices Assessing compliance with practices
Mean
95% CI
2.23 1.89 2.83 2.74 1.80
2.07-2.39 1.72-2.06 2.67-2.99 2.64-2.84 1.63-1.97
2.68 2.76 2.91
2.57-2.79 2.68-2.84 2.85-2.97
3.98 3.86 3.64
3.83-4.13 3.70-4.02 3.47-3.81
2.85 2.82 2.68
2.79-2.92 2.76-2.88 2.60-2.76
AST, automated surveillance technology; CAUTI, catheter-associated urinary tract infections; CI, confidence interval; CLABSI, central line-associated bloodstream infections; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus; SCIP, surgical care improvement project; VAP, ventilator-associated pneumonia.
Table 2. Bivariate relationships between adoption of AST and monitoring compliance with evidence-based infection control practices Monitor compliance with evidence-based practices HAI MRSA VAP CLABSI CAUTI Chlostridium difficile Infection control practices Contact precautions Hand hygiene SCIP
Use automated surveillance technology, %
Does not use AST, %
x2 Test (P value)
85 96 97 59 67
66 88 92 50 53
9.32 (.002) 4.10 (.04) 2084 (.09) 1.81 (.18) 3.70 (.05)
91 100 91
82 95 82
3.62 (.06) 4.11 (.04) 3.62 (.06)
AST, automated surveillance technology; CAUTI, catheter-associated urinary tract infections; CLABSI, central line-associated bloodstream infections; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus; SCIP, surgical care improvement project; VAP, ventilator-associated pneumonia.
for hand hygiene, contact precautions, and SCIP. In examining the means for the practice implementation scales, the extent to which evidence-based practices have been adopted is statistically significantly higher than their implementation and assessing compliance with them. In addition, the extent to which hospitals are assessing compliance with evidence-based practices is statistically significantly lower than their adoption and implementation. Table 2 presents the results of the bivariate analyses of the proportion of surveyed hospitals that are assessing compliance with evidence-based practices for the 5 HAI and 3 broad infection control processes as a function of their use of AST. A higher proportion of surveyed hospitals that use AST report that they are assessing compliance with evidence-based practices for all of the measures, and the differences were statistically
significant (P , .05) for MRSA, VAP, and hand hygiene and fell just short of statistical significance for C difficile (P 5 .05) and contact precautions and SCIP (P 5 .06). Table 3 presents the results from the multivariate models assessing the relationship between AST adoption and the implementation scales for HAI and infection control processes, controlling for number of patient safety collaboratives and teaching status. We find that adoption of AST is statistically significant and positively associated with the global measures of implementation of evidence-based practices for MRSA, VAP, SCIP, and contact precautions. In addition, use of AST is statistically significant and positively associated the breadth of implementation across all 5 HAI, measured in terms of adoption of formally written, evidence-based practices, implementing these practices, and assessing compliance with them.
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Table 3. Multivariate results of the relationship between ATS adoption and hospital implementation of HAI prevention and control practices, California, 2008* Global implementation scales Implementation of evidence-based practices by HAI MRSA implementation scale Chlostridium difficile implementation scale CLABSI implementation scale CAUTI implementation scale VAP implementation scale Implementation across 5 HAI Adoption of formal, written evidence-based practices Implementing practices Assessing compliance with practices Implementation of infection control practices SCIP scale Contact precaution scale Hand hygiene scale
b Coefficient
P value
.170 .120 .043 .085 .133
.01 .07 .51 .21 .04
.170 .158 .176
.01 .02 .01
.134 .145 .095
.04 .03 .16
AST, automated surveillance technology; CAUTI, catheter-associated urinary tract infections; CLABSI, central line-associated bloodstream infections; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus; SCIP, surgical care improvement project; VAP, ventilator-associated pneumonia. *Controlling for the number of patient safety collaboratives in which each hospital participated and the teaching status of the hospital.
Table 4. Differences in implementation of evidence-based practices in hospitals using commercial versus custom automated electronic surveillance systems, 2008 Custom AST system (n 5 23)
Commercial AST system (n 5 55)
Mean
Mean
P value
2.83 3.00 4.48
2.46 2.82 4.02
.071 .058 .049
Implementation scales Implementation of evidence-based guidelines for MRSA (0-3) Surgical care improvement project implementation (0-3) Implementation of evidence-based practices across 5 HAI (0-5)
AST, automated surveillance technology; HAI, hospital-acquired infections; MRSA, methicillin-resistant Staphylococcus aureus.
Table 4 presents the results from our analysis of differences in implementation as a function of using a custom-built AST system compared with a commercial system. We had limited power to detect statistically significant differences, and present results that are significant at the P , .10 level. We found that, in nearly all cases, the implementation scales for hospitals using custom-built systems were higher than hospitals using commercial systems and were statistically significant at the P , .05 level for implementation of surgical care improvement practices and at the P , .1 level for implementation of evidence-based guidelines for MRSA and across all 5 HAI.
Limitations The most important limitation of this research is that it was conducted using cross-sectional data that measure statistical association, not causality. Although every effort was made to control for potential confounding in the analysis, we cannot completely rule out the possibility that hospitals that choose to adopt AST are already further along in their implementation
of infection control practices. However, our finding that funding for infection control staff training, organizational resources for patient safety, bed size, and hospital margin are not associated with acquisition of AST suggests that this is probably not the case. In addition, we cannot rule out that there may be other hospital characteristics that were not measured in our study that explain both adoption of AST and implementation of HAI practices. In addition, our implementation scales are global measures indicating whether each surveyed hospital (1) has adopted written, evidence-based HAI-specific prevention practices; (2) is implementing these practices; and (3) is assessing their hospital’s compliance with these practices. These measures are not detailed accounts of the specific steps and processes outlined in each of the practice guidelines. However, a recent study published by Stulberg et al on the relationship between compliance with SCIP measures and surgical site infections found that adherence measured through a global all-or-none composite infection prevention score was associated with a lower probability of developing a postoperative infection, whereas individual
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SCIP measures were not.9 However, we do not know whether the global implementation measures of infection control in our study are associated with reductions in infection rates over time as the Stulberg et al study9 would suggest. Finally, the experiences of hospitals in California may not be representative of general, acute care hospitals in the rest of the country. We do not know whether hospitals in other states have adopted AST at the same rate as California hospitals, nor do we know whether the characteristics of hospitals that adopt AST are the same as those we identified in California. Thus, we cannot generalize our findings to other states.
DISCUSSION Our research finds a strong and statistically significant association between adoption of AST by California’s general acute care hospitals and global measures of hospital implementation (depth and breadth) of selected evidence-based practices for the prevention of HAI. This finding supports our initial hypothesis that hospitals that use AST are doing a better job implementing evidence-based practices to prevent and control HAI compared with hospitals that still rely on manual surveillance. In particular, we found that implementation of evidence-based practices was greatest for the 2 infections that have defined ‘‘bundles’’ to guide prevention, compared with CAUTI, C difficile, and MRSA, which do not at this time. We were also interested to learn that, in general, hospitals that had developed and implemented their own custom-built AST systems had made greater progress in implementing evidence-based practices compared with hospitals that had purchased commercial systems, although small sample sizes limited our ability to draw firm conclusions. Additional research is needed to determine whether these findings are achieved through less time spent on manual data collection and clerical work and more time spent on training and education of front-line staff, monitoring performance, and making changes in care practices.10 Additional research is also needed to better understand whether use of AST is predictive of future performance in reducing HAI and the exact mechanism by which this occurs. Most states (33) have enacted laws mandating that hospitals report infection rate data on specific HAI.11 In addition, several states have adopted policies that make available or provide incentives for hospitals to adopt AST. For example, New Jersey’s law requires the state to examine ways to incentivize hospitals to increase the adoption of automated surveillance systems to support the quality and quantity of data necessary to develop a strong HAI prevention program.
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Pennsylvania’s law goes much further.12 Governor Rendell originally sought to mandate computerized infection tracking systems for all hospitals statewide, but the hospitals fought back, arguing that the cost would hurt small hospitals. Under a compromise, the law allows hospitals to opt out of the requirement if they can demonstrate they lack the resources or technological capability. Approximately 20% of the state’s hospitals have indicated they will not install computerized systems, primarily because of budgetary constraints.13 In addition, California state law requires CDPH to investigate the use of automated infection reporting databases in hospitals, with a report of the findings to the legislature. Thus, our research is expected to be of immediate use to state policy makers. The Association for Professionals in Infection Control and Epidemiology supports the use of automated surveillance technologies as an essential part of infection prevention and control activities.14,15 Although there are many benefits to using AST, there are also many challenges, including the differences in the nosocomial infection markers identified by AST systems and the standardized infection rates required for reporting, the need to validate the data, and the need to have systems in place to act upon the data in a timely and effective manner, and the initial investment of time and resources to get systems up and running.16 In conclusion, our results suggest that hospitals that use AST may offer advantages over those that do not by having adopted more effective practices to prevent HAI. This potential advantage may also become important in California as the state begins public disclosure of HAI rates in January 2011. Hospitals may even want to use their adoption of AST as a marketing tool to consumers and physicians, demonstrating their commitment to continuously monitor and improve infection control hospital wide, potentially providing them with a competitive advantage in the marketplace. These advantages are also significant in an era where Medicare will no longer pay for the additional costs attributable to specific HAI and where more and more states are requiring hospitals to report specific HAI rates to the public.17,18 In 2007, the Centers for Medicare and Medicaid Services identified 2 HAI for nonpayment by Medicare, and 2 more were added in 2008. As of October 1, 2008, hospitals no longer receive Medicare payment for incremental medical services used to treat these infections if they were not present on admission.19 Although only a minority of hospitals presently use AST, changes in state mandatory reporting regulations and the Centers for Medicare and Medicaid Services reimbursement policy may act as additional incentives for more widespread adoption of AST in US hospitals.
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References 1. Koo D. Public Health Surveillance Overview. Centers for Disease Control and Prevention. Available from: www.cdc.gov/ncphi/disss/ nndss/phs/overview.htm. Accessed January 8, 2010. 2. Burke J. Surveillance, reporting, automation and interventional epidemiology. Infect Control Hosp Epidemiol 2003;24:10-2. 3. Klompas M, Yokoe DS. Automated surveillance of health care associate infections. Clin Infect Dis 2006;48:1268-75. 4. Wright M. Automated surveillance and infection control: toward a better tomorrow. Am J Infect Control 2008;36(Suppl 1):S1-6. 5. Hebden JN, Wright M, Fuss EP, Standiford HC. Leveraging surveillance technology to benefit the practice and profession of infection control. Am J Infect Control 2008;36(Suppl 1):S7-S11. 6. Leal J, Laupland K. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69:220-9. 7. Porkorny L, Rovira A, Martin-Baranera M, Gimeno C, Alonso-Tarres C, Vilarasau J. Automated detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol 2007;28:1030-5. 8. Chalfine A. Highly sensitive and efficient computer-assisted system for routine surveillance of surgical site infections. Infect Control Hosp Epidemiol 2006;27:794-801. 9. Stulberg JJ, Delaney CP, Neuhauser DV, Aron DC, Fu P, Koroukian SM. Adherence to surgical care improvement project measures and the association with postoperative infections. JAMA 2010;303:2479-85. 10. Grota PG, Stone PW, Jordan S, Pogorzelska M, Larson E. Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction. Am J Infect Control 2010;38:509-14.
American Journal of Infection Control May 2011 11. Halpin HA, Milstein A, Shortell SM, Vannemen M, Rosenberg J. Mandatory public reporting of hospital acquired infection rates: a report from California. Health Aff 2010;30:723-9. 12. Pennsylvania. The Health-Care Associated Infection Prevention Act of 2007: a preliminary guide to Act 52. The Hospital & Healthsystem Association of Pennsylvania. 13. Associated Press. Nabbing infections with technology: sweeping Pa. law aims to reduce illnesses contracted during hospital stays. December 30, 2008. Available from: http://www.msnbc.msn.com/id/284366 86/. Accessed October 15, 2010. 14. Greene LR, Cain TA, Khoury R, Krystofiak SP, Patrick M, Streed S. APIC position paper: the importance of surveillance technologies in the prevention of health care-associated infections. Am J Infect Control 2009;37:510-3. 15. Atreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ; Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention and control. Am J Infect Control 2008;36(Suppl 3):S37-45. 16. Hebden J. Leveraging surveillance technology to benefit the practice and profession of infection control. Am J Infect Control 2008;36:S7-S11. 17. Stone PW, Glied SA, McNair PD, Matthes N, Cohen B, Landers TF, et al. CMS changes in reimbursement for HAI: setting a research agenda. Med Care 2010;48:433-9. 18. National Conference of State Legislatures. State laws relating to hospital acquired infections. Available from: http://www.ncsl.org/Issues Research/Health/HospitalacquiredInfectionsHomepage/tabid/14084/ Default.aspx. Accessed January 10, 2008. 19. Stone PW. Changes in Medicare reimbursement for hospital-acquired conditions including infections. Am J Infect Control 2009;37:A17-8.