- Email: [email protected]
Automated surveillance and infection control: Toward a better tomorrow
Automated surveillance and infection control: Toward a better tomorrow Marc-Oliver Wright, MT(ASCP), MS Evanston, Illinois
Advances in the use of technology to collect, aggregate, and derive meaning from infection control data have increased the potential for the discipline as a whole. However, many infection control professionals have yet to adapt these tools to practice. This report provides the infection control professional with an introduction to the use of informatics for automated surveillance by defining key terms and describing their interrelationships. Several advantages and disadvantages to adapting automated surveillance are discussed, and future opportunities and challenges to the profession are offered. (Am J Infect Control 2008;36:S1-6.)
In 1951, the first nonmilitary use of a computer in the United States was by the US Census Bureau for collecting and tabulating the results of the 1950 Decennial Census.1 Less than a decade later, researchers were demonstrating the use of mathematical models and computer technology in diagnosing disease.2,3 Although not the first report, Jorgensen et al reported the computerization of a hospital microbiology laboratory in 1975, which included the ability to produce a daily epidemiologic report.4 Within the next 10 years, programs written in computer programming languages such as BASIC allowed users to discern rates above an average baseline as being excessive, leading to enhanced surveillance detection.5 In 1986, researchers at LDS Hospital in Salt Lake City, Utah, described a system capable of identifying patients with hospital-acquired infections as well as inappropriate antimicrobial therapy.6 By the 1990s, everything began to take off, and, today, the domain is awash with commercial vendors, independent consultants, stand-alone or in-house developed systems, and, to a certain extent, confusion.
TERMINOLOGY AND COMPONENTS To alleviate some of this confusion, it is essential to differentiate between several key terms. Whereas data From Evanston Northwestern Healthcare, Department of Infection Control, Evanston, IL. Address correspondence to Marc-Oliver Wright, MT(ASCP), MS, Evanston Northwestern Healthcare, Department of Infection Control, Evanston Hospital, 2650 Ridge-Burch 124, Evanston, IL 60201. E-mail: [email protected].
0196-6553/$34.00 Copyright ª 2008 by the Association for Professionals in Infection Control and Epidemiology, Inc. doi:10.1016/j.ajic.2007.09.003
is a ‘‘measurement or characteristic’’ of a single ‘‘thing that is the focus of an information system,’’ by itself, it has little or no meaning.7 Conceptually, a single cell in a spreadsheet would constitute data. A positive blood culture result of ‘‘Staphylococcus aureus’’ is an example, which in the context of outbreak detection is essentially meaningless alone. Data aggregated and stored in structured formats are databases, which can vary from several rows and columns to terabytes in size. An admission discharge transfer database would contain many pieces of data aggregated to form records related to the locations and encounters of patients admitted to a specific institution(s). Databases collate large amounts of data but are unable (by themselves) to provide meaning. To calculate an average length of stay from the admission discharge transfer system, a method of analysis must be deployed. This places the data ‘‘within the context of analysis’’ and, as such, forms information.7 The most common example of information in infection control is the rate, where data (numerators and denominators) combine to form a mathematical expression of the frequency in which an event (numerator) occurs. In many instances, information itself is also insufficient and requires a final step to achieve its worth. Although a calculated rate of infection is helpful, it often requires a comparator; hence, knowledge is achieved when one applies ‘‘information by the use of rules.’’7 Knowledge places the information in its context with the real world and helps answer questions such as, ‘‘how much is too much?’’ and ‘‘does this require action?’’ If the expressed rate of central line-associated bloodstream infections (BSI) is compared with (for example) National Healthcare Safety Network (NHSN) pooled means, and the observed exceeds the expected/acceptable value, the value judgment (eg, this rate is unacceptable) and actions applied to correct the problem (eg, S1
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implementation of the Institute for Healthcare Improvement’s central line bundle) constitutes knowledge. Although the terms data, information, and knowledge imply a distinguishable hierarchy, the exact same scenario may present as different stages in different circumstances. A clean catch outpatient urine specimen with Escherichia coli may represent pieces of data to the ICP and knowledge (actionable) to the primary care provider. Recognizing these terms in the context in which they present remains important in planning and evaluating automated surveillance opportunities. Although most infection control programs have little trouble accessing data, many find themselves overwhelmed in the efforts to translate data into information and, finally, knowledge. However, if infection control programs are to transcend from regulatory requirements to change makers, our dependence on data must diminish. Enter ‘‘the study and application of methods to improve management of patient data . relevant to patient care and community health,’’ also known as medical informatics.8 Of note, this definition removes the emphasis on technology itself. Informatics is the study, whereas technology, including automated surveillance, is the tool. In the absence of astute and committed owners (ie, ICPs), this tool remains dulled, broken, or unused. Automated surveillance (AS) is the process of obtaining useful information from infection control data through the systematic application of medical informatics and computer science technologies. It may include, but is not limited to, either of the following: 1. Data mining: An application of mathematical and statistical techniques to large collections of data for the purpose of discovering patterns and relationships that can be used to classify and predict. Data mining (DM) is arguably the most widely varied term among users of automated surveillance systems. To some, it is equivalent to all forms of automated surveillance, whether these systems actually seek patterns in the data. Pattern detection and prediction are essential components of DM, and automated graphs and reports are not equivalent. DM applications are either supervised, in which a model is developed prior to analysis, or unsupervised, in which no such model is used.9 An example of supervised learning may be a DM application that applies a regression model evaluating length of stay and risk of infection, where risk is anticipated to increase over time. An unsupervised application example would be a report indicating that a higher percentage of gram-negative organisms recovered from the blood were resistant to imipenem compared with the past 6 months. The unsupervised version is the form more commonly associated
with the ‘‘black-box’’ imagery of DM, where data goes in and answers come out. However, the utility of these results is dependent on the ability of the DM system to differentiate potentially significant issues (such as the example above) from nonsensical ones (eg, persons with names beginning with the letter L are more likely to have positive urine cultures). 2. Hypothesis-based knowledge discovery: querybased data management that requires user input for development.10 This differs from traditional DM in that someone (ICP, developer, data analyst, or others) must first ask the question of interest. It operates at the discretion and direction of the individual but does not seek out patterns independently. An example here may include an ICP who wants a daily report of the number of patients currently hospitalized with a history of methicillin-resistant Staphylococcus aureus. The ICP creates and schedules a report to be automatically generated. Had the ICP not created or asked for such a report, the system would not necessarily indicate when such a readmission occurs, only when the occurrence of readmissions exceeded a predicted threshold. The emphasis and, thus, the responsibility are on the user rather than the system. The technology facilitates easier transformation of data into information and knowledge, but it does not identify patterns independently. Commercial AS systems commonly consist of DM and/or hypothesis-based knowledge discovery (HBKD) technologies to varying degrees of sophistication. The differentiation is in the process, whether the system independently identifies potentially significant events or whether the system must be told where to look. Similarly, report-generating tools are often included in such systems, with charts or graphs created for the end user. Although these outputs may be useful, they are often template or macro based and not necessarily the product of either DM or HBKD technology.
OUTPUTS AND IMPLEMENTATION Results from AS may come in a variety of formats. The role of the ICP is to understand clearly the results, such that they are able to effectively utilize them in the next step or articulate the interpretation in a meaningful way to direct care providers. Outputs of AS may include the following: 1. Individual alerts for sentinel events/organisms. Notification of any multiresistant organism recovered from any patient at any time may be an example here, and this may come as a result of either DM or HBKD.
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2. Ad hoc data requests. An example of this may include generating a list of patients potentially exposed to an active tuberculosis patient in an outpatient facility by requesting a list of all patients seen in the same area that day. Generating line lists is another example, such as gathering a list of potential case-patients after hearing that a nearby facility has had imipenem-resistant Pseudomonas infections in their pulmonary service patients. With such requests, data understanding is essential. If in the above example, the clinical microbiology laboratory tests only meropenem rather than imipenem, the query is likely to report zero potential cases, unless the antibiotic classes are already mapped. This is commonly the product of systems using HBKD. 3. Automated cluster detection utilizing statistical processes such as control charts, analysis of variance, and others. Notification of an increase in methicillin-susceptible S aureus in an intensive care unit is an example, and, although most commonly associated with DM, advanced HBKD systems are capable of producing similar results, although in a user and developer defined context. This category would also include syndromic surveillance and similar statistical-based surveillance methodologies. The key is to understand the strengths and limitations of the statistical tests used and to maximize their effectiveness (sensitivity and specificity). 4. Fully automated infection reports. By developing, testing, and deploying a set of algorithms or decision rules, complete surveillance reports are generated such as a report detailing urinary tract infections caused by Escherichia coli per 1000 patient-days. Although theoretically HBKD based, the algorithms are rather complex and more likely generated and tested on the production side rather than user defined. This category represents one of the most sophisticated AS products to date. However, it is important to understand how the algorithms are defined, how these compare with traditional or standard definitions (sensitivity, specificity, positive and negative predictive values), and whether direct comparisons can be made to established benchmarks (eg, NHSN). Development and implementation of AS usually requires extensive time and resource allocation, although not necessarily and not entirely on the part of the ICP. Common to both commercial and independent systems is the need for effective information resource management planning, which includes understanding, and simplifying the work processes of doing infection control with the implementation of a new AS system.11 To understand the business, the ICP must define for themselves and more often others (system developers)
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exactly how they do their job and how or why the specific actions are important. By describing the normal work flows, systems developers are able to address the ICPs needs or articulate shortcomings of the technology in advance. This step often consumes the lion share of development and implementation resources, but failing to adequately invest in this area has significantly larger costs in the long-term when the system fails to meet the needs of the program.11 Understanding the business further describes and defines the needs of the end user (ICP) and acts as a catalyst for the next step. In simplifying the business of infection control, the existing infection control program is critically evaluated for redundancies, error potential, and streamlined work flows.11 These first 2 steps are critical because the emphasis is on the infection control program rather than the technology itself and allows the ICP to articulate their needs and plan ahead for the coming changes to their daily work. Following these steps, development and deployment of AS systems will require varying amounts of resources, depending on whether the system is from a commercial source (already developed and thus assumed to more readily adapt to different hospital platforms) or in-house (presumably easily deployed, but development may be extensive), the extent of validation needed, and the training/education programs for the ICPs. The process from start to finish is months if not years and remains ongoing in light of upgrades and newly identified needs.
BENEFITS TO AUTOMATION Automated surveillance technologies have been warmly welcomed in infection control for several obvious reasons: time saved in reducing manual efforts, reduced error potential, enhanced surveillance capabilities, and ease of access.12 Infection control programs are continually faced with competing priorities and reduced or insufficient resources. Expectations of expanded surveillance and reporting, whether from internal, regulatory, or public (SURVEILLANCE), demand time from practitioners, often at the expense of education, observation, and behavior modification (PREVENTION). To reverse this trend and hopefully expand the role of the ICP as an agent of change within a health care delivery system, surveillance must absorb fewer human resources. Although most publications describing AS systems are retrospective or a before-after design, virtually all report a time-savings benefit.6,13-22 As seen in Table 1, several studies have attempted to quantify this benefit. Beginning in 1986, Evans et al6 reported a 65% reduction in manual surveillance efforts for ICPs at LDS Hospital, whereas, as recent as 2006, Brosette et al16
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Table 1. Sensitivity, specificity, and time benefits to AS Author
Surveillance
Sensitivity (95% CI If available)
Specificity (95% CI If available)
Brossette et al16 Evans et al6 Pokorny et al17 Bellini et al18 Trick et al19 Bouam et al20 Chalfine et al15 Haas et al21
All HAI All HAI All HAI BSIs BSIs BSI/UTI SSIs VAPs
0.65 (0.76–0.98) Positive predictive value, 0.77 0.943 (0.795–0.99) Catheter-0.78 Secondary-0.88 0.81 0.91 (0.89–0.93) 0.843 (0.66–0.94) 0.71
0.984 (0.98–0.98) N/A 0.838(0.788–0.889) Catheter-0.93 Secondary-0.69 0.72 0.91 (0.89–0.93) 0.999 0.998
Time 1.5 FTE per 10,000 admissions 65% Reduction N/A N/A 458 hours per year saved 36 mins, 21 sec per unit per week 90 vs 223 Hours (60% reduction) N/A
FTE, Full time equivalent infection control professional; HAI, health-care associated infection; BSI, blood stream infection; UTI, urinary tract infection; VAP, ventilator associated pneumonia; CI, Confidence interval; N/A, Not available.
approximated that 1.5 full-time employees for every 10,000 admissions would be required to perform effectively the equivalent surveillance. Chalfine et al15 reported a 60% reduction in efforts targeting surgical site infections, whereas Trick et al19 reduced bloodstream infection surveillance efforts by nearly 11.5 weeks per annum, and Bouam et al20 reduced efforts by 36.35 minutes per week per unit with regards to bloodstream infection and urinary tract infection surveillance. What is not quantified, although mentioned anecdotally in a number of reports, is how this newfound time is used. Evans et al describes this benefit taking the form of ‘‘audits of patient care practices, increased in-service and education, earlier and more effective isolations, and improved analysis and feedback of surveillance data.’’6 The absence of AS leaves the practitioner(s) to manually collect, review, and transform data into knowledge. A key step in the process, review, demands excellent attention to detail and the ability to recognize patterns across time and distance. Understandably, it can be an imperfect practice. However, these limitations can, and often do, lead to missed opportunities for cluster detection. Peterson and Brosette provide a clear example of this by using conservative estimates related to facility size and organisms of interest to calculate a staggering 21 billion potential combinations of interest.10 Studies have repeatedly shown AS to improve or have the potential to improve event detection above and beyond the efforts of the existing program resources.6,13-22 Evans et al6 noted that AS identified 24% more patients with health care-associated infections, and a study from the University of Maryland found that 6 of the 11 potential clusters picked up by AS were not identified when compared with traditional practitioner-based surveillance.14 These enhanced surveillance capabilities can allow infection control programs to expand their surveillance activities beyond the traditional annual targeted surveillance plan, which is currently dictated, in part, by available resources. However, there seems to be a lack of published reports indicating how, if at all,
technology has fundamentally altered the manner in which the business of infection control is done. Last, enhanced telecommunication technologies within and outside health care facilities can facilitate greater access to health care data. Security and privacy are most important, yet the technology exists to allow safe and protected access to these systems outside the office. Although not a fully automated system, Farley et al utilized personal digital assistants in conjunction with an interrelational database to facilitate urinary tract infection surveillance, with resulting time and cost benefit to their infection control program.23 Web-based AS systems, combined with expanding availability of wireless technology within hospitals, should allow practitioners to leave their desks behind and become an active member of the interdisciplinary health care delivery team by being a visible presence in the unit.
LIMITATIONS Technology is not without its limits, and AS is no exception. Paramount to these is the up-front cost investment and the need to obtain financial support. Although projected and proposed costsavings are commonly reported in the literature and marketing materials, Franklin’s adage of ‘‘a penny saved is a penny earned’’ is difficult to prove and sometimes dismissed. Cost is not exclusive to commercial entities. Wisniewski et al estimate an up-front investment of approximately 4000 hours in development alone to construct an internal clinical data warehouse for infection control.13 Investment does not end once the system is in place. Technology’s only constant is constant change, and system owners (users, developers, administration) must be prepared for such change, including from a financial standpoint.13 Money does not guarantee success. Even the most advanced systems are unable to control the most likely source of system failure: people. Failure can come from a variety of nontechnical angles including projects not meeting user requirements or expectations (often because of a lack of involving users in development), lack of planning, and ineffective communication
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between users and developers.24 Learning curves of AS systems vary as does the knowledge and comfort level of the user. In a 1994 study of 8380 information technology projects, nearly one third were assessed to be complete failures, and only 16.2% were fully successful. This resulted in a projected financial loss of $140 billion. The leading success factors included user involvement and executive support, whereas the most commonly reported failure characteristics were lack of user input and poorly defined or changing system requirements.25 The most recent report from the same group showed marked improvement. Of 13,522 projects, a full one third succeeded, whereas 15% failed according to the 2003 report.26 This further underscores the need for effective planning including early ICP involvement and clearly defined work flows during system development. In addition, there remain underlying technical issues with the potential to provoke even the most advanced AS systems. Without consistent quality source data from within the institution, systems may fall prey to the aphorism GIGO (garbage-in, garbage-out). Similarly, changes to internal data structures or sources, ranging from a complete overhaul to changing a microbiology susceptibility panel, can lead to erroneous results. Although many AS systems are able to detect these aberrations, these events demand ongoing time and resources. It is especially important that the end users not become lackadaisical with the systems themselves but maintain vigilance and not assume that technology is a substitute for the critical-thinking ICP. Last, medicine as a whole suffers from the lack of a truly universal medical vocabulary, making decision rules based in part on clinical descriptions difficult at best.27
FUTURE CHALLENGES AND OPPORTUNITIES The 2004 ‘‘Health Information Technology Plan,’’ released by the White House, challenged the industry to outfit Americans with electronic health records by 2014. Efforts to meet this mandate have been somewhat successful and have potential benefits to AS.28 Mobile and uniform health care data should facilitate increased and easier communication of preexisting conditions, procedures, previous laboratory results, and others among health care facilities.29 Efforts toward electronic health records will require further development of universal medical terminology, easing the path to greater refinement of fully automated surveillance and facilitating easier adaptation of casemix adjustments and the use of standard surveillance definitions. The technology will advance, the systems will continue to improve, and the opportunities will expand. The question remains, what is next? From a technology standpoint, AS systems may be able to expand beyond surveillance to decision
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support. A recent study addressed the issue of predicting risk for multidrug-resistant organisms on admission.30 Common elements of risk include previous hospitalization, antimicrobial use, history of colonization or infection, and residence in long-term care facilities. Most, if not all, of these elements are available in the same data sources used for AS systems. Furthermore, these models may be institution specific, and sophisticated DM models may be deployed to predict and adjust over time. If the flow of information between an institution’s health care information infrastructure and AS systems becomes bidirectional, these models could be deployed to issue standing orders for surveillance cultures of high-risk patients to the admitting physician. Similarly, orders written for contact, droplet, and airborne isolation could be sent out automatically the moment the result is entered in the clinical laboratory, reducing the delay and decreasing the potential for cross transmission. Future developments are on the horizon, and ICPs should actively participate in the process to voice their wishes for tomorrow and hopefully guide future enhancements. To most effectively utilize AS now and going forward, ICPs, individually or as a profession, should consider allowing this technology to fundamentally alter the manner in which surveillance is traditionally done. By harnessing this tool, programs may consider returning to whole house surveillance, decentralizing ICPs from the office to the inpatient unit or adopting nontraditional measures as quality indicators. In today’s world, feedback of surveillance data to health care providers has already been shown to improve performance.31 Regional infection control networks are demonstrating added benefit.32 Recently, Mah and Meyers noted that traditional didactic education methods often used by ICPs lack success and that a more social, interactive, and multimodal approach may offer better results.33 Each of these require an investment of time on the part of the practitioner. In an editorial, John Burke of LDS Hospital noted that the time saved by automation will actually lead to the need for greater (not less) human efforts aimed at managing the previously unanswerable questions.34 Perhaps the lasting legacy of surveillance technology will be its ability to ease the burden of data management from the ICP to go forth and accomplish the profession’s highest calling: prevention. References 1. Anderson MJ. The American census: a social history. New Haven: Yale University Press; 1988. p. 197. 2. Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science 1959;130:9-21. 3. Warner HR, Toronot AF, Veasey LG, Stephenson R. A mathematical approach to medical diagnosis: application to congenital heart disease. JAMA 1961;177:75-81.
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4. Jorgensen JH, Holmes P, Williams WL, Harris JL. Computerization of a hospital clinical microbiology laboratory. Am J Clin Pathol 1978;69: 605-14. 5. Schifman RB, Palmer RA. Surveillance of nosocomial infections by computer analysis of positive culture rates. J Clin Microbiol 1985;21:493-5. 6. Evans RS, Larsen RA, Burke JP, Gardner RM, Meier FA, Jacobson JA, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:1007-11. 7. Lumpkin JR. History and significance of information systems in public health. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 18. 8. Wyatt JC, Liu JLY. Basic concepts in medical informatics. J Epidemiol Community Health 2002;56:808-12. 9. Kroenke DM. Database processing. Upper Saddle River, NJ: Pearson Prentice Hall; 2006. p. 549-54. 10. Peterson LR, Brossette SE. Hunting health care-associated infections from the clinical microbiology laboratory: passive, active and virtual surveillance. J Clin Microbiol 2002;40:1-4. 11. O’Carroll PW. Information architecture. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 85-97. 12. Obenshain MK. Application of data mining techniques to healthcare data. Infect Control Hosp Epidemiol 2004;25:690-5. 13. Wisniewski MF, Kieszkowski P, Zagorski BM, Trick WE, Sommers M, Weinstein RA. Development of a clinical data warehouse for hospital infection control. J Am Med Inform Assoc 2003;10:454-62. 14. Wright MO, Perencevich EN, Novak C, Hebden JN, Standiford HC, Harris AD. Preliminary assessment of an automated surveillance system for infection control. Infect Control Hosp Epidemiol 2004;25:325-32. 15. Chalfine A, Cauet D, Lin WC, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol 2006;27:794-801. 16. Brossette SE, Hacek DM, Gavin PJ, Kamdar MA, Gadbois KD, Fisher AG, et al. A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance? Am J Clin Pathol 2006;125:34-9. 17. Pokorny L, Rovira A, Martin-Baranera M, Gimeno C, Alonso-Tarres C, Vilarasau J. Automatic detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol 2006;27:500-3. 18. Bellini C, Petignat C, Francioli P, Wenger A, Bille J, Klopotov A, et al. Comparison of automated strategies for surveillance of nosocomial bacteremia. Infect Control Hosp Epidemiol 2007;28:1030-5. 19. Trick WE, Zagorski BM, Tokars JI, Vernon MO, Welbel SF, Wisniewski MF, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10:1612-20.
Wright 20. Bouam S, Girou E, Brun-Buisson C, Karadimas H, Lepage E. An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians’ self-reports. Infect Control Hosp Epidemiol 2003;24:51-5. 21. Haas JP, Mendonca EA, Ross B, Friedman C, Larson E. Use of computerized surveillance to detect nosocomial pneumonia in neonatal intensive care unit patients. Am J Infect Control 2005;33:439-43. 22. Ma L, Tsui FC, Hogan WR, Wagner MM, Ma H. A framework for infection control surveillance using association rules. AMIA Annu Symp Proc 2003;410-4. 23. Farley JE, Srinivasan A, Richards A, Song X, McEachen J, Perl TM. Handheld computer surveillance: shoe-leather epidemiology in the ‘‘palm’’ of your hand. Am J Infect Control 2005;33:444-9. 24. Kitch P, Yasanoff WA. Managing IT personnel and projects. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 159-78. 25. The CHAOS Report: 1994. Available at: http://www.standishgroup. com/sample_research/chaos_1994_1.php. Accessed March 29, 2007. 26. Latest Standish Group CHAOS Report shows project success rates have improved by 50%. Available at: http://www. standishgroup.com/press/article.php?id52. Accessed March 29, 2007. 27. Jernigan DB, Davies J, Sim A. Data standards in public health information. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 218-22. 28. Brownback and Moore praise health information technology plan. In HIMSS News, December 6, 2007. Available at: http://www.himss.org/ASP/ ContentRedirector.asp?ContentId566342&type5HIMSSNewsItem. Accessed March 29, 2007. 29. McGrow KM, Roys R, Maloney RC, Xiao Y. Using wireless technologies to improve information flow for interhospital transfers of critical care patients. Crit Care Nurse 2004;24:66-72 114. 30. Furuno JP, McGregor JC, Harris AD, Johnson JA, Johnson JK, Langenberg P, et al. Identifying groups at high risk for carriage of antibioticresistant bacteria. Arch Intern Med 2006;166:580-5. 31. Richards C, Emori TG, Peavy G, Gaynes R. Free full text promoting quality through measurement of performance and response: prevention success stories. Emerg Infect Dis 2001;7:299-301. 32. Kaye KS, Engemann JJ, Fulmer EM, Clark CC, Noga EM, et al. Favorable impact of an infection control network on nosocomial infection rates in community hospitals. Infect Control Hosp Epidemiol 2006; 27:228-32. 33. Mah MW, Meyers G. Toward a socioethical approach to behavior change. Am J Infect Control 2006;34:73-9. 34. Burke JP. Surveillance, reporting, automation, and interventional epidemiology. Infect Control Hosp Epidemiol 2003;24:10-2.
Advances in the use of technology to collect, aggregate, and derive meaning from infection control data have increased the potential for the discipline as a whole. However, many infection control professionals have yet to adapt these tools to practice. This report provides the infection control professional with an introduction to the use of informatics for automated surveillance by defining key terms and describing their interrelationships. Several advantages and disadvantages to adapting automated surveillance are discussed, and future opportunities and challenges to the profession are offered. (Am J Infect Control 2008;36:S1-6.)
In 1951, the first nonmilitary use of a computer in the United States was by the US Census Bureau for collecting and tabulating the results of the 1950 Decennial Census.1 Less than a decade later, researchers were demonstrating the use of mathematical models and computer technology in diagnosing disease.2,3 Although not the first report, Jorgensen et al reported the computerization of a hospital microbiology laboratory in 1975, which included the ability to produce a daily epidemiologic report.4 Within the next 10 years, programs written in computer programming languages such as BASIC allowed users to discern rates above an average baseline as being excessive, leading to enhanced surveillance detection.5 In 1986, researchers at LDS Hospital in Salt Lake City, Utah, described a system capable of identifying patients with hospital-acquired infections as well as inappropriate antimicrobial therapy.6 By the 1990s, everything began to take off, and, today, the domain is awash with commercial vendors, independent consultants, stand-alone or in-house developed systems, and, to a certain extent, confusion.
TERMINOLOGY AND COMPONENTS To alleviate some of this confusion, it is essential to differentiate between several key terms. Whereas data From Evanston Northwestern Healthcare, Department of Infection Control, Evanston, IL. Address correspondence to Marc-Oliver Wright, MT(ASCP), MS, Evanston Northwestern Healthcare, Department of Infection Control, Evanston Hospital, 2650 Ridge-Burch 124, Evanston, IL 60201. E-mail: [email protected].
0196-6553/$34.00 Copyright ª 2008 by the Association for Professionals in Infection Control and Epidemiology, Inc. doi:10.1016/j.ajic.2007.09.003
is a ‘‘measurement or characteristic’’ of a single ‘‘thing that is the focus of an information system,’’ by itself, it has little or no meaning.7 Conceptually, a single cell in a spreadsheet would constitute data. A positive blood culture result of ‘‘Staphylococcus aureus’’ is an example, which in the context of outbreak detection is essentially meaningless alone. Data aggregated and stored in structured formats are databases, which can vary from several rows and columns to terabytes in size. An admission discharge transfer database would contain many pieces of data aggregated to form records related to the locations and encounters of patients admitted to a specific institution(s). Databases collate large amounts of data but are unable (by themselves) to provide meaning. To calculate an average length of stay from the admission discharge transfer system, a method of analysis must be deployed. This places the data ‘‘within the context of analysis’’ and, as such, forms information.7 The most common example of information in infection control is the rate, where data (numerators and denominators) combine to form a mathematical expression of the frequency in which an event (numerator) occurs. In many instances, information itself is also insufficient and requires a final step to achieve its worth. Although a calculated rate of infection is helpful, it often requires a comparator; hence, knowledge is achieved when one applies ‘‘information by the use of rules.’’7 Knowledge places the information in its context with the real world and helps answer questions such as, ‘‘how much is too much?’’ and ‘‘does this require action?’’ If the expressed rate of central line-associated bloodstream infections (BSI) is compared with (for example) National Healthcare Safety Network (NHSN) pooled means, and the observed exceeds the expected/acceptable value, the value judgment (eg, this rate is unacceptable) and actions applied to correct the problem (eg, S1
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implementation of the Institute for Healthcare Improvement’s central line bundle) constitutes knowledge. Although the terms data, information, and knowledge imply a distinguishable hierarchy, the exact same scenario may present as different stages in different circumstances. A clean catch outpatient urine specimen with Escherichia coli may represent pieces of data to the ICP and knowledge (actionable) to the primary care provider. Recognizing these terms in the context in which they present remains important in planning and evaluating automated surveillance opportunities. Although most infection control programs have little trouble accessing data, many find themselves overwhelmed in the efforts to translate data into information and, finally, knowledge. However, if infection control programs are to transcend from regulatory requirements to change makers, our dependence on data must diminish. Enter ‘‘the study and application of methods to improve management of patient data . relevant to patient care and community health,’’ also known as medical informatics.8 Of note, this definition removes the emphasis on technology itself. Informatics is the study, whereas technology, including automated surveillance, is the tool. In the absence of astute and committed owners (ie, ICPs), this tool remains dulled, broken, or unused. Automated surveillance (AS) is the process of obtaining useful information from infection control data through the systematic application of medical informatics and computer science technologies. It may include, but is not limited to, either of the following: 1. Data mining: An application of mathematical and statistical techniques to large collections of data for the purpose of discovering patterns and relationships that can be used to classify and predict. Data mining (DM) is arguably the most widely varied term among users of automated surveillance systems. To some, it is equivalent to all forms of automated surveillance, whether these systems actually seek patterns in the data. Pattern detection and prediction are essential components of DM, and automated graphs and reports are not equivalent. DM applications are either supervised, in which a model is developed prior to analysis, or unsupervised, in which no such model is used.9 An example of supervised learning may be a DM application that applies a regression model evaluating length of stay and risk of infection, where risk is anticipated to increase over time. An unsupervised application example would be a report indicating that a higher percentage of gram-negative organisms recovered from the blood were resistant to imipenem compared with the past 6 months. The unsupervised version is the form more commonly associated
with the ‘‘black-box’’ imagery of DM, where data goes in and answers come out. However, the utility of these results is dependent on the ability of the DM system to differentiate potentially significant issues (such as the example above) from nonsensical ones (eg, persons with names beginning with the letter L are more likely to have positive urine cultures). 2. Hypothesis-based knowledge discovery: querybased data management that requires user input for development.10 This differs from traditional DM in that someone (ICP, developer, data analyst, or others) must first ask the question of interest. It operates at the discretion and direction of the individual but does not seek out patterns independently. An example here may include an ICP who wants a daily report of the number of patients currently hospitalized with a history of methicillin-resistant Staphylococcus aureus. The ICP creates and schedules a report to be automatically generated. Had the ICP not created or asked for such a report, the system would not necessarily indicate when such a readmission occurs, only when the occurrence of readmissions exceeded a predicted threshold. The emphasis and, thus, the responsibility are on the user rather than the system. The technology facilitates easier transformation of data into information and knowledge, but it does not identify patterns independently. Commercial AS systems commonly consist of DM and/or hypothesis-based knowledge discovery (HBKD) technologies to varying degrees of sophistication. The differentiation is in the process, whether the system independently identifies potentially significant events or whether the system must be told where to look. Similarly, report-generating tools are often included in such systems, with charts or graphs created for the end user. Although these outputs may be useful, they are often template or macro based and not necessarily the product of either DM or HBKD technology.
OUTPUTS AND IMPLEMENTATION Results from AS may come in a variety of formats. The role of the ICP is to understand clearly the results, such that they are able to effectively utilize them in the next step or articulate the interpretation in a meaningful way to direct care providers. Outputs of AS may include the following: 1. Individual alerts for sentinel events/organisms. Notification of any multiresistant organism recovered from any patient at any time may be an example here, and this may come as a result of either DM or HBKD.
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2. Ad hoc data requests. An example of this may include generating a list of patients potentially exposed to an active tuberculosis patient in an outpatient facility by requesting a list of all patients seen in the same area that day. Generating line lists is another example, such as gathering a list of potential case-patients after hearing that a nearby facility has had imipenem-resistant Pseudomonas infections in their pulmonary service patients. With such requests, data understanding is essential. If in the above example, the clinical microbiology laboratory tests only meropenem rather than imipenem, the query is likely to report zero potential cases, unless the antibiotic classes are already mapped. This is commonly the product of systems using HBKD. 3. Automated cluster detection utilizing statistical processes such as control charts, analysis of variance, and others. Notification of an increase in methicillin-susceptible S aureus in an intensive care unit is an example, and, although most commonly associated with DM, advanced HBKD systems are capable of producing similar results, although in a user and developer defined context. This category would also include syndromic surveillance and similar statistical-based surveillance methodologies. The key is to understand the strengths and limitations of the statistical tests used and to maximize their effectiveness (sensitivity and specificity). 4. Fully automated infection reports. By developing, testing, and deploying a set of algorithms or decision rules, complete surveillance reports are generated such as a report detailing urinary tract infections caused by Escherichia coli per 1000 patient-days. Although theoretically HBKD based, the algorithms are rather complex and more likely generated and tested on the production side rather than user defined. This category represents one of the most sophisticated AS products to date. However, it is important to understand how the algorithms are defined, how these compare with traditional or standard definitions (sensitivity, specificity, positive and negative predictive values), and whether direct comparisons can be made to established benchmarks (eg, NHSN). Development and implementation of AS usually requires extensive time and resource allocation, although not necessarily and not entirely on the part of the ICP. Common to both commercial and independent systems is the need for effective information resource management planning, which includes understanding, and simplifying the work processes of doing infection control with the implementation of a new AS system.11 To understand the business, the ICP must define for themselves and more often others (system developers)
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exactly how they do their job and how or why the specific actions are important. By describing the normal work flows, systems developers are able to address the ICPs needs or articulate shortcomings of the technology in advance. This step often consumes the lion share of development and implementation resources, but failing to adequately invest in this area has significantly larger costs in the long-term when the system fails to meet the needs of the program.11 Understanding the business further describes and defines the needs of the end user (ICP) and acts as a catalyst for the next step. In simplifying the business of infection control, the existing infection control program is critically evaluated for redundancies, error potential, and streamlined work flows.11 These first 2 steps are critical because the emphasis is on the infection control program rather than the technology itself and allows the ICP to articulate their needs and plan ahead for the coming changes to their daily work. Following these steps, development and deployment of AS systems will require varying amounts of resources, depending on whether the system is from a commercial source (already developed and thus assumed to more readily adapt to different hospital platforms) or in-house (presumably easily deployed, but development may be extensive), the extent of validation needed, and the training/education programs for the ICPs. The process from start to finish is months if not years and remains ongoing in light of upgrades and newly identified needs.
BENEFITS TO AUTOMATION Automated surveillance technologies have been warmly welcomed in infection control for several obvious reasons: time saved in reducing manual efforts, reduced error potential, enhanced surveillance capabilities, and ease of access.12 Infection control programs are continually faced with competing priorities and reduced or insufficient resources. Expectations of expanded surveillance and reporting, whether from internal, regulatory, or public (SURVEILLANCE), demand time from practitioners, often at the expense of education, observation, and behavior modification (PREVENTION). To reverse this trend and hopefully expand the role of the ICP as an agent of change within a health care delivery system, surveillance must absorb fewer human resources. Although most publications describing AS systems are retrospective or a before-after design, virtually all report a time-savings benefit.6,13-22 As seen in Table 1, several studies have attempted to quantify this benefit. Beginning in 1986, Evans et al6 reported a 65% reduction in manual surveillance efforts for ICPs at LDS Hospital, whereas, as recent as 2006, Brosette et al16
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Table 1. Sensitivity, specificity, and time benefits to AS Author
Surveillance
Sensitivity (95% CI If available)
Specificity (95% CI If available)
Brossette et al16 Evans et al6 Pokorny et al17 Bellini et al18 Trick et al19 Bouam et al20 Chalfine et al15 Haas et al21
All HAI All HAI All HAI BSIs BSIs BSI/UTI SSIs VAPs
0.65 (0.76–0.98) Positive predictive value, 0.77 0.943 (0.795–0.99) Catheter-0.78 Secondary-0.88 0.81 0.91 (0.89–0.93) 0.843 (0.66–0.94) 0.71
0.984 (0.98–0.98) N/A 0.838(0.788–0.889) Catheter-0.93 Secondary-0.69 0.72 0.91 (0.89–0.93) 0.999 0.998
Time 1.5 FTE per 10,000 admissions 65% Reduction N/A N/A 458 hours per year saved 36 mins, 21 sec per unit per week 90 vs 223 Hours (60% reduction) N/A
FTE, Full time equivalent infection control professional; HAI, health-care associated infection; BSI, blood stream infection; UTI, urinary tract infection; VAP, ventilator associated pneumonia; CI, Confidence interval; N/A, Not available.
approximated that 1.5 full-time employees for every 10,000 admissions would be required to perform effectively the equivalent surveillance. Chalfine et al15 reported a 60% reduction in efforts targeting surgical site infections, whereas Trick et al19 reduced bloodstream infection surveillance efforts by nearly 11.5 weeks per annum, and Bouam et al20 reduced efforts by 36.35 minutes per week per unit with regards to bloodstream infection and urinary tract infection surveillance. What is not quantified, although mentioned anecdotally in a number of reports, is how this newfound time is used. Evans et al describes this benefit taking the form of ‘‘audits of patient care practices, increased in-service and education, earlier and more effective isolations, and improved analysis and feedback of surveillance data.’’6 The absence of AS leaves the practitioner(s) to manually collect, review, and transform data into knowledge. A key step in the process, review, demands excellent attention to detail and the ability to recognize patterns across time and distance. Understandably, it can be an imperfect practice. However, these limitations can, and often do, lead to missed opportunities for cluster detection. Peterson and Brosette provide a clear example of this by using conservative estimates related to facility size and organisms of interest to calculate a staggering 21 billion potential combinations of interest.10 Studies have repeatedly shown AS to improve or have the potential to improve event detection above and beyond the efforts of the existing program resources.6,13-22 Evans et al6 noted that AS identified 24% more patients with health care-associated infections, and a study from the University of Maryland found that 6 of the 11 potential clusters picked up by AS were not identified when compared with traditional practitioner-based surveillance.14 These enhanced surveillance capabilities can allow infection control programs to expand their surveillance activities beyond the traditional annual targeted surveillance plan, which is currently dictated, in part, by available resources. However, there seems to be a lack of published reports indicating how, if at all,
technology has fundamentally altered the manner in which the business of infection control is done. Last, enhanced telecommunication technologies within and outside health care facilities can facilitate greater access to health care data. Security and privacy are most important, yet the technology exists to allow safe and protected access to these systems outside the office. Although not a fully automated system, Farley et al utilized personal digital assistants in conjunction with an interrelational database to facilitate urinary tract infection surveillance, with resulting time and cost benefit to their infection control program.23 Web-based AS systems, combined with expanding availability of wireless technology within hospitals, should allow practitioners to leave their desks behind and become an active member of the interdisciplinary health care delivery team by being a visible presence in the unit.
LIMITATIONS Technology is not without its limits, and AS is no exception. Paramount to these is the up-front cost investment and the need to obtain financial support. Although projected and proposed costsavings are commonly reported in the literature and marketing materials, Franklin’s adage of ‘‘a penny saved is a penny earned’’ is difficult to prove and sometimes dismissed. Cost is not exclusive to commercial entities. Wisniewski et al estimate an up-front investment of approximately 4000 hours in development alone to construct an internal clinical data warehouse for infection control.13 Investment does not end once the system is in place. Technology’s only constant is constant change, and system owners (users, developers, administration) must be prepared for such change, including from a financial standpoint.13 Money does not guarantee success. Even the most advanced systems are unable to control the most likely source of system failure: people. Failure can come from a variety of nontechnical angles including projects not meeting user requirements or expectations (often because of a lack of involving users in development), lack of planning, and ineffective communication
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between users and developers.24 Learning curves of AS systems vary as does the knowledge and comfort level of the user. In a 1994 study of 8380 information technology projects, nearly one third were assessed to be complete failures, and only 16.2% were fully successful. This resulted in a projected financial loss of $140 billion. The leading success factors included user involvement and executive support, whereas the most commonly reported failure characteristics were lack of user input and poorly defined or changing system requirements.25 The most recent report from the same group showed marked improvement. Of 13,522 projects, a full one third succeeded, whereas 15% failed according to the 2003 report.26 This further underscores the need for effective planning including early ICP involvement and clearly defined work flows during system development. In addition, there remain underlying technical issues with the potential to provoke even the most advanced AS systems. Without consistent quality source data from within the institution, systems may fall prey to the aphorism GIGO (garbage-in, garbage-out). Similarly, changes to internal data structures or sources, ranging from a complete overhaul to changing a microbiology susceptibility panel, can lead to erroneous results. Although many AS systems are able to detect these aberrations, these events demand ongoing time and resources. It is especially important that the end users not become lackadaisical with the systems themselves but maintain vigilance and not assume that technology is a substitute for the critical-thinking ICP. Last, medicine as a whole suffers from the lack of a truly universal medical vocabulary, making decision rules based in part on clinical descriptions difficult at best.27
FUTURE CHALLENGES AND OPPORTUNITIES The 2004 ‘‘Health Information Technology Plan,’’ released by the White House, challenged the industry to outfit Americans with electronic health records by 2014. Efforts to meet this mandate have been somewhat successful and have potential benefits to AS.28 Mobile and uniform health care data should facilitate increased and easier communication of preexisting conditions, procedures, previous laboratory results, and others among health care facilities.29 Efforts toward electronic health records will require further development of universal medical terminology, easing the path to greater refinement of fully automated surveillance and facilitating easier adaptation of casemix adjustments and the use of standard surveillance definitions. The technology will advance, the systems will continue to improve, and the opportunities will expand. The question remains, what is next? From a technology standpoint, AS systems may be able to expand beyond surveillance to decision
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support. A recent study addressed the issue of predicting risk for multidrug-resistant organisms on admission.30 Common elements of risk include previous hospitalization, antimicrobial use, history of colonization or infection, and residence in long-term care facilities. Most, if not all, of these elements are available in the same data sources used for AS systems. Furthermore, these models may be institution specific, and sophisticated DM models may be deployed to predict and adjust over time. If the flow of information between an institution’s health care information infrastructure and AS systems becomes bidirectional, these models could be deployed to issue standing orders for surveillance cultures of high-risk patients to the admitting physician. Similarly, orders written for contact, droplet, and airborne isolation could be sent out automatically the moment the result is entered in the clinical laboratory, reducing the delay and decreasing the potential for cross transmission. Future developments are on the horizon, and ICPs should actively participate in the process to voice their wishes for tomorrow and hopefully guide future enhancements. To most effectively utilize AS now and going forward, ICPs, individually or as a profession, should consider allowing this technology to fundamentally alter the manner in which surveillance is traditionally done. By harnessing this tool, programs may consider returning to whole house surveillance, decentralizing ICPs from the office to the inpatient unit or adopting nontraditional measures as quality indicators. In today’s world, feedback of surveillance data to health care providers has already been shown to improve performance.31 Regional infection control networks are demonstrating added benefit.32 Recently, Mah and Meyers noted that traditional didactic education methods often used by ICPs lack success and that a more social, interactive, and multimodal approach may offer better results.33 Each of these require an investment of time on the part of the practitioner. In an editorial, John Burke of LDS Hospital noted that the time saved by automation will actually lead to the need for greater (not less) human efforts aimed at managing the previously unanswerable questions.34 Perhaps the lasting legacy of surveillance technology will be its ability to ease the burden of data management from the ICP to go forth and accomplish the profession’s highest calling: prevention. References 1. Anderson MJ. The American census: a social history. New Haven: Yale University Press; 1988. p. 197. 2. Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science 1959;130:9-21. 3. Warner HR, Toronot AF, Veasey LG, Stephenson R. A mathematical approach to medical diagnosis: application to congenital heart disease. JAMA 1961;177:75-81.
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4. Jorgensen JH, Holmes P, Williams WL, Harris JL. Computerization of a hospital clinical microbiology laboratory. Am J Clin Pathol 1978;69: 605-14. 5. Schifman RB, Palmer RA. Surveillance of nosocomial infections by computer analysis of positive culture rates. J Clin Microbiol 1985;21:493-5. 6. Evans RS, Larsen RA, Burke JP, Gardner RM, Meier FA, Jacobson JA, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:1007-11. 7. Lumpkin JR. History and significance of information systems in public health. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 18. 8. Wyatt JC, Liu JLY. Basic concepts in medical informatics. J Epidemiol Community Health 2002;56:808-12. 9. Kroenke DM. Database processing. Upper Saddle River, NJ: Pearson Prentice Hall; 2006. p. 549-54. 10. Peterson LR, Brossette SE. Hunting health care-associated infections from the clinical microbiology laboratory: passive, active and virtual surveillance. J Clin Microbiol 2002;40:1-4. 11. O’Carroll PW. Information architecture. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 85-97. 12. Obenshain MK. Application of data mining techniques to healthcare data. Infect Control Hosp Epidemiol 2004;25:690-5. 13. Wisniewski MF, Kieszkowski P, Zagorski BM, Trick WE, Sommers M, Weinstein RA. Development of a clinical data warehouse for hospital infection control. J Am Med Inform Assoc 2003;10:454-62. 14. Wright MO, Perencevich EN, Novak C, Hebden JN, Standiford HC, Harris AD. Preliminary assessment of an automated surveillance system for infection control. Infect Control Hosp Epidemiol 2004;25:325-32. 15. Chalfine A, Cauet D, Lin WC, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol 2006;27:794-801. 16. Brossette SE, Hacek DM, Gavin PJ, Kamdar MA, Gadbois KD, Fisher AG, et al. A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance? Am J Clin Pathol 2006;125:34-9. 17. Pokorny L, Rovira A, Martin-Baranera M, Gimeno C, Alonso-Tarres C, Vilarasau J. Automatic detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol 2006;27:500-3. 18. Bellini C, Petignat C, Francioli P, Wenger A, Bille J, Klopotov A, et al. Comparison of automated strategies for surveillance of nosocomial bacteremia. Infect Control Hosp Epidemiol 2007;28:1030-5. 19. Trick WE, Zagorski BM, Tokars JI, Vernon MO, Welbel SF, Wisniewski MF, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10:1612-20.
Wright 20. Bouam S, Girou E, Brun-Buisson C, Karadimas H, Lepage E. An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians’ self-reports. Infect Control Hosp Epidemiol 2003;24:51-5. 21. Haas JP, Mendonca EA, Ross B, Friedman C, Larson E. Use of computerized surveillance to detect nosocomial pneumonia in neonatal intensive care unit patients. Am J Infect Control 2005;33:439-43. 22. Ma L, Tsui FC, Hogan WR, Wagner MM, Ma H. A framework for infection control surveillance using association rules. AMIA Annu Symp Proc 2003;410-4. 23. Farley JE, Srinivasan A, Richards A, Song X, McEachen J, Perl TM. Handheld computer surveillance: shoe-leather epidemiology in the ‘‘palm’’ of your hand. Am J Infect Control 2005;33:444-9. 24. Kitch P, Yasanoff WA. Managing IT personnel and projects. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 159-78. 25. The CHAOS Report: 1994. Available at: http://www.standishgroup. com/sample_research/chaos_1994_1.php. Accessed March 29, 2007. 26. Latest Standish Group CHAOS Report shows project success rates have improved by 50%. Available at: http://www. standishgroup.com/press/article.php?id52. Accessed March 29, 2007. 27. Jernigan DB, Davies J, Sim A. Data standards in public health information. In: O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York, NY: Springer; 2003. p. 218-22. 28. Brownback and Moore praise health information technology plan. In HIMSS News, December 6, 2007. Available at: http://www.himss.org/ASP/ ContentRedirector.asp?ContentId566342&type5HIMSSNewsItem. Accessed March 29, 2007. 29. McGrow KM, Roys R, Maloney RC, Xiao Y. Using wireless technologies to improve information flow for interhospital transfers of critical care patients. Crit Care Nurse 2004;24:66-72 114. 30. Furuno JP, McGregor JC, Harris AD, Johnson JA, Johnson JK, Langenberg P, et al. Identifying groups at high risk for carriage of antibioticresistant bacteria. Arch Intern Med 2006;166:580-5. 31. Richards C, Emori TG, Peavy G, Gaynes R. Free full text promoting quality through measurement of performance and response: prevention success stories. Emerg Infect Dis 2001;7:299-301. 32. Kaye KS, Engemann JJ, Fulmer EM, Clark CC, Noga EM, et al. Favorable impact of an infection control network on nosocomial infection rates in community hospitals. Infect Control Hosp Epidemiol 2006; 27:228-32. 33. Mah MW, Meyers G. Toward a socioethical approach to behavior change. Am J Infect Control 2006;34:73-9. 34. Burke JP. Surveillance, reporting, automation, and interventional epidemiology. Infect Control Hosp Epidemiol 2003;24:10-2.