Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction

Perceptions of infection control professionals toward electronic surveillance software supporting inpatient infections: A mixed methods study

BACKGROUND

Electronic surveillance software (ESS) collects multiple patient data from hospital software to assist infection control professionals in the prevention and control of hospital-associated infections. This study aimed to understand the perceptions of end users (i.e., infection control professionals) and the facilitators and barriers related to a commercial ESS named ZINC and to assess its usability.

METHODS

A mixed-method research approach was adopted among infection control professionals 10 months after the implementation of commercial ESS in the university hospital of Nancy, France. A qualitative analysis based on individual semistructured interviews was conducted to collect professionals' perceptions of ESS and to understand barriers and facilitators. Qualitative data were systematically coded and thematically analyzed. A quantitative analysis was performed using the System Usability Scale (SUS).

RESULTS

Thirteen infection control professionals were included. Qualitative analysis revealed technical, organizational and human barriers to the installation and use stages and five significant facilitators: the relevant design of the ESS, the improvement of infection prevention and control practices, the designation of a champion/superuser among professionals, training, and collaboration with the developer team. Quantitative analysis indicated that the evaluated ESS was a "good" system in terms of perceived ease of use, with an overall median SUS score of 85/100.

CONCLUSIONS

This study shows the value of ESS to support inpatient infections as perceived by infection control professionals. It reveals barriers and facilitators to the implementation and adoption of ESS. These barriers and facilitators should be considered to facilitate the installation of the software in other hospitals.

Semiautomated surveillance of deep surgical site infections after colorectal surgeries: A multicenter external validation of two surveillance algorithms

Objective:

Automated surveillance methods increasingly replace or support conventional (manual) surveillance; the latter is labor intensive and vulnerable to subjective interpretation. We sought to validate 2 previously developed semiautomated surveillance algorithms to identify deep surgical site infections (SSIs) in patients undergoing colorectal surgeries in Dutch hospitals.

Design:

Multicenter retrospective cohort study.

Methods:

From 4 hospitals, we selected colorectal surgery patients between 2018 and 2019 based on procedure codes, and we extracted routine care data from electronic health records. Per hospital, a classification model and a regression model were applied independently to classify patients into low- or high probability of having developed deep SSI. High-probability patients need manual SSI confirmation; low-probability records are classified as no deep SSI. Sensitivity, positive predictive value (PPV), and workload reduction were calculated compared to conventional surveillance.

Results:

In total, 672 colorectal surgery patients were included, of whom 28 (4.1%) developed deep SSI. Both surveillance models achieved good performance. After adaptation to clinical practice, the classification model had 100% sensitivity and PPV ranged from 11.1% to 45.8% between hospitals. The regression model had 100% sensitivity and 9.0%–14.9% PPV. With both models, <25% of records needed review to confirm SSI. The regression model requires more complex data management skills, partly due to incomplete data.

Conclusions:

In this independent external validation, both surveillance models performed well. The classification model is preferred above the regression model because of source-data availability and less complex data-management requirements. The next step is implementation in infection prevention practices and workflow processes.

Transmission visualizations of healthcare infection clusters: A scoping review

OBJECTIVE

To evaluate infectious pathogen transmission data visualizations in outbreak publications.

DESIGN

Scoping review.

METHODS

Medline was searched for outbreak investigations of infectious diseases within healthcare facilities that included ≥1 data visualization of transmission using data observable by an infection preventionist showing temporal and/or spatial relationships. Abstracted data included the nature of the cluster(s) (pathogen, scope of transmission, and individuals involved) and data visualization characteristics including visualization type, transmission elements, and software.

RESULTS

From 1,957 articles retrieved, we analyzed 30 articles including 37 data visualizations. The median cluster size was 20.5 individuals (range, 7-1,963) and lasted a median of 214 days (range, 12-5,204). Among the data visualization types, 10 (27%) were floor-plan transmission maps, 6 (16%) were timelines, 11 (30%) were transmission networks, 3 (8%) were Gantt charts, 4 (11%) were cluster map, and 4 (11%) were other types. In addition, 26 data visualizations (70%) contained spatial elements, 26 (70%) included person type, and 19 (51%) contained time elements. None of the data visualizations contained contagious periods and only 2 (5%) contained symptom-onset date.

CONCLUSIONS

The data visualizations of healthcare-associated infectious disease outbreaks in the systematic review were diverse in type and visualization elements, though no data visualization contained all elements important to deriving hypotheses about transmission pathways. These findings aid in understanding the visualizing transmission pathways by describing essential elements of the data visualization and will inform the creation of a standardized mapping tool to aid in earlier initiation of interventions to prevent transmission.

Strategies for Effective Infection Prevention Programs: Structures, Processes, and Funding

Successful Infection Prevention Programs (IPPs) consist of a multidisciplinary team led by a hospital epidemiologist and managed by infection preventionists. Knowledge of the economics of health care-associated infections (HAIs) and the ability to make a business plan is now essential to the success of programs. Prevention of HAIs is the core function of IPPs with impact on patient outcomes, quality of care, and cost savings for hospitals. This article discusses the structure and responsibilities of an IPP, the regulatory pressures and opportunities that these programs face, and how to build and manage a successful program.

Do we need to change catheter-related bloodstream infection surveillance in the Netherlands? A qualitative study among infection prevention professionals

OBJECTIVES

Catheter-related bloodstream infections (CRBSI) are a common healthcare-associated infection and therefore targeted by surveillance programmes in many countries. Concerns, however, have been voiced regarding the reliability and construct validity of CRBSI surveillance and the connection with the current diagnostic procedures. The aim of this study was to explore the experiences of infection control practitioners (ICPs) and medical professionals with the current CRBSI surveillance in the Netherlands and their suggestions for improvement.

DESIGN

Qualitative study using focus group discussions (FGDs) with ICPs and medical professionals separately, followed by semistructured interviews to investigate whether the points raised in the FGDs were recognised and confirmed by the interviewees. Analyses were performed using thematic analyses.

SETTING

Basic, teaching and academic hospitals in the Netherlands.

PARTICIPANTS

24 ICPs and 9 medical professionals.

RESULTS

Main themes derived from experiences with current surveillance were (1) ICPs' doubt regarding the yield of surveillance given the low incidence of CRBSI, the high workload and IT problems; (2) the experienced lack of leadership and responsibility for recording information needed for surveillance and (3) difficulties with applying and interpreting the CRBSI definition. Suggestions were made to simplify the surveillance protocol, expand the follow-up and surveillance to homecare settings, simplify the definition and customise it for specific patient groups. Participants reported hoping for and counting on automatisation solutions to support future surveillance.

CONCLUSIONS

This study reveals several problems with the feasibility and acceptance of the current CRBSI surveillance and proposes several suggestions for improvement. This provides valuable input for future surveillance activities, thereby taking into account automation possibilities.

Governance aspects of large-scale implementation of automated surveillance of healthcare-associated infections

OBJECTIVES

Surveillance of healthcare-associated infections (HAI) is increasingly automated by applying algorithms to routine-care data stored in electronic health records. Hitherto, initiatives have mainly been confined to single healthcare facilities and research settings, leading to heterogeneity in design. The PRAISE network - Providing a Roadmap for Automated Infection Surveillance in Europe - designed a roadmap to provide guidance on how to move automated surveillance (AS) from the research setting to large-scale implementation. Supplementary to this roadmap, we here discuss the governance aspects of automated HAI surveillance within networks, aiming to support both the coordinating centres and participating healthcare facilities as they set up governance structures and to enhance involvement of legal specialists.

METHODS

This article is based on PRAISE network discussions during two workshops. A taskforce was installed that further elaborated governance aspects for AS networks by reviewing documents and websites, consulting experts and organizing teleconferences. Finally, the article has been reviewed by an independent panel of international experts.

RESULTS

Strict governance is indispensable in surveillance networks, especially when manual decisions are replaced by algorithms and electronically stored routine-care data are reused for the purpose of surveillance. For endorsement of AS networks, governance aspects specifically related to AS networks need to be addressed. Key considerations include enabling participation and inclusion, trust in the collection, use and quality of data (including data protection), accountability and transparency.

CONCLUSIONS

This article on governance aspects can be used by coordinating centres and healthcare facilities participating in an AS network as a starting point to set up governance structures. Involvement of main stakeholders and legal specialists early in the development of an AS network is important for endorsement, inclusivity and compliance with the laws and regulations that apply.

PRAISE: providing a roadmap for automated infection surveillance in Europe

INTRODUCTION

Healthcare-associated infections (HAI) are among the most common adverse events of medical care. Surveillance of HAI is a key component of successful infection prevention programmes. Conventional surveillance - manual chart review - is resource intensive and limited by concerns regarding interrater reliability. This has led to the development and use of automated surveillance (AS). Many AS systems are the product of in-house development efforts and heterogeneous in their design and methods. With this roadmap, the PRAISE network aims to provide guidance on how to move AS from the research setting to large-scale implementation, and how to ensure the delivery of surveillance data that are uniform and useful for improvement of quality of care.

METHODS

The PRAISE network brings together 30 experts from ten European countries. This roadmap is based on the outcome of two workshops, teleconference meetings and review by an independent panel of international experts.

RESULTS

This roadmap focuses on the surveillance of HAI within networks of healthcare facilities for the purpose of comparison, prevention and quality improvement initiatives. The roadmap does the following: discusses the selection of surveillance targets, different organizational and methodologic approaches and their advantages, disadvantages and risks; defines key performance requirements of AS systems and suggestions for their design; provides guidance on successful implementation and maintenance; and discusses areas of future research and training requirements for the infection prevention and related disciplines. The roadmap is supported by accompanying documents regarding the governance and information technology aspects of implementing AS.

CONCLUSIONS

Large-scale implementation of AS requires guidance and coordination within and across surveillance networks. Transitions to large-scale AS entail redevelopment of surveillance methods and their interpretation, intensive dialogue with stakeholders and the investment of considerable resources. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists.

Validation of semiautomated surgical site infection surveillance using electronic screening algorithms in 38 surgery categories

OBJECTIVE

To verify the validity of a semiautomated surgical site infection (SSI) surveillance system using electronic screening algorithms in 38 categories of surgery.

DESIGN

A cohort study for validation of semiautomated SSI surveillance system using screening algorithms.

SETTING

A 1,989-bed tertiary-care referral center in Seoul, Republic of Korea.

METHODS

A dataset of 40,516 surgical procedures in 38 categories stored in the conventional SSI surveillance registry at the Samsung Medical Center between January 2013 and December 2014 was used as the reference standard. In the semiautomated surveillance system, electronic screening algorithms flagged cases meeting at least 1 of 3 criteria: antibiotic prescription, microbial culture, and infectious disease consultation. Flagged cases were audited by infection preventionists. Analyses of sensitivity, specificity, and positive predictive value (PPV) were conducted for the semiautomated surveillance system, and its effect on reducing the workload for chart review was evaluated.

RESULTS

A total of 575 SSI events (1·42%) were identified by conventional SSI surveillance. The sensitivity of the semiautomated SSI surveillance was 96·7%, and the PPV of the screening algorithms alone was 4·1%. Semiautomated SSI surveillance reduced the chart review workload of the infection preventionists from 1,283 to 482 person hours per year (a 62·4% decrease).

CONCLUSIONS

Compared to conventional surveillance, semiautomated surveillance using electronic screening algorithms followed by chart review of selected cases can provide high-validity surveillance results and can significantly reduce the workload of infection preventionists.

Real-Time, Automated Detection of Ventilator-Associated Events: Avoiding Missed Detections, Misclassifications, and False Detections Due to Human Error

OBJECTIVETo validate a system to detect ventilator associated events (VAEs) autonomously and in real time.DESIGNRetrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.SETTINGThe Massachusetts General Hospital, a tertiary-care academic health center, during January-March 2015 (development cohort) and January-March 2016 (validation cohort).PATIENTSVentilated patients in 4 intensive care units.METHODSThe automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance-automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.RESULTSIn the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.CONCLUSIONSManual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.Infect Control Hosp Epidemiol 2018;826-833.

Building a Successful Infection Prevention Program: Key Components, Processes, and Economics

Infection control is the discipline responsible for preventing health care-associated infections (HAIs) and has grown from an anonymous field, to a highly visible, multidisciplinary field of incredible importance. There has been increasing focus on prevention rather than control of HAIs. Infection prevention programs (IPPs) have enormous scope that spans multiple disciplines. Infection control and the prevention and elimination of HAIs can no longer be compartmentalized. This article discusses the structure and responsibilities of an IPP, the regulatory pressures and opportunities that these programs face, and how to build and manage a successful program.

Role of Human Factors Engineering in Infection Prevention: Gaps and Opportunities

Human factors engineering (HFE), with its focus on studying how humans interact with systems, including their physical and organizational environment, the tools and technologies they use, and the tasks they perform, provides principles, tools, and techniques for systematically identifying important factors, for analyzing and evaluating how these factors interact to increase or decrease the risk of Healthcare-associated infections (HAI), and for identifying and implementing effective preventive measures. We reviewed the literature on HFE and infection prevention and control and identified major themes to document how researchers and infection prevention staff have used HFE methods to prevent HAIs and to identify gaps in our knowledge about the role of HFE in HAI prevention and control. Our literature review found that most studies in the healthcare domain explicitly applying (HFE) principles and methods addressed patient safety issues not infection prevention and control issues. In addition, most investigators who applied human factors principles and methods to infection prevention issues assessed only one human factors element such as training, technology evaluations, or physical environment design. The most significant gap pertains to the limited use and application of formal HFE tools and methods. Every infection prevention study need not assess all components in a system, but investigators must assess the interaction of critical system components if they want to address latent and deep-rooted human factors problems.

Electronic Surveillance of Surgical Site Infections

BACKGROUND

Electronic health and administrative data are increasingly being used for identifying surgical site infections (SSI). We found an unexpectedly high number of patients who could not be classified definitively as having an infection or not. To further explore this, we present an electronic classification algorithm for conservative case finding and identify alterations that would adapt the method for other purposes.

METHODS

Two computer algorithms were created to identify SSI. One model used a strict National Healthcare Safety Network (NHSN) based SSI algorithm, which was applied to all discharges from 443,284 all discharges from four hospitals in Manhattan, NY, 2009 through 2012. The second model used discharges that only had NHSN-defined SSI procedures during the same period.

RESULTS

The strict SSI algorithm was able to classify SSI status for 27.3% of discharges; there was a high number of indeterminate cases. In contrast, the modified, less strict model, classified 97.2% of discharges with NHSN-approved SSI procedures.

CONCLUSION

Electronic records provide several options for aiding with the identification of infections in healthcare settings and can be tailored to suit specific uses. While algorithms for SSI classification should reflect the NHSN definition, our research emphasizes how variations of model building can affect the number of indeterminate cases that may necessitate manual review.

Slow adoption of automated infection prevention surveillance: are human factors contributing?

Although automated surveillance technology has been evolving for decades, adoption of these technologies is in a nascent state. The current trajectory of public reporting, continued emergence of multidrug-resistant organisms, and mandated antimicrobial stewardship initiatives will result in an increased surveillance workload for ICPs. The use of traditional surveillance methods will be inefficient in meeting the demands for more data and are potentially flawed by subjective interpretation. An examination is offered of the slow adoption of automated surveillance technology from a system perspective with the inherent ambiguities that may operate within the ICP work structure. Formal qualitative research is needed to assess the human factors associated with lack of acceptance of automated surveillance systems. Identification of these factors will allow the National Healthcare Safety Network and professional organizations to offer educational programs and mentoring to the ICP community that target knowledge deficits and the embedded culture that embraces the status quo. With the current focus on fully electronic surveillance systems that perform surveillance in its entirety without case review, effective use of the data will be dependent on ICP skills and their understanding of the strengths and limitations of output from algorithmic detection models.

Clinical decision support systems and infection prevention: to know is not enough

Clinical decision support (CDS) systems are an increasingly used form of technology designed to guide health care providers toward established protocols and best practices with the intent of improving patient care. Utilization of CDS for infection prevention is not widespread and is particularly focused on antimicrobial stewardship. This article provides an overview of CDS systems and summarizes key attributes of successfully executed tools. A selection of published reports of CDS for infection prevention and antimicrobial stewardship are described. Finally, an individual organization describes its CDS infrastructure, process of prioritization, design, and development, with selected highlights of CDS tools specifically targeting common infection prevention quality improvement initiatives.

Hospital infection control units: staffing, costs, and priorities

BACKGROUND

This article describes infection prevention and control professionals' (ICPs') staffing levels, patient outcomes, and costs associated with the provision of infection prevention and control services in Australian hospitals. A secondary objective was to determine the priorities for infection control units.

METHODS

A cross-sectional study design was used. Infection control units in Australian public and private hospitals completed a Web-based anonymous survey. Data collected included details about the respondent; hospital demographics; details and services of the infection control unit; and a description of infection prevention and control-related outputs, patient outcomes, and infection control priorities.

RESULTS

Forty-nine surveys were undertaken, accounting for 152 Australian hospitals. The mean number of ICPs was 0.66 per 100 overnight beds (95% confidence interval, 0.55-0.77). Privately funded hospitals have significantly fewer ICPs per 100 overnight beds compared with publicly funded hospitals (P < .01). Staffing costs for nursing staff in infection control units in this study totaled $16,364,392 (mean, $380,566). Infection control units managing smaller hospitals (<270 beds) identified the need for increased access to infectious diseases or microbiology support.

CONCLUSION

This study provides valuable information to support future decisions by funders, hospital administrators, and ICPs on service delivery models for infection prevention and control. Further, it is the first to provide estimates of the resourcing and cost of staffing infection control in hospitals at a national level.

Introduction of software tools for epidemiological surveillance in infection control in Colombia

INTRODUCTION

Healthcare-Associated Infections (HAI) are a challenge for patient safety in the hospitals. Infection control committees (ICC) should follow CDC definitions when monitoring HAI. The handmade method of epidemiological surveillance (ES) may affect the sensitivity and specificity of the monitoring system, while electronic surveillance can improve the performance, quality and traceability of recorded information.

OBJECTIVE

To assess the implementation of a strategy for electronic surveillance of HAI, Bacterial Resistance and Antimicrobial Consumption by the ICC of 23 high-complexity clinics and hospitals in Colombia, during the period 2012-2013.

METHODS

An observational study evaluating the introduction of electronic tools in the ICC was performed; we evaluated the structure and operation of the ICC, the degree of incorporation of the software HAI Solutions and the adherence to record the required information.

RESULTS

Thirty-eight percent of hospitals (8/23) had active surveillance strategies with standard criteria of the CDC, and 87% of institutions adhered to the module of identification of cases using the HAI Solutions software. In contrast, compliance with the diligence of the risk factors for device-associated HAIs was 33%.

CONCLUSIONS

The introduction of ES could achieve greater adherence to a model of active surveillance, standardized and prospective, helping to improve the validity and quality of the recorded information.

Impact of Electronic Surveillance on Isolation Practices

Objective.

To assess the impact of an electronic surveillance system on isolation practices and rates of methicillin-resistant Staphylococcus aureus (MRSA).

Design.

A pre-post test intervention.

Setting.

Inpatient units (except psychiatry and labor and delivery) in 4 New York City hospitals.

Patients.

All patients for whom isolation precautions were indicated, May 2009–December 2011.

Methods.

Trained observers assessed isolation sign postings, availability of isolation carts, and staff use of personal protective equipment (PPE). Infection rates were obtained from the infection control department. Regression analyses were used to examine the association between the surveillance system, infection prevention practices, and MRSA infection rates.

Results.

A total of 54,159 isolation days and 7,628 staff opportunities for donning PPE were observed over a 31-month period. Odds of having an appropriate sign posted were significantly higher after intervention than before intervention (odds ratio [OR], 1.10 [95% confidence interval {CI}, 1.01–1.20]). Relative to baseline, postintervention sign posting improved significantly for airborne and droplet precautions but not for contact precautions. Sign posting improved for vancomycin-resistant enterococci (OR, 1.51 [95% CI, 1.23–1.86]; P = .0001), Clostridium difficile (OR, 1.59 [95% CI, 1.27–2.02]; P = .00005), and Acinetobacter baumannii (OR, 1.41 [95% CI, 1.21–1.64]; P = .00001) precautions but not for MRSA precautions (OR, 1.11 [95% CI, 0.89–1.39]; P = .36). Staff and visitor adherence to PPE remained low throughout the study but improved from 29.1% to 37.0% after the intervention (OR, 1.14 [95% CI, 1.01–1.29]). MRSA infection rates were not significantly different after the intervention.

Conclusions.

An electronic surveillance system resulted in small but statistically significant improvements in isolation practices but no reductions in infection rates over the short term. Such innovations likely require considerable uptake time.

Development of trigger-based semi-automated surveillance of ventilator-associated pneumonia and central line-associated bloodstream infections in a Dutch intensive care

Background

Availability of a patient data management system (PDMS) has created the opportunity to develop trigger-based electronic surveillance systems (ESSs). The aim was to evaluate a semi-automated trigger-based ESS for the detection of ventilator-associated pneumonia (VAP) and central line-associated blood stream infections (CLABSIs) in the intensive care.

Methods

Prospective comparison of surveillance was based on a semi-automated ESS with and without trigger. Components of the VAP/CLABSI definition served as triggers. These included the use of VAP/CLABSI-related antibiotics, the presence of mechanical ventilation or an intravenous central line, and the presence of specific clinical symptoms. Triggers were automatically fired by the PDMS. Chest X-rays and microbiology culture results were checked only on patient days with a positive trigger signal from the ESS. In traditional screening, no triggers were used; therefore, chest X-rays and culture results had to be screened for all patient days of all included patients. Patients with pneumonia at admission were excluded.

Results

A total of 553 patients were screened for VAP and CLABSI. The incidence of VAP was 3.3/1,000 ventilation days (13 VAP/3,927 mechanical ventilation days), and the incidence of CLABSI was 1.7/1,000 central line days (24 CLABSI/13.887 central line days). For VAP, the trigger-based screening had a sensitivity of 92.3%, a specificity of 100%, and a negative predictive value of 99.8% compared to traditional screening of all patients. For CLABSI, sensitivity was 91.3%, specificity 100%, and negative predictive value 99.6%.

Conclusions

Pre-selection of patients to be checked for signs and symptoms of VAP and CLABSI by a computer-generated automated trigger system was time saving but slightly less accurate than conventional surveillance. However, this after-the-fact surveillance was mainly designed as a quality indicator over time rather than for precise determination of infection rates. Therefore, surveillance of VAP and CLABSI with a trigger-based ESS is feasible and effective.

Development and validation of a simple and easy-to-employ electronic algorithm for identifying clinical methicillin-resistant Staphylococcus aureus infection

BACKGROUND

With growing demands to track and publicly report and compare infection rates, efforts to utilize automated surveillance systems are increasing. We developed and validated a simple algorithm for identifying patients with clinical methicillin-resistant Staphylococcus aureus (MRSA) infection using microbiologic and antimicrobial variables. We also estimated resource savings.

METHODS

Patients who had a culture positive for MRSA at any of 5 acute care Veterans Affairs hospitals were eligible. Clinical infection was defined on the basis of manual chart review. The electronic algorithm defined clinical MRSA infection as a positive non-sterile-site culture with receipt of MRSA-active antibiotics during the 5 days prior to or after the culture.

RESULTS

In total, 246 unique non-sterile-site cultures were included, of which 168 represented infection. The sensitivity (43.4%-95.8%) and specificity (34.6%-84.6%) of the electronic algorithm varied depending on the combination of antimicrobials included. On multivariable analysis, predictors of algorithm failure were outpatient status (odds ratio, 0.23 [95% confidence interval, 0.10-0.56]) and respiratory culture (odds ratio, 0.29 [95% confidence interval, 0.13-0.65]). The median cost was $2.43 per chart given 4.6 minutes of review time per chart.

CONCLUSIONS

Our simple electronic algorithm for detecting clinical MRSA infections has excellent sensitivity and good specificity. Implementation of this electronic system may streamline and standardize surveillance and reporting efforts.

Fitting outbreak models to data from many small norovirus outbreaks

Infectious disease often occurs in small, independent outbreaks in populations with varying characteristics. Each outbreak by itself may provide too little information for accurate estimation of epidemic model parameters. Here we show that using standard stochastic epidemic models for each outbreak and allowing parameters to vary between outbreaks according to a linear predictor leads to a generalized linear model that accurately estimates parameters from many small and diverse outbreaks. By estimating initial growth rates in addition to transmission rates, we are able to characterize variation in numbers of initially susceptible individuals or contact patterns between outbreaks. With simulation, we find that the estimates are fairly robust to the data being collected at discrete intervals and imputation of about half of all infectious periods. We apply the method by fitting data from 75 norovirus outbreaks in health-care settings. Our baseline regression estimates are 0.0037 transmissions per infective-susceptible day, an initial growth rate of 0.27 transmissions per infective day, and a symptomatic period of 3.35 days. Outbreaks in long-term-care facilities had significantly higher transmission and initial growth rates than outbreaks in hospitals.

Assessment of the quality of publicly reported central line-associated bloodstream infection data in Colorado, 2010

BACKGROUND

Validation of self-reported health care-associated infection data is essential to verify correct understanding of definition criteria, surveillance practices, and reporting integrity. Recent studies have found significant under-reporting of central line-associated bloodstream infections (CLABSI) leading Colorado Department of Public Health and Environment to examine the quality of Colorado's CLABSI data.

METHODS

Trained Colorado Department of Public Health and Environment staff members performed onsite validation visits that included interviews with infection preventionists to assess surveillance practices and retrospective chart reviews of patients with positive blood cultures in specific intensive care units (adult and neonatal) and long-term acute care hospitals during the first quarter of 2010.

RESULTS

Fifty-five CLABSIs from the original sample were identified; 33 (60%) in the adult intensive care unit, 7 (12.7%) in the neonatal intensive care unit, and 15 (27.3%) in the long-term acute care hospital. Of the 55 CLABSIs identified by reviewers, 18 (32.7%) were not reported by the hospitals, 37 CLABSIs (67.3%) were reported correctly into the National Healthcare Safety Network, and 1 CLABSI was over-reported.

CONCLUSIONS

There was wide variation noted in surveillance practices as well as in application of definition criteria. With 33% under-reported cases, it was concluded that ongoing validation of health care-associated infection data is necessary.

Infection preventionists' awareness of and engagement in health information exchange to improve public health surveillance

BACKGROUND

Advances in electronic health record (EHR) systems and health information exchange (HIE) are shifting efforts in public health toward greater use of information systems to automate notifiable disease surveillance. Little is known about infection preventionists' (IPs) awareness, adoption, and use of these technologies to report information to public health.

METHODS

To measure awareness and engagement in EHR and HIE activities, an online survey of IPs was conducted in states with HIE networks. A total of 63 IPs was invited to participate; 44 IPs (69%) responded. The survey asked about the adoption and use of EHR systems, participation in regional HIE initiatives, and IP needs with respect to EHR systems and public health reporting.

RESULTS

Over 70% of responding IPs reported access to an EHR system, but less than 20% of IPs with access to an EHR reported being involved in the design, selection, or implementation of the system. Just 10% of IPs reported that their organizations were formally engaged in HIE activities, and 49% were unaware of organizational involvement in HIE. IPs expressed a desire for better decision support, paperless reporting methods, and situational awareness of community outbreaks.

CONCLUSION

Many IPs lack awareness and engagement in EHR and HIE activities, which may limit IPs ability to influence or utilize key information technologies as they are implemented in health care organizations.

Assessment of the burden of mandatory reporting of health care-associated infection using the National Healthcare Safety Network in Massachusetts

An online survey was sent to 73 facilities in December 2010 to assess the time commitment, staff involvement, and methods used in reporting health care-associated infection (HAI) events through the National Healthcare Safety Network in Massachusetts. Of the 65 respondents, 45% reported electronically importing at least a portion of their data. Facilities that reported using electronic import spent fewer hours per week on data collection and entry than those performing manual data entry. Although not all facilities found electronic import easy to use, nearly all found it to be helpful. Allocating financial and information technology resources to allow for electronic import may ease the burden of HAI reporting to the National Healthcare Safety Network.

California hospitals response to state and federal policies related to health care-associated infections

In October 2008, the Centers for Medicare and Medicaid Services (CMS) denied payment for ten selected health care-associated infections (HAI). In January 2009, California enacted mandatory reporting of infection prevention processes and HAI rates. This longitudinal mixed-methods study examined the impact of federal and state policy changes on California hospitals. Data on structures, processes, and outcomes of care were collected pre- and post-policy changes. In-depth interviews with hospital personnel were performed after policy implementation. More than 200 hospitals participated with 25 personnel interviewed. We found significant increases in adoption of and adherence to evidence-based practices and decreased HAI rates (p < .05). Infection preventionists (IP) spent more time on surveillance and in their offices and less time on education and in other locations (p < .05). Qualitative data confirmed mandatory reporting had intended and unintended consequences and highlighted the importance of technology and organizational climate in preventing infections and the changing IPs' role. This is especially relevant because the California Department of Public Health has since mandated hospitals to report data on 29 different for surgical site infections and a lawsuit has been filed to delay the implementation of these requirements.

Evaluation of healthcare-associated infection surveillance in Pennsylvania hospitals

OBJECTIVE

In Pennsylvania, reporting of healthcare-associated infections (HAIs) was mandated in 2007, and hospitals were encouraged to implement qualified electronic surveillance (QES) systems to assist HAI detection. This study evaluated the usefulness of these systems in reducing HAIs.

DESIGN

Online survey and retrospective cohort study. Eligible facilities had a QES or manual system in place for the entire study period and sufficient data in selected hospital units.

METHODS

Surveys were sent to infection preventionists (IPs) in all Pennsylvania hospitals to gather qualitative information about their systems. National Healthcare Safety Network data from Pennsylvania hospitals for July 2008 through June 2010 were used to compare catheter-associated urinary tract infection (CAUTI) rates in facilities with and without a QES system.

PARTICIPANTS

IPs from 174 facilities responded to the survey. Data from 119 of 234 hospitals were analyzed.

RESULTS

IPs in facilities with a QES system reported spending as much time on data management and education as IPs in hospitals with manual surveillance. Significant interaction was observed in CAUTI rates over time between groups of facilities with and without a QES system after controlling for device-utilization ratio, location within hospital, and licensed bed size (P < .01). QES hospitals showed a significant decline in CAUTI rates (P < .01) manual surveillance facilities showed no change in rates (P > .05).

CONCLUSIONS

Over the 2-year period, a significant decline in CAUTI rates was observed in facilities with a QES system. This suggests that electronic systems may aid in reducing HAI rates. Additional data are needed to see whether these improvements and trends persist.

Hospital adoption of automated surveillance technology and the implementation of infection prevention and control programs

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 AST can achieve greater depth and breadth in implementing evidenced-based infection control practices.

Moving evidence from the literature to the bedside: report from the APIC Research Task Force

Research is an integral component of the mission of the Association for Professionals in Infection Control and Epidemiology (APIC). In January 2010, APIC 's Board of Directors decided to update and clarify the Association's approach to research. The purpose of this paper is to briefly review the history of APIC's role in research and to report on the recent vision and direction developed by a research task force regarding appropriate roles and contributions for APIC and its members in regards to research. APIC and its membership play critical roles in the research process, especially in terms of setting the research agenda so that research resources can be directed to important areas. Additionally, dissemination and implementation are areas in which APIC members can utilize their unique talents to ensure that patients receive the most up-to-date and evidence-based infection prevention practices possible.