Beyond the abacus: Leveraging the electronic medical record for central line day surveillance

Controlling the chaos: Information management in home-infusion central-line-associated bloodstream infection (CLABSI) surveillance

Objectives

Access to patient information may affect how home-infusion surveillance staff identify central-line-associated bloodstream infections (CLABSIs). We characterized information hazards in home-infusion CLABSI surveillance and identified possible strategies to mitigate information hazards.

Design

Qualitative study using semistructured interviews.

Setting and participants

The study included 21 clinical staff members involved in CLABSI surveillance at 5 large home-infusion agencies covering 13 states and the District of Columbia. Methods: Interviews were conducted by 1 researcher. Transcripts were coded by 2 researchers; consensus was reached by discussion.

Results

Data revealed the following barriers: information overload, information underload, information scatter, information conflict, and erroneous information. Respondents identified 5 strategies to mitigate information chaos: (1) engage information technology in developing reports; (2) develop streamlined processes for acquiring and sharing data among staff; (3) enable staff access to hospital electronic health records; (4) use a single, validated, home-infusion CLABSI surveillance definition; and (5) develop relationships between home-infusion surveillance staff and inpatient healthcare workers.

Conclusions

Information chaos occurs in home-infusion CLABSI surveillance and may affect the development of accurate CLABSI rates in home-infusion therapy. Implementing strategies to minimize information chaos will enhance intra- and interteam collaborations in addition to improving patient-related outcomes.

Automating surveillance for healthcare-associated infections: Rationale and current realities (Part I/III)

Infection surveillance is one of the cornerstones of infection prevention and control. Measurement of process metrics and clinical outcomes, such as detection of healthcare-associated infections (HAIs), can be used to support continuous quality improvement. HAI metrics are reported as part of the CMS Hospital-Acquired Conditions Program, and they influence facility reputation and financial outcomes.

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.

Development and Evaluation of a Fully Automated Surveillance System for Influenza-Associated Hospitalization at a Multihospital Health System in Northeast Ohio

BACKGROUND

Performing high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential.

OBJECTIVES

Our objectives were to develop a fully automated surveillance system for laboratory-confirmed IAH at our multihospital health system, to evaluate the performance of the automated system during the 2018 to 2019 influenza season at eight hospitals by comparing its sensitivity and positive predictive value to that of manual surveillance, and to estimate the time and cost savings associated with reliance on the automated surveillance system.

METHODS

Infection preventionists (IPs) perform manual surveillance for IAH by reviewing laboratory records and making a determination about each result. For automated surveillance, we programmed a query against our Enterprise Data Vault (EDV) for cases of IAH. The EDV query was established as a dynamic data source to feed our data visualization software, automatically updating every 24 hours.To establish a gold standard of cases of IAH against which to evaluate the performance of manual and automated surveillance systems, we generated a master list of possible IAH by querying four independent information systems. We reviewed medical records and adjudicated whether each possible case represented a true case of IAH.

RESULTS

We found 844 true cases of IAH, 577 (68.4%) of which were detected by the manual system and 774 (91.7%) of which were detected by the automated system. The positive predictive values of the manual and automated systems were 89.3 and 88.3%, respectively.Relying on the automated surveillance system for IAH resulted in an average recoup of 82 minutes per day for each IP and an estimated system-wide payroll redirection of $32,880 over the four heaviest weeks of influenza activity.

CONCLUSION

Surveillance for IAH can be entirely automated at multihospital health systems, saving time, and money while improving case detection.