An evaluation of surveillance methods for detecting infections in hospital inpatients

Glenister, Helen; Taylor, Lynda; Bartlett, Chris; Cooke, E. M.; Sedgwick, J.; Mackintosh, C.A.

doi:10.1016/0195-6701(93)90028-x

Cited by 83 publications

(41 citation statements)

References 19 publications

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“…These methods met most of our objectives for enhancing infection control activity by identifying trends in a timely manner, while demonstrating reasonable sensitivity and specificity. The results are comparable to the These surveillance approaches required only one-sixth to onethird the amount of time as their standard surveillance method (8).…”

Section: Discussionsupporting

confidence: 70%

“…In addition, healthcare organizations are faced with controlling the spread of emerging and reemerging pathogens, especially drug-resistant bacteria that are the cause of many nosocomial infections. Many years ago it was recognized that the microbiology laboratory can help in this effort by monitoring its extensive data generated from culture results for patterns of infection (4,8,11,20). The laboratory holds the earliest opportunity to detect specific organism or antimicrobial resistance patterns emerging in the hospital.…”

mentioning

confidence: 99%

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Computer-Assisted Surveillance for Detecting Clonal Outbreaks of Nosocomial Infection

et al. 2004

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Whole-house surveillance for healthcare-associated infection is no longer the recommended practice because of the large personnel time investment required. We developed a computer-based tracking system using microbiologic data as an aid in detecting potential outbreaks of healthcare-associated infections on a hospitalwide basis. Monthly total isolates of 25 clinically significant hospital pathogens were tallied from 1991 to 1998 to form a database for future comparison. Two different algorithm tools (based on increases of organism numbers over baseline) were applied to determine alert thresholds for suspected outbreaks using this information. The first algorithm (2SD) defined an alert as two standard deviations above the mean monthly number of isolates. The second (MI) defined an alert as either a 100% increase from the baseline organism number over 2 months or a >50% increase (compared to baseline) during a three-consecutive-month period. These two methods were compared to standard infection control professional surveillance (ICP) for the detection of clonal outbreaks over 12 months. Overall, a total of seven clonal outbreaks were detected during the 1-year study. Using standard methods, ICP investigated nine suspected outbreaks, four of which were associated with clonal microbes. The 2SD method signaled a suspected outbreak 15 times, of which three were clonal and ICP had detected one. The MI method signaled a suspected outbreak 30 times; four of these were clonal, and ICP had detected one. The sensitivity and specificity values for ICP, 2SD, and MI for detecting clonal outbreaks were 57, 43, and 57% and 17, 83, and 67%, respectively. Statistical methods applied to clinical microbiology laboratory information system data efficiently supplement infection control efforts for outbreak detection.

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Section: Discussionsupporting

confidence: 70%

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confidence: 99%

Computer-Assisted Surveillance for Detecting Clonal Outbreaks of Nosocomial Infection

et al. 2004

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“…Effort is needed to not only assess newly positive laboratory results but to also track prior history of MDRO carriage and recent C difficile infection that may still be associated with diarrhea. 4 Furthermore, infection prevention programs must ensure that MDRO carriers receive single rooms, if available, and appropriate precautions on admission. Finally, infection prevention programs often track and promote the removal of precautions when appropriate criteria are met.…”

mentioning

confidence: 99%

Automated tracking and ordering of precautions for multidrug-resistant organisms

Quan¹,

Cousins²,

Porter³

et al. 2015

American Journal of Infection Control

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Background:The transmission and infection risk associated with multidrug-resistant organism (MDRO) carriers necessitates surveillance and tracking to provide proper contact precautions. As MDROs increase in scope, automated electronic health record (EHR) systems may help with surveillance demands.Methods: We created a system for MDROs and Clostridium difficile tracking that automated the following 3 main surveillance and tracking activities: monitoring of microbiology results and initiation of chart-based flags, ordering of contact precautions on admission, and ensuring appropriate removal of precautions.Results: Automation saved 43 infection preventionist hours per 1,000 admissions, in addition to previously unquantified hours spent reviewing MDRO history for every admission. Automatic retiring of certain MDRO flags ensured removal of contact precautions after a specified time. A point-prevalence assessment for eligibility for discontinuation found that all precautions were appropriate, with none eligible for removal. By integrating microbiology data, EHR tracking flags, and automated orders, this system assured rapid and comprehensive placement of patients into contact precautions without requiring oversight by infection prevention personnel. Conclusion:We show that automated systems embedded within EHRs can ensure tracking and application of appropriate contact precautions while simultaneously producing tremendous time savings for infection prevention programs.

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“…Glenister et al (1993) compared 8 surveillance methods and found that the most effective method, a combination of several indirect methods, took 6.4 hours for 100 beds a week. The higher the costs of registration, the fewer hospital administrations and surgical departments are willing to support it.…”

mentioning

confidence: 99%

Guest editorial – Surveillance of surgical–site infections in orthopedics

Walenkamp

2003

Acta Orthopaedica Scandinavica

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An evaluation of surveillance methods for detecting infections in hospital inpatients

Cited by 83 publications

References 19 publications

Computer-Assisted Surveillance for Detecting Clonal Outbreaks of Nosocomial Infection

Computer-Assisted Surveillance for Detecting Clonal Outbreaks of Nosocomial Infection

Automated tracking and ordering of precautions for multidrug-resistant organisms

Guest editorial – Surveillance of surgical–site infections in orthopedics

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