Electronic Surveillance of Surgical Site Infections

Cato, Kenrick; Liu, Jianfang; Cohen, Bevin; Larson, Elaine

doi:10.1089/sur.2016.262

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…88–91 Computer-assisted decision support can be time-consuming to implement, 72 and institutions must appropriately validate computer-assisted decision support systems after implementation to ensure that they are functioning appropriately. 92 Utilization of automated data Install information technology infrastructure to facilitate data transfer, receipt, and organization to aid with tracking of process and outcome measures. Consider use of data mining software to identify potential SSIs which can then be further evaluated. Consider leveraging existing electronic health record capabilities to provide process measure information that informs improvement approaches. …”

Section: Section 3: Background On Prevention Of Ssimentioning

confidence: 99%

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Strategies to prevent surgical site infections in acute-care hospitals: 2022 Update

Calderwood

Anderson

Bratzler

et al. 2023

Infect. Control Hosp. Epidemiol.

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and purpose The intent of this document is to highlight practical recommendations in a concise format designed to assist acute-care hospitals in implementing and prioritizing their surgical-site infection (SSI) prevention efforts. This document updates the Strategies to Prevent Surgical Site Infections in Acute Care Hospitals published in 2014.1 This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA). It is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.

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Section: Section 3: Background On Prevention Of Ssimentioning

confidence: 99%

Section: Section 3: Background On Prevention Of Ssimentioning

confidence: 99%

Strategies to prevent surgical site infections in acute-care hospitals: 2022 Update

Calderwood

Anderson

Bratzler

et al. 2023

Infect. Control Hosp. Epidemiol.

View full text Add to dashboard Cite

show abstract

“…[12][13][14] Prior attempts to create electronic SSI detection tools to augment or replace the NHSN manual review process perform poorly; however, these tools were limited to structured data fields in electronic medical records (EMRs; eg, microbiology order or diagnosis code) and missed essential information recorded only in free-text form in clinical notes. [15][16][17] A successful automated infection monitoring system may benefit from augmentation with clinical note data. Thus, we sought to develop a method to detect procedures with a high likelihood of a true HAI that integrates structured EMR data with text mining of electronic clinical notes.…”

Section: Introductionmentioning

confidence: 99%

Novel Method to Flag Cardiac Implantable Device Infections by Integrating Text Mining With Structured Data in the Veterans Health Administration’s Electronic Medical Record

et al. 2020

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Key Points Question How can coded and free-text data from electronic medical records be used to support infection tracking and other patient safety surveillance following common but understudied cardiac device procedures? Findings In this national cohort study of 19 212 patients who underwent cardiovascular implantable electronic device procedures in the US Department of Veterans Affairs health care system, an algorithm to reliably identify cases with a true 90-day infection by combining coded data (eg, diagnosis of a comorbid condition) and free-text data extracted from clinical notes (eg, documentation of an infection by a cardiologist) was developed and validated. Text note searching was a useful and straightforward adjunct to coded data for surveillance. Meaning The findings of this study suggest that the algorithm to detect patients who received cardiovascular implantable electronic device and developed an infection has the potential to significantly enhance surveillance in an underserved area.

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Development and Validation of a Semi-Automated Surveillance Algorithm for Cardiac Device Infections: Insights from the VA CART program

Asundi

Stanislawski

Mehta

et al. 2020

Sci Rep

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Procedure-related cardiac electronic implantable device (CIED) infections have high morbidity and mortality, highlighting the urgent need for infection prevention efforts to include electrophysiology procedures. We developed and validated a semi-automated algorithm based on structured electronic health records data to reliably identify CIED infections. A sample of CIED procedures entered into the Veterans’ Health Administration Clinical Assessment Reporting and Tracking program from FY 2008–2015 was reviewed for the presence of CIED infection. This sample was then randomly divided into training (2/3) validation sets (1/3). The training set was used to develop a detection algorithm containing structured variables mapped from the clinical pathways of CIED infection. Performance of this algorithm was evaluated using the validation set. 2,107 unique CIED procedures from a cohort of 5,753 underwent manual review; 97 CIED infections (4.6%) were identified. Variables strongly associated with true infections included presence of a microbiology order, billing codes for surgical site infections and post-procedural antibiotic prescriptions. The combined algorithm to detect infection demonstrated high c-statistic (0.95; 95% confidence interval: 0.92–0.98), sensitivity (87.9%) and specificity (90.3%) in the validation data. Structured variables derived from clinical pathways can guide development of a semi-automated detection tool to surveil for CIED infection.

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Electronic Surveillance of Surgical Site Infections

Cited by 9 publications

References 17 publications

Strategies to prevent surgical site infections in acute-care hospitals: 2022 Update

Strategies to prevent surgical site infections in acute-care hospitals: 2022 Update

Novel Method to Flag Cardiac Implantable Device Infections by Integrating Text Mining With Structured Data in the Veterans Health Administration’s Electronic Medical Record

Development and Validation of a Semi-Automated Surveillance Algorithm for Cardiac Device Infections: Insights from the VA CART program

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