scholarly journals Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization

2022 ◽  
Author(s):  
Janneke D.M. Verberk ◽  
Seven J.S. Aghdassi ◽  
Mohamed Abbas ◽  
Pontus Nauclér ◽  
Sophie Gubbels ◽  
...  
Keyword(s):  
Real Life ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
European Survey ◽  
Automated Surveillance ◽  
Healthcare Associated

Surveillance for Healthcare associated Infections in Indian NICUs: Researchable Issues

2009 ◽  
Vol 23 (4) ◽  
pp. 331-336
Author(s):  
Ashwani Kumar ◽  
Praveen Kumar
Keyword(s):  
Developed Countries ◽  
Control Measures ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Infection Control Measures ◽  
Infection Surveillance ◽  
Healthcare Associated ◽  
Symptoms And Signs ◽  
Local Research ◽  
Underlying Risk

Systematic surveillance is the first and integral step of all infection control measures, especially in intensive care settings. Surveillance systems started evolving in developed countries nearly 40 years ago. With experience and wisdom gained, the surveillance methods have improved and become more standardized. It is now clearly recognized that all patients are not at equal risk. For fair comparisons over time within an unit and in between units, the denominator must take the underlying risk into account. Infection surveillance in the NICU presents a number of unique challenges regarding definitions and differing symptoms and signs in the neonate. Although the importance of surveillance is being increasingly recognized in our country and the methods of developed countries are being adopted, there are numerous issues which need local research. This is in view of the limited manpower and financial resources and different profile of organisms and their epidemiology.

scholarly journals Artificial Intelligence-based tools to control healthcare associated infections: where do we stand

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
A Scardoni ◽  
F Balzarini ◽  
F Cabitza ◽  
A Odone
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Performance Measures ◽  
Performance Outcomes ◽  
Health Concern ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Public Health Action ◽  
Practice Performance ◽  
Healthcare Associated

Abstract Background Control of Healthcare associated infections (HAI) is a key public health concern in Europe. Current HAI surveillance systems are based on manual medical records review, vulnerable to misclassification and expensive. Artificial intelligence (AI) offers great potential to public health action and, specifically, to HAI control. Still, scant evidence is available on both its practice and impact. Methods As part of a broader multidisciplinary project, we conducted a systematic review to retrieve, pool and critically apprize all the available evidence on practice, performance and impact of AI-based HAI control programmes. We followed PRISMA guidelines and searched the Medline and Embase databases for relevant studies. Included studies were stratified by HAI type and outcomes of interest, including all possible performance measures, clinical, organizational and economic outcomes. Results We screened 2873 records, resulting in 27 papers included in the review. Studies were carried out in 9 countries, the majority in the US (56%), 18.5% in EU countries, 25.9% published in 2018. Two thirds of studies focused on selected types of infections. Study designs were very diverse and performance observed for HAI detection were very heterogeneous, precluding pooled calculation of summary diagnostic accuracy estimates in most instances, but generally higher than non AI-based models. The highest performance outcomes were Specificity and Negative Predictive Value. Overall performance measures of AI algorithms were: sensitivity range 19%-92%, specificity range 64%-96%, accuracy 70.2%-96.1%. Conclusions Use of AI algorithms for HAI surveillance of HAI has increased reliability compared to traditional surveillance or to automated surveillance models. With ongoing improvements in information technology, implementation of AI models will improve the quality and capacity of surveillance will support hospital HAI surveillance. Key messages Artificial Intelligence (AI) offer great potential to healthcare associated infections (HAI) control. Preliminary evidence show AI-based models have perform better than manual or automated models for HAIs detection.

Automated surveillance of healthcare-associated infections

2017 ◽  
Vol 30 (4) ◽  
pp. 425-431 ◽  
Author(s):  
Meander E. Sips ◽  
Marc J.M. Bonten ◽  
Maaike S.M. van Mourik
Keyword(s):  
Healthcare Associated Infections ◽  
Automated Surveillance ◽  
Healthcare Associated

scholarly journals Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
H Roel A Streefkerk ◽  
Roel PAJ Verkooijen ◽  
Wichor M Bramer ◽  
Henri A Verbrugh
Keyword(s):  
Systematic Review ◽  
Control Programme ◽  
Mature Stage ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Medical Specialties ◽  
Specificity And Sensitivity ◽  
Healthcare Associated ◽  
Traditional Surveillance

Background Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.

scholarly journals Data Requirements for Electronic Surveillance of Healthcare-Associated Infections

2014 ◽  
Vol 35 (9) ◽  
pp. 1083-1091 ◽  
Author(s):  
Keith F. Woeltje ◽  
Michael Y. Lin ◽  
Michael Klompas ◽  
Marc Oliver Wright ◽  
Gianna Zuccotti ◽  
...  
Keyword(s):  
White Paper ◽  
Specific Information ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Electronic Surveillance ◽  
Specific Data ◽  
Healthcare Facilities ◽  
Data Elements ◽  
Healthcare Associated ◽  
Optimal Implementation

Electronic surveillance for healthcare-associated infections (HAIs) is increasingly widespread. This is driven by multiple factors: a greater burden on hospitals to provide surveillance data to state and national agencies, financial pressures to be more efficient with HAI surveillance, the desire for more objective comparisons between healthcare facilities, and the increasing amount of patient data available electronically. Optimal implementation of electronic surveillance requires that specific information be available to the surveillance systems. This white paper reviews different approaches to electronic surveillance, discusses the specific data elements required for performing surveillance, and considers important issues of data validation.Infect Control Hosp Epidemiol 2014;35(9):1083-1091

scholarly journals Bacteria Associated with Healthcare-Associated Infections on Environmental Samples Obtained from Two Fire Departments

2021 ◽  
Vol 18 (22) ◽  
pp. 11885
Author(s):  
Kelli L. Barr ◽  
Rodney X. Sturdivant ◽  
Denise N. Williams ◽  
Debra Harris
Keyword(s):  
Fire Department ◽  
Occupational Exposures ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Fire Departments ◽  
E Coli ◽  
Medical Emergencies ◽  
Fire Stations ◽  
Healthcare Associated ◽  
In Fire

(1) Background: Firefighters spend about 64% of their time responding to medical emergencies and providing medical care without a patient history, which can render them vulnerable to healthcare-associated infections (HAI). Infection prevention, control, and surveillance systems have been instituted at hospitals. However, the prevalence of firefighters’ exposure to HAI is unknown. The objective of this study was to document evidence of HAI on surfaces in fire stations and engines to inform disinfection procedures and identify which pathogens might contribute to occupational exposures. (2) Methods: High-touch or high-use surfaces of two fire departments were sampled during five separate occasions. One fire station from one fire department was sampled over a 4-week period, whereas four fire stations were sampled from a different fire department only once. Sampled surfaces included: entryway floor, washing machine, medical bag, back seat of engine, keyboard of reporting computer, engine console, and uniform pants. (3) Results: Multiple statistical models determined that bacterial contamination was similar between the two fire departments and their stations. Keyboards were the most contaminated surface for all fire stations and departments, E. coli was the most common bacteria detected, and C. difficile was the least detected bacteria. Adjustments for rates of contamination found that contamination rates varied between fire stations. (4) Conclusions: Comprehensive environmental sampling and clinical studies are needed to better understand occupational exposures of firefighters to HAI.

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

2021 ◽  
Vol 27 ◽  
pp. S20-S28 ◽  
Author(s):  
Stephanie M. van Rooden ◽  
Olov Aspevall ◽  
Elena Carrara ◽  
Sophie Gubbels ◽  
Anders Johansson ◽  
...  
Keyword(s):  
Large Scale ◽  
Healthcare Associated Infections ◽  
Automated Surveillance ◽  
Healthcare Associated

Healthcare-Associated Infections Due to Multidrug-Resistant Organisms: a Surveillance Study on Extra Hospital Stay and Direct Costs

2019 ◽  
Vol 20 (8) ◽  
pp. 643-652 ◽  
Author(s):  
Guglielmo Giraldi ◽  
Marzietta Montesano ◽  
Christian Napoli ◽  
Paola Frati ◽  
Raffaele La Russa ◽  
...  
Keyword(s):  
Patient Safety ◽  
Antimicrobial Resistance ◽  
Hospital Stay ◽  
Multidrug Resistant ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Multidrug Resistant Organisms ◽  
Healthcare Associated ◽  
And Control ◽  
The Impact

Background: The increasing antimicrobial resistance poses a challenge to surveillance systems and raises concerns about the impact of multidrug-resistant organisms on patient safety. Objective: The study aimed to estimate extra hospital stay and economic burden of infections due to alert organisms - mostly multidrug-resistant - in a teaching hospital. Methods: The present retrospective matched cohort study was conducted based on the analysis of hospital admissions at Sant’Andrea Teaching Hospital in Rome from April to December 2015. Extra hospital stay was the difference in the length of stay between each case and control. All the patients developing an infection due to an alert organism were considered cases, all others were eligible as controls. The costs of LOS were evaluated by multiplying the extra stay with the hospital daily cost. Results: Overall, 122 patients developed an infection due to alert organisms and were all matched with controls. The attributable extra stay was of 2,291 days (mean 18.8; median 19.0) with a significantly increased hospitalization in intensive care units (21.2 days), bloodstream infections (52.5 days), and infections due to Gram-negative bacteria (mean 29.2 days; median 32.6 days). Applying the single day hospital cost, the overall additional expenditure was 11,549 euro per patient. The average additional cost of antibiotic drugs for the treatment of infections was about 1,200 euro per patient. Conclusion: The present study presents an accurate mapping of the clinical and economic impact of infections attributable to alert organisms demonstrating that infections due to multidrug-resistant organisms are associated with higher mortality, longer hospital stays, and increased costs. Article Highlights Box: The increasing antimicrobial resistance poses a challenge for surveillance systems and raises concerns about the impact of multidrug-resistant organisms on patient safety. • Healthcare-associated infections (HAIs) have historically been recognized as a significant public health problem requiring close surveillance. • Despite several and reliable findings have been achieved on clinical issues, our knowledge on the economic impact of healthcare-associated infections due to multidrug-resistant organisms needs to be widened. • Estimating the cost of infections due to multidrug-resistant organisms in terms of extra hospital stay and economic burden is complex, and the financial impact varies across different health systems. • Evaluations of social and economic implications of hospital infections play an increasingly important role in the implementation of surveillance systems. • The costs of infection prevention and control programs and dedicated personnel are relatively low and self-sustainable when efficient.

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