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

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
A Scardoni ◽  
A Odone
Keyword(s):  
Artificial Intelligence ◽  
Performance Measures ◽  
Technology Implementation ◽  
Preliminary Evidence ◽  
Health Concern ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Digital Tool ◽  
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 & expensive. Artificial intelligence (AI), as a digital tool, offers great potential 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 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. Overall performance measures of AI algorithms were: sensitivity range 19%-92%, accuracy 70.2%-96.1%. Conclusions Use of AI 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. Main messages AI offer great potential to healthcare associated infections control. Preliminary evidence show AI-based models have perform better than manual or automated models for HAIs detection.

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.

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 An overview of antimicrobial resistance surveillance among healthcare-associated pathogens in South Africa

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Ashika Singh-Moodley ◽  
Husna Ismail ◽  
Olga Perovic
Keyword(s):  
Antimicrobial Resistance ◽  
Treatment Guidelines ◽  
Resistance Mechanisms ◽  
Health Concern ◽  
Infection Prevention And Control ◽  
Surveillance Systems ◽  
African Countries ◽  
Lack Of Information ◽  
Healthcare Associated ◽  
And Control

Healthcare-associated infections are a serious public health concern resulting in morbidity and mortality particularly in developing countries. The lack of information from Africa, the increasing rates of antimicrobial resistance and the emergence of new resistance mechanisms intensifies this concern warranting the need for vigorous standardised surveillance platforms that produce reliable and accurate data which can be used for addressing these concerns. The implementation of national treatment guidelines, policies, antimicrobial stewardship programmes and infection prevention and control practices within healthcare institutions require a platform from which it can draw information and direct its approach. In this review, the importance of standardised surveillance systems, the challenges faced in the application of a surveillance system and the condition (existence and nonexistence) of such systems in African countries is discussed. This review also reports on some South African data.

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.

Medical Records Quality as Prevention Tool for Healthcare-Associated Infections (HAIs) Related Litigation: a Case Series

2019 ◽  
Vol 20 (8) ◽  
pp. 653-657 ◽  
Author(s):  
Giuseppe D. Albano ◽  
Giuseppe Bertozzi ◽  
Francesca Maglietta ◽  
Angelo Montana ◽  
Giulio Di Mizio ◽  
...  
Keyword(s):  
Risk Management ◽  
Medical Care ◽  
Medical Records ◽  
Case Series ◽  
Health Concern ◽  
Care Process ◽  
Healthcare Associated Infections ◽  
Healthcare Associated

Background: Healthcare-associated infections are one of the most serious Public Health concern, as they prolong the length of hospitalization, reduce the quality of life, and increase morbidity and mortality. Despite they are not completely avoidable, the number of healthcare-associated infections related to negligence claims has risen over the last years, contributing to remarkable economic and reputation losses of Healthcare System. Methods: In this regard, several studies suggested a key role of medical records quality in determining medical care process, risk management and preventing liability. Clinical documentation should be able to demonstrate that clinicians met their duty of care and did not compromise patient’s safety. Results: Therefore, it has a key role in assessing healthcare workers’ liability in malpractice litigation. Our risk management experience has confirmed the role of medical records accuracy in preventing hospital liability and improving the quality of medical care. Conclusion: In the presented healthcare-associated infections cases, evidence-based and guidelinesbased practice, as well as a complete/incomplete medical record, have shown to significantly affect the verdict of the judicial court and inclusion/exclusion of hospital liability in healthcare-associated infections related claims.

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 Recent advances in the understanding and management of Klebsiella pneumoniae

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1760 ◽  
Author(s):  
David P. Calfee
Keyword(s):  
Klebsiella Pneumoniae ◽  
Multidrug Resistant ◽  
Health Concern ◽  
Patient Specific ◽  
Healthcare Associated Infections ◽  
Emerging Pathogens ◽  
Resistant Strains ◽  
Poor Outcomes ◽  
Optimal Approach ◽  
Healthcare Associated

Klebsiella pneumoniae, a gram-negative bacillus of the Enterobacteriaceae family, is a component of the normal human microbiota and a common cause of community- and healthcare-associated infections. The increasing prevalence of antimicrobial resistance among K. pneumoniae isolates, particularly among those causing healthcare-associated infections, is an important public health concern. Infections caused by these multidrug-resistant organisms, for which safe and effective antimicrobial therapy options are extremely limited, are associated with poor outcomes for patients. The optimal approach to the treatment of infections caused by these multidrug-resistant strains remains undefined, and treatment decisions for an individual patient should be based on a number of organism- (for example, minimum inhibitory concentration) and patient-specific (for example, site of infection) factors. The emergence of pandrug-resistant strains of K. pneumoniae highlights the critical need for consistent implementation of effective strategies for prevention of transmission and infection and for the development of new antimicrobials with activity against these emerging pathogens.

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