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Minimum data set and standards in the context of nosocomial infection surveillance and antimicrobial stewardship
BioCon Valley GmbH,, , .
BioCon Valley GmbH, , .
TREAT Systems, , .
Medexter Healthcare,, , .
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2016 (English)In: Workshop at HEC (Health - Exploring Complexity: An Interdisciplinary Systems Approach), IOS Press , 2016Conference paper (Refereed)
Abstract [en]

Antimicrobial resistance (AMR), i.e., the ability of microbes such as bacteria, viruses, fungi and parasites to resist the actions of one or more antimicrobial drugs or agents, is a serious global threat. Bacterial antibiotic resistance poses the largest threat to public health. The prevention of antimicrobial infections and their spread relies heavily on infection control management, and requires urgent, coordinated action by many stakeholders. This is especially true for nosocomial infections, also known as healthcare-associated infections (HAIs), i.e., infections that are acquired in healthcare settings. It is known that continuous, systematic collection, analysis and interpretation of data relevant to nosocomial infections and feedback for the use by doctors and nurses can reduce the frequency of these infections. Data from one hospital are more valid and more effective when they are compared with those from other hospitals. In order to avoid false conclusions, comparisons are only possible when identical methods of data collection with fixed diagnostic definitions are used. The automatic aggregation of standardized data using data from electronic medical records (EMRs), lab data, surveillance data and data on antibiotic use would greatly enhance comparison and computerized decision support systems (CDSSs). Once standardized, data can be aggregated from unit to institutional, regional, national and EU level, analysed and fed back to enhance local decision support on antibiotic use and detection of nosocomial infections.

Place, publisher, year, edition, pages
IOS Press , 2016.
National Category
Information Systems
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-136595OAI: diva2:1055460
Available from: 2016-12-12 Created: 2016-12-12

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