Infection Control Through Clinical Pipelines Built with Arden Syntax MLM Building Blocks
Leonhard Hauptfeld1, Andrea Rappelsberger2, Klaus-Peter Adlassnig2,1
1Medexter Healthcare GmbH, Borschkegasse 7/5, 1090 Vienna, Austria.
Abstract:
Healthcare-associated infections (HAIs) may have grave consequences for patients. In the case of sepsis, the 30-day mortality rate is about 25%. HAIs cost EU member states an estimated 7 billion Euros annually. Clinical decision support tools may be useful for infection monitoring, early warning, and alerts. MONI, a tool for monitoring nosocomial infections, is used at University Hospital Vienna, but needs to be clinically and technically revised and updated. A new, completely configurable pipeline-based system for defining and processing HAI definitions was developed and validated. A network of data access points, clinical rules, and explanatory output is arranged as an inference network, a clinical pipeline as it is called, and processed in a stepwise manner. Arden-Syntax-based medical logic modules were used to implement the respective rules. The system was validated by creating a pipeline for the ECDC PN5 pneumonia rule. It was tested on a set of patient data from intensive care medicine. The results were compared with previously obtained MONI output as a suitable reference, yielding a sensitivity of 93.8% and a specificity of 99.8%. Clinical pipelines show promise as an open and configurable approach to graphically-based, human-readable, machine-executable HAI definitions.
Insights
A new clinical pipeline system improves healthcare-associated infection (HAI) detection. This configurable tool offers a human-readable, machine-executable approach for infection monitoring and alerts.
Area of Science:
- Medical Informatics
- Infectious Disease Epidemiology
- Clinical Decision Support
Background:
- Healthcare-associated infections (HAIs) pose significant risks, including a 25% 30-day mortality rate for sepsis.
- HAIs incur substantial economic costs, estimated at €7 billion annually for EU member states.
- Existing tools like MONI require clinical and technical updates for effective infection monitoring.
Purpose of the Study:
- To develop and validate a novel, configurable pipeline-based system for defining and processing HAI definitions.
- To enhance clinical decision support for infection monitoring, early warning, and alerts.
- To provide a flexible and open approach for HAI definition management.
Main Methods:
- Developed a configurable pipeline-based system using a network of data access points and clinical rules.
- Implemented rules using Arden Syntax-based medical logic modules within a clinical pipeline framework.
- Validated the system by creating a pipeline for the ECDC PN5 pneumonia rule and testing on intensive care patient data.
Main Results:
- The new system achieved high performance in detecting pneumonia, with a sensitivity of 93.8% and a specificity of 99.8%.
- Results were validated against the established MONI system, demonstrating comparable or improved accuracy.
- The clinical pipeline approach proved effective for processing HAI definitions.
Conclusions:
- Clinical pipelines offer a promising, open, and configurable method for HAI definitions.
- This approach facilitates human-readable and machine-executable HAI definitions.
- The validated system demonstrates potential for improving HAI surveillance and patient safety.
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