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Clinical Trial Summary

Antimicrobials (drugs that kill or stop the growth of microorganisms including bacteria, thereby treating infections) commonly used to treat patients with infections are becoming less effective over time as bacteria develop resistance to them. Antimicrobial usage itself can lead to development and spread of antimicrobial resistance. Antimicrobial resistance is now a major threat to patient safety. To conserve the effectiveness of antimicrobials the investigator need to develop ways to use them more sensibly healthcare professionals who diagnose and treat infections must be able to access antimicrobial guidelines and test results at the patient bedside. This needs to be provided rapidly and with support to make sure that the decisions on prescribing antimicrobials are the best that can be made.


Clinical Trial Description

Prototype software to achieve this has been developed through collaboration between healthcare professionals and biomedical engineers. This prototype software (run on a mobile device) retrieves patient results from various laboratory and clinical databases (securely within the Trust firewall) and displays this to the clinician making the prescribing decision. Furthermore a machine learning algorithm is applied to the data, and similar anonymised historical cases (and the antimicrobials prescribed and the clinical outcomes) are also displayed to the clinician to further inform their decision making. The prototype has been designed for use in intensive care, where the risk of infection is high, but through the research project detailed here, the software will be developed and validated across other areas of hospital patient care. Furthermore there is a key need to engage patients with how decisions are made around antimicrobial prescribing. The investigator propose to adapt the prototype to meet these needs. This system should improve patient safety and help preserve the effectiveness of existing antimicrobials ;


Study Design


Related Conditions & MeSH terms


NCT number NCT04013737
Study type Observational
Source Imperial College London
Contact
Status Completed
Phase
Start date February 28, 2017
Completion date August 8, 2019

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