- Automatic Results
- Monthly incidence density of resistant/intermediate strains for all microorganisms against all tested antibiotics
- Monthly percentage of resistant/intermediate strains for all microorganisms against all tested antibiotics
- Monthly sum of DDD/1000 AOBD for all antibiotics
- Monthly sum of DDD/1000 AOBD for all antibiotics groups
- Monthly series of alcohol based solution per 1000 AOBD
- Monthly series of gloves per 1000 AOBD
- Monthly series of length of stay
- Monthly series of age average of inpatients
- Using appropriate algorithms, the system will identify MDR, XDR and PDR isolates.
- Not automatic results
- Access to results
- Local Level
- Dynamic Empirical Guideline (DEG)
- We recommend that the Infection Commission encourages all clinicians to use the DEG.
- It is necessary to facilitate the access to this DEG, installing links wherever its access is the most easy as possible: prescription forms, electronic clinical records, etc. Obviously the best way to offer the DEG is via the Electronic Microbiologic Form if available.
- Table of most likely microorganisms per Clinical Situation (or per sample type).
- Table of expected resistance for every interesting microorganisms against tested antibiotics.
- Evolution of resistance (incidence density and monthly percentage).
- Evolution of antimicrobial use.
- Evolution of hygiene data.
- Joint evolution of all series.
- Incidence of MDR, XDR and PDR isolates.
- Central Level (CL)
- To encrypt the access to its data. In this case other interested hospital must request access to the owner of data.
- To open the access only to participant hospitals
- To offer a public access to their data.
Per service types and per overall hospital:
The system will automatically update a table with expected resistance for current quarter for every interesting microorganism and, if EMF exists, the most likely microorganism for each CS. With this information the Infection Committee will be able to elaborate the local Dynamic Empirical Guideline.
The econometrician central services will progressively analyze several TSA models looking for the calculation of the impact of antimicrobial use on resistance. These models, if successful, will calculate the impact of specific antimicrobial use on specific resistance phenomena: how much the current resistance level will increase by each new patient treated with this antibiotic. This is a crucial data for the local antibiotic policy. When clinicians choose an antibiotic to treat his patient, it is not only necessary to consider the likelihood of resistance but also the impact of their clinical decision on the local ecosystem. Obviously, if this information is available, the Infection Committee will be reinforced when elaborating guidelines and will be able to better decide the general antibiotic policy of the hospital.
A local web application will be installed in the participant hospital.
This web application will only be accessible into the intranet.
A mobile application (Android, iPhone and iPad) will be created to access to some results (Dynamic Empirical Guideline).
The access to results will depend of the decision of the local committee. In general we recommend to be available for all health professional working in the hospital. The mobile application will be accessible by mean of users and passwords provided by a special application that will recognise the user when he access via the intranet.
The Central Econometrics Service will help local participants to the evaluation of the impact of their intervention programs using the most appropriate methodology.
The CL will offer, via the website, only access to aggregated data: time series.
The CL, if requested, will encrypt the identity of hospitals using some codes known only by each hospital. Each hospital will decide between these possibilities:
Only a general demo will be freely shown.
When a participant centre is interested in data of other centres, in order to build scientific works, it is mandatory to request the permission of the owner of data.
Encrypting hospital identities the CS will offers the possibility to easily compare data between participant hospitals.