Airborne disease transmission in hospitals Infections and Infestations
Conditions
Interventions
The Air Safety programme research team has developed an Artificial Intelligence Air Safety Tool (AISaT) - a computer software that guides users on how to reduce airborne infection risks in hospitals u
Sponsors
University College London
Eligibility
Sex/Gender
All
Age
18 Years to 120 Years
Inclusion criteria
Inclusion criteria: 1. Clinicians working at relevant clinical testing environments (1st trial outpatient clinic room, 2nd trial AGP room, 3rd trial ward bay) 2. All patients (and accompanying persons) attending those relevant clinical testing environments 3. The mixed method evaluation may include people working in estates teams, hospital managers and other healthcare staff.
Exclusion criteria
Exclusion criteria: 1. Obstetric, psychiatric and paediatric clinical areas will be excluded, to minimise the risks of ethical complications involving children, pregnant women or patients with psychiatric illnesses that may have difficulty consenting 2. People under the age of 18 years old
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Number of aerosol droplets on average per minute per clinical encounter measured using an air particle counter (APC) installed next to the clinician and at up to 4 other fixed locations in the room or ward bay at each of the sessions over the 3-week intervention period | — |
Secondary
| Measure | Time frame |
|---|---|
| 1. Implementation processes are measured using the Normalisation Measure Development Questionnaire (NoMAD), collected at one time point alongside the clinical trial 2. Hospital staff views on the importance of AISaT, usability and management of the AISaT software, challenges to fidelity in implementation, and how team members can work together effectively to support implementation are measured using ethnographic observation and formal and informal interviews. These will take place across each site, in both pilot and main trial phases, and in each of the three trial settings at one time point alongside the clinical trial 3. Cost efficiency of AISaT is measured using resource use collected as an outcome, to which cost estimates are applied. The resource use measures are defined in the protocol and include inputs including size of the room, number of beds/patients seen, training, clinical, engineering and other staff and costs of the AI system; as well as outputs with regard to droplets removed. The unit of analysis is the service, not the patient, and data collection occurs during each session over the 3-week intervention period, consistent with the timepoints used for droplet measurement. | — |
Countries
England, United Kingdom
Contacts
Public ContactLaurence Lovat
Outcome results
None listed