Real-time data analytics to improve mechanical ventilator wean – guiding clinical behaviour (ATTITUDE study) Queen’s University of Belfast

Patients in intensive care units (ICU) are the most vulnerable of hospital patients and need close monitoring and delicate administration of complex therapy. Critical care therapies such as mechanical ventilation can save lives, but inappropriate use is associated with patient harm. Ventilator-induced injury, excessive fluid administration, uncontrolled oxygen therapy and inappropriate sedation can all worsen outcomes.

This project aims to use behavioural insights methods to find out whether real-time data analytics and use of a clinical decision support tool can improve the quality of key critical care practices in an adult ICU.

It will begin with a review of the existing evidence on the perceptions of clinical decision support aids in health care. The team will then survey doctors and nurses at Royal Victoria Hospital to find out what barriers there are for weaning, oxygen administration, sedation and fluid balance. They will also observe current practice.

A behavioural intervention in the form of a clinical decision support tool, driven by real-time data analytics, will then be developed to ‘nudge’ practice, based on identified barriers. There will be other supportive interventions such as educational support if knowledge gap is identified as a barrier.

The overall aim is to achieve a significant reduction in the duration of weaning. Improvement in care for patients on mechanical ventilators would lead to improved patient outcomes, including mortality, and reduced health care costs by a reduction in intensive care unit and hospital stay.

Contact details

• Dr Murali Shyamsundar, NIHR Clinician Scientist/Clinical Senior Lecturer/Consultant in Intensive Care Medicine, Queen's University, Belfast/ Royal Hospitals, Murali.Shyamsundar@qub.ac.uk
• Darshan Patel, Senior Research Manager, Health Foundation, darshan.patel@health.org.uk