WAiRD: Would Artificial Intelligence Reduce Delays to Nurse Response Times
Study Details
Study Description
Brief Summary
Patients are admitted to wards at all times of day and night and in various states of ill health. As this research is non interventional and does not impact on patient safety, we reviewed the guidance from the ethics committee and agreed that it would be appropriate to enrol every admission into the 2 bed bays and gain consent within 24 hours of admission. All data collected within the trial using the smart tablets will be associated to a study number, no patient details will be stored on the smart tablet and therefore the cloud data store. We have discussed with the trust information governance and this complies with their regulations.
Any patient identifiable data will be kept by the research team. All data will be archived and stored as per the Sponsors policy. The novel nurse call system has been designed to be user friendly to all patients regardless of age, learning ability and first language used. By using colours, images and words we hope this will be accessible to all. The nursing staff on the ward advised on the main reasons for the nurse call system activation and therefore the icons used in the novel system were adapted from this. This trial was discussed in the patient and public involvement group. As this is a pilot trial, any adaptions that need to be made will be made before the large scale trial.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Bed bay A Time from nurse call system activated by the novel nurse call system to initial response time T1 and time to complete task T2. Reason for nurse call system activated: Toilet/Pain/Medication/ /Need a nurse/Other |
Device: novel nurse call system
Inavya Ventures Ltd (Inavya) has developed a medical-grade artificial intelligence enabled mobile system (AVATR) to support out-of-hospital healthcare. AVATR is approved as a UK Government official supplier of healthcare technology on the UK Digital Marketplace for cloud-based solutions (G Cloud). AVATR in hospital, will connect to the existing AVATR outpatient service building on existing AVATR technology, which is regulated CE-mark Grade 1 (EU/UK). The research team will create, deploy and test a novel ward-based AI technology innovation to transform current nurse call systems to patient-centred mobile technology assets at bedside, thus reducing the need for unproductive visits by nurses to the bedside, which takes away time and attention where it is otherwise best served.
Having mobile connection to the patients, would improve patient experience and saving nursing staff time, thereby improving quality of care, and saving money.
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| Bed bay B Time from nurse call system activated by standard system to initial response timeT1 and time to complete task T2. |
Outcome Measures
Primary Outcome Measures
- study objective [1 year]
The primary outcome of this study is the time taken to respond to the alert raised by the novel nurse call system and time taken from call to completion of task.
Secondary Outcome Measures
- Nursing time saved [1 year]
The time taken using the novel system will be measured against the regular method to find the difference in time.
- patient acceptability of the novel system [1 year]
this is a qualitative measure
Eligibility Criteria
Criteria
Inclusion Criteria:
All adults 18 years who are admitted to the cardiology admissions ward will be eligible to take part.
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Exclusion Criteria: Any patient who does not wish to participate will have their anonymised data removed from the trial.
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Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Leeds Teaching Hospital NHS Trust | Leeds | United Kingdom | LS9 7TF |
Sponsors and Collaborators
- The Leeds Teaching Hospitals NHS Trust
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- CD22/153868