Condition category
Circulatory System
Date applied
17/11/2017
Date assigned
08/12/2017
Last edited
05/01/2018
Prospective/Retrospective
Retrospectively registered
Overall trial status
Ongoing
Recruitment status
No longer recruiting

Plain English Summary

Background and study aims
Patient flow describes the movement of patients throughout the ward. Entering the ward, having surgery, being moved back to the CICU, and recovering over a period of around 5 to 10 days. Self-learning machines refer to machines that are capable of taking feedback into account. If a self-learning machine predicts that a patient will take five days to recover and then is informed that it was correct, it will strengthen its prediction algorithm. If it predicts a similar patient to take five days to recover but they instead take eight, it will investigate the differences between the patients more closely in order to determine the cause of its failure and change its algorithm in order to take this into effect. As this machine is trained on 35,000 patients, this will eventually lead to accurate predictions for many different types of individuals. The study aims to improve patient flow through the cardiac ICU (CICU) via analysis of patient recovery times. Self-learning machines will be developed to adjust to patients and predict accurate recovery times, allowing inefficient planning methods to be revised and fine-tuned in order to provide accurate bed, pharmaceutical (medication), and staff management.

Who can participate?
Patients in the CICU

What does the study involve?
This study uses a NHS database to access routinely collected data about those who are in the CICU and have had heart surgery. The data from 2009 until present is collected about patient flow in the CICU in order to train the self-learning machines.

What are the possible benefits and risks of participating?
There are no benefits or risks of participating

Where is the study run from?
Bristol Royal Infirmary Cardiac Intensive Care Unit (UK)

When is the study starting and how long is it expected to run for?
January 2017 to September 2020

Who is funding the study?
National Institute for Health Research (UK)

Who is the main contact?
Mr Duncan Shillan
ds17453@bristol.ac.uk

Trial website

Contact information

Type

Public

Primary contact

Mr Duncan Shillan

ORCID ID

Contact details

Bristol University
Senate House
Tyndall Avenue
Bristol
BS8 1TH
United Kingdom
+44 (0)117 928 9000
ds17453@bristol.ac.uk

Additional identifiers

EudraCT number

ClinicalTrials.gov number

Protocol/serial number

1

Study information

Scientific title

Application of machine learning to improve patient flow through the cardiac intensive care unit

Acronym

Study hypothesis

The study aims to improve patient flow through the cardiac ICU via analysis of patient recovery times. Self-learning machines will be developed to adjust to patients and predict accurate recovery times, allowing inefficient planning methods to be revised and fine-tuned in order to provide accurate bed, pharamceutical, and staff management.

Ethics approval

Not provided at time of registration

Study design

Observational cross-sectional study

Primary study design

Observational

Secondary study design

Cross sectional study

Trial setting

Hospitals

Trial type

Diagnostic

Patient information sheet

Not available in web format, please use the contact details to request a patient information sheet

Condition

Patient flow

Intervention

There are no interventional components to this study. Machine learning systems are being developed in order to show hypothetical increases in patient flow throughout the ward (meaning that beds are at near full occupancy with some left for emergencies etc).

This study uses routinely collected observational data. These patients are all from the cardiac intensive care ward and will have had heart surgery. As this study only uses routinely collected observational data, absolutely nothing happens to the patient as part of this trial. The data is collected from gaining access to an NHS database. The data from the original state of patient flow from 2009-present. Data is taken to see if machines can learn to analyse data from the Cardiac Intensive Care Unit and that could improve patient flow management.

Intervention type

Other

Phase

Drug names

Primary outcome measure

Bed occupancy in the Cardiac ICU (CICU). This is to be kept close to full, with room for emergencies, and with beds neither empty nor double-booked due to bad estimations of patient recovery times.

Secondary outcome measures

Prediction of potential complications in patients with preventative measures recommended to hospital staff.

Overall trial start date

01/01/2017

Overall trial end date

01/09/2020

Reason abandoned (if study stopped)

Eligibility

Participant inclusion criteria

Patients of the CICU

Participant type

Patient

Age group

All

Gender

Both

Target number of participants

35000, data from 2009 - present

Participant exclusion criteria

There are no participant exclusion criteria

Recruitment start date

01/08/2017

Recruitment end date

01/08/2018

Locations

Countries of recruitment

United Kingdom

Trial participating centre

Bristol Royal Infirmary Cardiac Intensive Care Unit
Bristol
BS2 8HW
United Kingdom

Sponsor information

Organisation

University of Bristol

Sponsor details

University of Bristol
Senate House
Tyndall Avenue
Bristol
BS8 1TH
United Kingdom
+44 (0)117 928 9000
ds17453@bristol.ac.uk

Sponsor type

University/education

Website

Funders

Funder type

Government

Funder name

National Institute for Health Research

Alternative name(s)

NIHR

Funding Body Type

government organisation

Funding Body Subtype

Federal/National Government

Location

United Kingdom

Results and Publications

Publication and dissemination plan

Planned publication in a high-impact peer reviewed journal around the end of 2020.

IPD sharing statement:
The datasets generated during and/or analysed during the current study are/will be available upon request from Duncan Shillan, ds17453@bristol.ac.uk.

Intention to publish date

31/12/2020

Participant level data

Available on request

Basic results (scientific)

Publication list

Publication citations

Additional files

Editorial Notes

05/01/2018: Internal review.