Cardiac intensive care: Machine learning to improve patient flow

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

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN10085738

Enrollment

35000

Registered

2017-12-08

Start date

2017-08-01

Completion date

Unknown

Last updated

2018-02-05

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Patient flow Circulatory System Patient flow

Interventions

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

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients of the CICU

Exclusion criteria

Exclusion criteria: There are no participant exclusion criteria

Design outcomes

Primary

Measure	Time frame
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

Measure	Time frame
Prediction of potential complications in patients with preventative measures recommended to hospital staff.	—

Countries

United Kingdom

Contacts

Public ContactDuncan Shillan

ds17453@bristol.ac.uk+44 (0)117 928 9000

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026