Abstract
Increasing MRI capacity is of primary importance to both NHS England and individual radiology departments. Consequently, central funding was provided to allow trusts to instal artificial intelligence-enabled image reconstruction (AI-IR) on their MRI scanners, with the stated aim of increasing capacity by two patients scanned per day within a year of installation on a given scanner. This work demonstrates how a two-phase quality improvement (QI) initiative can be followed to increase capacity using AI-IR in a community diagnostic centre (CDC) at Mile End Hospital and an acute trauma centre, the Royal London Hospital, in East London with comprehensive stakeholders' engagement.The Model for Improvement framework was used. Our pilot study focused on 3 Plan-Do-Study-Act (PDSA) cycles for three anatomies in musculoskeletal (MSK) imaging at our CDC. A second, substantive study at our major trauma centre was followed, which was a 20-month project encompassing all MSK anatomies of interest.In our initial pilot study at the CDC, we were able to reduce booking times by 10 min for Knee, Ankle and Spine protocols. In our wide-ranging MSK programme at our trauma centre, we saved on average of 07:26 min per scan and while an increased throughput was not achieved, an increase in complex patients being scanned, from 7% to 15% was achieved, reducing healthcare inequities.Our two-centre study suggests that engaging with stakeholders in a structured QI programme can significantly reduce scanning times, improve patient experience and allow for longer precare and postcare time. Additionally, significant throughput increase at the CDC for low-risk ambulatory patients suggests efforts to increase capacity using this technology should be focused at such centres and other scanners focused on ambulatory outpatients, while for scanners focused on inpatients, paediatrics and A&E at trauma centres, the time saved can be used to increase the capacity for complex patients, reducing waiting times for these patients.