Increasing MRI capacity at a clinical diagnostic centre and a trauma hospital using artificial intelligence-based image reconstruction (AI-IR): a quality improvement project using the Model for Improvement framework

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.

Authors

Institutions

Publication Details

Journal
BMJ Open Quality
Published
2025-10-01
DOI
https://doi.org/10.1136/bmjoq-2025-003470
Citations
1
Primary Topic
Radiology practices and education
Type
article
Field-Weighted Citation Impact
1.36

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Increasing MRI capacity at a clinical diagnostic centre and a trauma hospital using artificial intelligence-based image reconstruction (AI-IR): a quality improvement project using the Model for Improvement framework

R. Jalan, Joe Martin, Dina Pefanis, Marc E. Miquel et al.
1 citations
BMJ Open Quality
Radiology practices and education
1.36
article

Increasing MRI capacity at a clinical diagnostic centre and a trauma hospital using artificial intelligence-based image reconstruction (AI-IR): a quality improvement project using the Model for Improvement framework

R. Jalan, Joe Martin, Dina Pefanis, Marc E. Miquel, Rebecca Jane Bennett, Nathan Proudlove, David P. Adams, Agnieszka Peplinski, Sivadas Ganeshalingham, Muaaze Ahmad, Susan Cross, Sujit Vaidya, Krishanantham Ambalawaner, Sara Moeen, Zohreh Hurcum, Francisco Pepito Ablen, Sunitha Sivarajah, Hannah Dupreez, Marianthi Vasiliki Papoutsaki
article en
1 citations

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.

BMJ Open QualityVol. 14(4)
King's College London (GB), Guy's and St Thomas' NHS Foundation Trust (GB), Barts Health NHS Trust (GB), University of Manchester (GB), King's College School (GB)
University of Manchester, Centers for Disease Control and Prevention, Alliance Manchester Business School, University of Manchester
Openalex Percentile: Top 14%
Radiology practices and education
1.36
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.