Efficiency in colonoscopy
File(s)
Author(s)
Ahmad, Ahmir
Type
Thesis
Abstract
Global trends, including demographic changes, are significantly increasing the demand and cost of healthcare. Endoscopy services are no exception and, even before the Covid-19 pandemic, significant pressure resulted in many units failing to meet cancer wait targets. The need to improve efficiency has never been greater and particularly so for colonoscopy which significantly reduces morbidity and mortality from colorectal cancer. Today, advances in colonoscope technologies and emergence of artificial intelligence offer the potential for improved colonoscopy practice. The aim of this thesis is to explore how efficiency in colonoscopy can be enhanced throughout the patient pathway.
Five major studies were performed evaluating bowel preparation (CLEANSE), polyp detection (AI-DETECT), optical diagnosis (DISCARD3), insertion technique (WAVE) and post-colonoscopy colorectal cancer (AI-DETECT).
CLEANSE is an evaluation of a novel low-volume same-day bowel preparation regime (Plenvu) and showed this offers a more efficient bowel cleansing option than standard regimens. AI-DETECT is a randomised study evaluating a computer-aided detection (CADe) system (GI Genius) and showed a borderline significant improvement in polyp detection is achieved amongst high performing endoscopists. DISCARD3 is a major evaluation of optical diagnosis with a “resect and discard” strategy exploring the learning curve, quality assurance process, causes of error and economic impact. This study shows such a strategy is feasible and safe and could potentially be implemented with a quality assurance process in place within the English Bowel Cancer Screening Progamme (BCSP). WAVE is a randomised study evaluating colonoscopy insertion technique. This showed a ‘hybrid’ insertion technique is more efficient than a water-exchange colonoscopy technique. REFLECT is a retrospective evaluation of post-colonoscopy colorectal cancer cases identified at national level and showed after local root cause analysis a significant proportion were in fact detected cancers.
These studies provide valuable insights that we hope will ultimately lead to more efficient colonoscopy whilst maintaining quality and enhancing patient care.
Five major studies were performed evaluating bowel preparation (CLEANSE), polyp detection (AI-DETECT), optical diagnosis (DISCARD3), insertion technique (WAVE) and post-colonoscopy colorectal cancer (AI-DETECT).
CLEANSE is an evaluation of a novel low-volume same-day bowel preparation regime (Plenvu) and showed this offers a more efficient bowel cleansing option than standard regimens. AI-DETECT is a randomised study evaluating a computer-aided detection (CADe) system (GI Genius) and showed a borderline significant improvement in polyp detection is achieved amongst high performing endoscopists. DISCARD3 is a major evaluation of optical diagnosis with a “resect and discard” strategy exploring the learning curve, quality assurance process, causes of error and economic impact. This study shows such a strategy is feasible and safe and could potentially be implemented with a quality assurance process in place within the English Bowel Cancer Screening Progamme (BCSP). WAVE is a randomised study evaluating colonoscopy insertion technique. This showed a ‘hybrid’ insertion technique is more efficient than a water-exchange colonoscopy technique. REFLECT is a retrospective evaluation of post-colonoscopy colorectal cancer cases identified at national level and showed after local root cause analysis a significant proportion were in fact detected cancers.
These studies provide valuable insights that we hope will ultimately lead to more efficient colonoscopy whilst maintaining quality and enhancing patient care.
Version
Open Access
Date Issued
2022-11
Date Awarded
2023-08
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Saunders, Brian
Wilson, Ana
Publisher Department
Department of Surgery & Cancer
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)