Artificial Intelligence, Colonoscopy, Quality Control
Conditions
Brief summary
Quality measures in colonoscopy are important guides for improving the quality of patient care. But quality improvement intervention is not taking place, primarily because of the inconvenience and expense. To address the difficulties above, we used artificial intelligence for quality control of colonoscopy.
Interventions
OTHERquality improvement intervention using artificial intelligence
Colonoscopists received performance measure monitoring and feedback
Sponsors
Shandong University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* aged between 18 and 80; * agree to give written informed consent.
Exclusion criteria
* patients with the contraindications to colonoscopy examination; * patients with a history of inflammatory bowel disease (IBD), CRC, colorectal surgery; * patients with prior failed colonoscopy and high suspicion of polyposis syndromes, IBD and typical advanced CRC; * patients refused to participate in the trial; * the colonoscopyprocedure cannot be completed due to stenosis, obstruction, huge occupying lesions, or solid stool; * the colonoscopy procedure have to be terminated due to complications of anaesthesia.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Adenoma detection rate | 8 months | Adenoma detection rate was defined as the number of exams with findings of adenoma divided by the total number of exams. |
Countries
China
Outcome results
None listed