Quality Improvement Intervention in Colonoscopy Using Artificial Intelligence

Shandong University676 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

DOUBLE

Enrollment

676

Conditions

Quality Control Artificial Intelligence Colonoscopy

Interventions

quality improvement intervention using artificial intelligenceOTHER

Colonoscopists received performance measure monitoring and feedback

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (1)

Department of Gastroenterology, Qilu Hospital, Shandong University

Jinan, Shandong, China

Outcomes

Primary Outcomes

Adenoma detection rate

Adenoma detection rate was defined as the number of exams with findings of adenoma divided by the total number of exams.

Time frame: 8 months

Data from ClinicalTrials.gov

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