AI-assisted Integrated Care to Promote Colonoscopy Uptake

Official title

Artificial Intelligence-assisted Integrated Care to Promote Colonoscopy Uptake in China: a Cluster Randomized Controlled Trial

Overview

Colorectal cancer (CRC) ranks the second most common cancer and the fourth leading cause of cancer-related deaths in China. Early screening of CRC has been proven to reduce the incidence and mortality, with colonoscopy as the gold standard for CRC screening. This trial aims to evaluate the effectiveness of artificial intelligence-assistant integrated care for improving uptake rate of colonoscopy among high-risk individuals aged 40 to 64 in China. It's a two-arm, parallel cluster randomized controlled trial. The main question it aims to answer is whether the AI-assisted integrated care influence participants' screening-related knowledge, health beliefs, behavioral intention, and uptake of colonoscopy. Participants will: 1. Be recruited and allocated into one of two groups according to the assigned clusters. Participants in one group will be invited to receive usual specialty care. In addition to usual specialty care, participants in the other group will receive AI-assisted integrated care provided by specialist and general practitioners collaboratively. 2. Complete a questionnaire survey on their knowledge, health beliefs, behavioral intention on CRC screening. 3. Have their colonoscopy status checked at the middle and end of trial.

Detailed description

We will conduct a two-arm, cluster randomized controlled trial to evaluate the effectiveness of an AI-assisted integrated care (AICC) model in improving colonoscopy uptake rate among high-risk individuals aged 40-64. This will be followed by a pragmatic implementation science study to assess user engagement of AICC and identify the facilitators and barriers to its real-world implementation.

Sample size calculation, based on detecting an increase in colonoscopy uptake from 15% to 30% with 80% power (α=0.05, two-sided), an ICC of 0.05, and 10 participants per cluster, indicates a need for 18 clusters per arm. Allowing for 10% attrition, the final sample size is determined to be 20 clusters per arm. Thus, a total sample size is 400 participants from 40 clusters.

Participant recruitment will be conducted across 40 villages/communities in three representative counties/cities in China. An independent biostatistician will randomly allocate these villages/communities within each county/city to the study arms in a 1:1 ratio. The study procedure involves first identifying high-risk individuals for CRC through an initial risk assessment questionnaire and a fecal immunochemical test (FIT). Those who meet the criteria will then receive the intervention corresponding to their village's assigned study group.

Participants in the intervention group will receive AICC. This includes a colonoscopy recommendation from a county specialist for both participants and their families, followed by an introduction to and guided registration for a CRC education chatbot with an initial 5-minute tutorial. Subsequently, general practitioners will conduct three monthly face-by-face follow-ups, each comprising a brief reminder of colonoscopy and a guided usage of CRC education chatbot. The control group will receive only a colonoscopy recommendation from a county specialist, with access to the chatbot granted only after the end of the 6-month study period. Post-intervention, all participants will complete a questionnaire assessing CRC screening knowledge, health beliefs, and behavioral intention. Colonoscopy uptake will be collected via the hospital information system at the 3- and 6-month follow-up.

The primary analysis will follow the intention-to-treat (ITT) principle. The primary outcome is the uptake and timing of colonoscopy at 3 and 6 months after intervention. Secondary outcomes encompassed several domains: CRC screening knowledge, beliefs, and intention; chatbot usability and user engagement; and intervention costs. Between-group comparisons for continuous and categorical variables will utilize t-tests and chi-square tests. To account for potential confounders, the generalized estimating equation (GEE) will be employed to derive robust effect estimates. The timing of colonoscopy uptake will be analyzed using Kaplan-Meier survival curves and log-rank tests, and the intervention effects on the time-to-event will be quantified with a Cox proportional hazards model. Subgroup analyses will be conducted to elucidate the effect heterogeneity across populations stratified by baseline characteristics.

Interventions

• Behavioral AI-assisted integrated care
  A colorectal cancer screening chatbot delivered via WeChat or a web browser, designed to provide information and health education about the colonoscopy, including essential knowledge, screening rationale, methods, procedural details, and local screening policies,. The chatbot is powered by large language models and is trained on an expert-validated knowledge base derived from authoritative sources such as the China colorectal cancer screening guidelines to ensure accuracy. The knowledge base is

Primary outcome measures

• Uptake of colonoscopy [Time frame: Three and six months after recruitment]
• Time to completion of colonoscopy [Time frame: Six months after recruitment]

Secondary outcome measures (6)

• CRC screening literacy [Time frame: One month after recruitment]
• CRC screening belief [Time frame: One month after recruitment]
• Colonoscopy behavioral intention [Time frame: One month after recruitment]
• User engagement level with chatbot [Time frame: Six months after recruitment]
• Usability of AI-assisted integrated care intervention [Time frame: Six months after recruitment]
• Incremental cost-effectiveness ratio (ICER) [Time frame: Six months after recruitment]

Eligibility criteria

Inclusion criteria

• Individuals who test positive on either the Colorectal Cancer Risk Assessment Scale or the fecal immunochemical test (FIT);
• Aged 40 \~ 64 years;
• Proficient in smartphone use and able to engage with the intervention;
• Provided informed consent .

Exclusion criteria

• History of colorectal cancer;
• Contraindications to colonoscopy,(e.g. severe cardiac, cerebral, lung diseases, or renal dysfunction).

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Single blind

Primary purpose

Health services research

Study locations

Center list to be confirmed — check the primary protocol.

Publications

• Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024 Feb 2;4(1):47-53. doi: 10.1016/j.jncc.2024.01.006. eCollection 2024 Mar. PMID 39036382
• Zhang Q, Wong AKC, Bayuo J. The Role of Chatbots in Enhancing Health Care for Older Adults: A Scoping Review. J Am Med Dir Assoc. 2024 Sep;25(9):105108. doi: 10.1016/j.jamda.2024.105108. Epub 2024 Jun 22. PMID 38917965
• Zeng A, Steinke J, Bocse HF, De Pastena M. Dr. LLM Will See You Now: The Ability of ChatGPT to Provide Geographically Tailored Colorectal Cancer Screening and Surveillance Recommendations. J Clin Med. 2025 Jul 18;14(14):5101. doi: 10.3390/jcm14145101. PMID 40725794
• Kerbage A, Kassab J, El Dahdah J, Burke CA, Achkar JP, Rouphael C. Accuracy of ChatGPT in Common Gastrointestinal Diseases: Impact for Patients and Providers. Clin Gastroenterol Hepatol. 2024 Jun;22(6):1323-1325.e3. doi: 10.1016/j.cgh.2023.11.008. Epub 2023 Nov 19. PMID 37984563
• Maida M, Mori Y, Fuccio L, Sferrazza S, Vitello A, Facciorusso A, Hassan C. Exploring ChatGPT effectiveness in addressing direct patient queries on colorectal cancer screening. Endosc Int Open. 2025 May 12;13:a25689416. doi: 10.1055/a-2568-9416. eCollection 2025. PMID 40376022
• Heald B, Keel E, Marquard J, Burke CA, Kalady MF, Church JM, Liska D, Mankaney G, Hurley K, Eng C. Using chatbots to screen for heritable cancer syndromes in patients undergoing routine colonoscopy. J Med Genet. 2021 Dec;58(12):807-814. doi: 10.1136/jmedgenet-2020-107294. Epub 2020 Nov 9. PMID 33168571
• Chen D, Avison K, Alnassar S, Huang RS, Raman S. Medical accuracy of artificial intelligence chatbots in oncology: a scoping review. Oncologist. 2025 Apr 4;30(4):oyaf038. doi: 10.1093/oncolo/oyaf038. PMID 40285677
• Leung DY, Chow KM, Lo SW, So WK, Chan CW. Contributing Factors to Colorectal Cancer Screening among Chinese People: A Review of Quantitative Studies. Int J Environ Res Public Health. 2016 May 17;13(5):506. doi: 10.3390/ijerph13050506. PMID 27196920

Identifiers

NCT: NCT07261059 · Fudan-CRC chatbot

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗