Prospective Single Blinded Randomized Control Trial on the Effectiveness of Using Large Language Model Artificial Intelligence Chatbot to Improve Boston Bowel Preparation Score (BBPS) for Colonoscopy Preparation
Phase: NA
Conditions: Colorectal Cancer Screening, Colon Polyps and Adenomas, Bowel Preparation Quality, Artificial Intelligence (AI)
Sponsor: National University of Malaysia

 
 
 
 
TITLE  
PROPOSAL OF PROSPECTIVE SINGLE BLINDED RANDOMIZED CONTROL TRIAL 
ON THE EFFECTIVENESS OF USING LARGE LANGUAGE MODEL ARTIFICIAL 
INTELLIGENCE (AI) CHATBOT TO IMPROVE BOSTON BOWEL PREPARATION 
SCORES (BPPS) FOR COLONOSCOPY PREPARATION  
 
NCT ID: Not available yet 
 
Version 2.0: Date of revised 24th March 2025 
 
 

 
1 
Literature review  
 
Paragraph 
Content 
1 
Colonoscopy and importance of bowel preparation 
2-3 
Challenges in patient preparation 
4-5 
Application of AI chatbot in healthcare 
6-9 
Effectiveness of AI chatbot in patient preparation 
10-11 
Clinical outcome 
12 
Challenges and limitations 
13-14 
Comparison with traditional methods 
15 
Future directions and research gaps  
16 
Conclusions 
 
 
    Colonoscopy is a vital diagnostic tool for colorectal cancer and adenomas, significantly reducing 
morbidity and mortality through early detection (1). However, its effectiveness relies heavily on 
high-quality bowel preparation, which impacts adenoma detection rates (ADR) and procedural 
success. Inadequate preparation decreases colonoscopy efficiency due to the necessity for repeat 
procedures, generating increased expenses (2).  Furthermore, it engenders delays in diagnosing 
malignant or precancerous lesions, curtails the adenoma detection rate (ADR), and increases 
procedural times and possibly patient risk. However, the prevalence of suboptimal colonoscopies 
across endoscopy units evinces considerable variability in studies, spanning from 6.8% to 33%. 
(3) A multitude of factors have been linked to inadequate bowel preparation, prompting endeavors 
to mitigate suboptimal bowel cleansing through interventional studies aimed at high-risk patients 
with poor bowel cleansing 
 
    Patient compliance with these pre-procedural instructions is crucial for achieving high-quality 
results and reducing the likelihood of complications or the need for repeat procedures. Despite the 
clear importance of bowel preparation, ensuring patient adherence to pre-procedural instructions 
remains a significant challenge. Several barriers to effective bowel preparation exist, including 
non-compliance, lack of understanding of instructions, and anxiety regarding the procedure. 
Sociodemographic factors, such as age, education level have been identified as predictors of 
inadequate preparation (4).  
 

 
2 
    Confronted with this challenge, the incorporation of Artificial Intelligence (AI) technology 
presents an innovative approach to bowel preparation scoring. Particularly noteworthy are the 
significant achievements of Convolutional Neural Networks (CNN) in medical image analysis, 
exemplified by the AI system ENDOANGEL, which has brought a higher level of accuracy and 
consistency to bowel preparation scoring (5). These advancements suggest that deep learning and 
AI could assist clinicians in improving the accuracy and consistency of bowel preparation scoring. 
In the past few years, CNNs have advanced in bowel preparation assessment. However, they fall 
short in clinical usability and human-centered communication. In other words, most deep learning 
models have this hard boundary in a discriminative way instead of parametrically assigning 
probabilities to work together with clinicians. Conversely, ChatGPT, a large language processing 
model, has shown proficiency in processing medical texts and enhancing doctor-patient 
communication, exemplified by its ability to provide contextually informed suggestions on 
colonoscopy follow-up timings (6). Furthermore, ChatGPT 4.0 has shown potential in image 
recognition, enhancing its multimodal analysis capabilities. 
 
    Artificial intelligence has become a transformative force in healthcare, with applications ranging 
from clinical decision support systems to patient management tools. Among these innovations, AI 
chatbots stand out due to their ability to simulate human conversation, provide tailored 
information, and engage with patients interactively. Use of AI chatbots can address current health 
care challenges, such as shortages of healthcare providers, which reduce the availability and 
accessibility of health care services (7-9). AI chatbots use artificial intelligence (AI), including 
machine learning (a statistical means of training models with data so that they can make predictions 
based on a variety of features) and natural language processing (NLP; the ability to recognize and 
analyze verbal and written language) to interact with humans via speech, text, or other inputs and 
outputs on mobile, web-based, or audio-based platforms (10,11). Many of these agents are 
designed to use NLP so that users can speak or write to the agent as they would to a human. The 
agent can then analyze the input and respond appropriately in a conversational manner.  
 
    AI chatbots have been developed for many different aspects of the health sector to support health 
care professionals and the public. Specific uses include screening for health conditions, triage, 
counseling, at-home health management support, and training for healthcare professionals (12). 
With phone, mobile, and online platforms being widely accessible, conversational agents can 
support populations with limited access to health care or poor health literacy (13,14). They also 
have the potential to be affordably scaled up to reach large proportions of a population (15). Due 
to this accessibility, conversational agents are also a promising tool for the advancement of patient-
centered care and can support users’ involvement in the management of their own health (14,16). 
Personalize features have the potential to further improve usability and satisfaction, although more 
research is needed to evaluate their effectiveness in achieving their stated health outcomes and 
reducing costs and to ensure that there are no negative consequences for decision making or 
privacy. These systems use NLP to understand and respond to patients' questions in a 

 
3 
conversational manner, thereby overcoming one of the most significant barriers to effective 
communication: the complexity of medical information. In the context of colonoscopy preparation, 
AI chatbots can be designed to provide specific instructions on dietary restrictions, bowel 
cleansing, and medications, ensuring that the patient receives accurate, easy-to-understand 
guidance.  
 
    Based on research, The Effectiveness of Artificial Intelligence Conversational Agents in Health 
Care: Systematic Review, conducted by Madison Milne-Ivs in 2020 (17), 31 studies have 
evaluated the use of AI chatbots for educational purposes in healthcare. The study designs varied 
widely, with 29% (9/31) using cross-sectional designs, 26% (8/31) using RCTs, 23% (7/31) using 
qualitative methods, 19% (6/31) using cohort studies, and 1 using a cluster crossover design.  
 
    There were a wide variety of areas of health care targeted by the conversational agents of the 
included studies (17). The largest proportion of them (12/31, 39%) addressed mental health issues 
[13,32-42], with 19% (6/31) providing some form of clinical decision or triage support 
[8,12,40,42-44] and treatment support (including encouraging users to get screened) , 10% (3/31) 
being used to support training of health care students and the screening or diagnosis of users 
[14,38,51], 7% (2/31) targeting physical health and layperson medical education; 1 agent was 
designed to help monitor users’ speech.  
 
    Overall, about three-quarters of the studies (22/30, 73%) reported positive or mixed results for 
most of the outcomes. Perceived ease of use or usefulness (27/30, 90%), the process service 
delivery or performance (26/30, 87%), appropriateness (24/30, 80%), and satisfaction (26/31, 
84%) were the outcomes that had the most support from the studies. Just over three-quarters 
(23/30, 77%) of the studies also reported positive or mixed evidence of effectiveness. However, 
very few studies discussed the cost-effectiveness (5/30, 17%, coded as positive or mixed) or safety, 
privacy, and security (14/30, 47%, coded as positive or mixed) outcomes for the agents being 
evaluated. About a quarter of studies (8/30, 27%) had neither positive nor mixed reported evidence 
for more than half of the SF/HIT (synthesis framework for the assessment of health information 
technology) outcomes (17). 
 
  A systematic review highlighted that conversational agents generally improve patient 
engagement and health outcomes, with 75% of studies reporting positive results in usability and 
effectiveness. Specifically, they enhance adherence to pre-procedure protocols through interactive 
guidance.  In addition to providing information, AI chatbots can help alleviate patient anxiety, 
which is another common barrier to successful colonoscopy preparation. Anxiety around the 
procedure, the bowel prep process, and potential discomfort can discourage patients from 
completing their preparation effectively. AI chatbots, by offering empathetic responses and 
providing reassurance, can help ease these concerns and improve patient compliance. 

 
4 
    The ultimate goal of using AI chatbots for patient preparation is to improve clinical outcomes. 
In the case of colonoscopy (18), clinical outcomes can be measured by the quality of bowel 
preparation, which directly affects the diagnostic efficacy of the procedure, as measured by the 
Boston Bowel Preparation Scale (BBPS) score (total score range, 0-9, where 0 indicates extremely 
unsatisfactory bowel preparation and 9 indicates complete bowel preparation), evaluated by the 
endoscopist during the procedure, who scored from 0 to 3 for the 3 broad regions of the colon: 
right, transverse, and left (the cecum and ascending colon, the hepatic and splenic flexures, and 
the descending colon, sigmoid colon, and rectum). If the mucosa could not be visualized because 
of remaining stool or food residue, this segment would receive a score of 0; a score of 3 means 
that the whole mucosa of the colon segment can be seen clearly without residual staining, small 
fragments of stool, or opaque liquid. The endoscopist blinded to the group of patients. High-quality 
bowel preparation reduces the likelihood of repeat colonoscopies, increases the chances of 
detecting abnormalities, and reduces the risk of complications.  
    Secondary outcomes included the rate of adequate preparation (a score 2 for all regions), the 
polyp detection rate, the biopsy-verified adenoma detection rate, and the cecal intubation rate. A 
questionnaire can be used to evaluate patient compliance with bowel preparation (rate of 
complying with diet restriction and laxative use). A self-rated score from 0 to 10 points was used 
in the questionnaire to assess pre procedure anxiety (measured by self-rated sleep quality before 
the procedure [where 0 indicates very poor sleep quality and 10 indicates sleep quality that is the 
same as usual]), overall satisfaction with bowel preparation (where 0 indicates a very poor 
experience with a very painful process and many adverse effects and 10 indicates the examination 
has no adverse effects), and willingness to undergo another colonoscopy if indicated (where 0 
indicates great reluctance to undergo the examination again and 10 indicates willingness to 
undergo the examination again if necessary) 
 
    While AI chatbots show great promise, there are several challenges and limitations to their use 
in healthcare. One of the key challenges is the technological barrier, particularly in the complexity 
of natural language understanding (17). However, research by Marni H. Wilkoff on Can Artificial 
Intelligence Create an Accurate Colonoscopy Bowel Preparation Prompt, in 2024 suggested, 
providing ChatGPT with contextualized medical information can help further expand on areas that 
are considered vague. In addition to utilizing ChatGPT to incorporate contextual knowledge, an 
experienced gastroenterologist should review and provide additional expertise as well. Chatbots 
must be able to process and respond to a wide variety of patient queries, which can be difficult 
when patients ask nuanced or unanticipated questions. Additionally, the chatbot’s performance is 
heavily dependent on the quality of the underlying algorithms and data. Poor design or insufficient 
data can lead to errors or unhelpful responses, undermining the effectiveness of the tool. Other 
chatbots-specific negative feedback was that the virtual doctor did not have the ability to go deep 
enough or provide access to other materials, that too much information was provided or the 
interaction was too long, the use of nonverbal expressions by the avatar, and a lack of clarity 
regarding the aim of the chatbot. Some students who used the virtual patients also reported that it 

 
5 
was difficult to empathize and that the agent did not sufficiently encompass real situational 
complexity. Based on research conducted by Marni H. Wilkoff on Can Artificial Intelligence 
Create an Accurate Colonoscopy Bowel Preparation Prompt? In 2024, to ensure the prompt is 
appropriate for various education levels, it is important to modify it, allowing all patients to 
understand the instructions. Having instructions that are too advanced for an individual can lead 
to poor bowel preparation compliance and success. Creating multiple versions of bowel 
preparation instructions has the potential to create more work for physicians, but with the help of 
ChatGPT, one can query the large language model to customize the response to a specific 
education level. 
 
Another limitation is patient acceptance. Some patients may be hesitant to trust AI-based systems, 
especially when it comes to medical advice or procedural instructions. This is particularly true for 
older adults or individuals with lower levels of health literacy or technological proficiency. 
Ensuring that AI chatbots are designed to be user-friendly and accessible is crucial to overcoming 
this barrier. As for patients with technology illiteracy, it is important to prepare the patient with 
hands-on guidance and offering them reassurance, to build their confidence in using the AI chatbot 
effectively. Starting off by providing basic education and demonstration, making sure patients 
have easy access to the AI chatbots by bookmarking or installing apps, walking them through the 
steps and clarifying any concerns they have.  
 
   Traditional methods of patient preparation for colonoscopy often rely on printed materials, 
pamphlets, or phone calls from healthcare professionals. While effective to some extent, these 
methods lack interactivity and personalization. To address this, a designated laptop in the clinic 
can be used during the consent process, allowing patients to interact with an AI chatbot for 
colonoscopy preparation. This setup ensures immediate assistance if patients face difficulties, with 
prompts and guidance provided by staff. This hybrid approach combines the 24/7 availability of 
AI chatbots with direct support, enhancing patient understanding and adherence to preparation 
guidelines, ultimately improving outcomes like detection rates during colonoscopy.. 
 
    Although the initial studies on AI chatbots in healthcare are promising, there is still a need for 
further research to fully understand their potential in pre-procedural patient education. Future 
studies should focus on improving the natural language processing capabilities of chatbots to 
ensure they can handle more complex patient interactions. Additionally, research is needed to 
evaluate the long-term impact of AI chatbot use on patient outcomes, such as the quality of 
colonoscopy preparation and procedural success. There is also a need to explore patient attitudes 
towards AI chatbots and identify strategies to increase acceptance and trust in these systems. 
Further, integration with electronic health records (EHRs) could enable AI chatbots to provide 
even more personalized and contextually aware guidance, improving their effectiveness and 
reducing the risk of errors. 
 

 
6 
    AI chatbots represent a promising tool for enhancing patient preparation for colonoscopy. They 
offer several advantages, including personalized education, real-time support, and improved 
patient adherence to preparation guidelines. Early studies suggest that AI chatbots can lead to 
better clinical outcomes, such as improved bowel preparation. However, challenges such as 
technological limitations, patient trust, and acceptance need to be addressed. As AI technology 
continues to evolve, further research will be critical in refining chatbot systems and exploring their 
full potential in improving patient preparation for medical procedures like colonoscopy. 
 
 
Problem Statement  
Traditional pre-colonoscopy counseling requires significant time from healthcare workers to 
explain procedures, limiting efficiency and patient turnover. Inadequate bowel preparation 
exacerbates this issue, leading to repeat procedures and increased costs. However, no study has yet 
evaluated the effectiveness of AI in improving the Boston Bowel Preparation Scale (BBPS) for 
colonoscopy preparation. By addressing this gap, AI chatbots could provide personalized 
guidance, reduce healthcare worker burden, improve preparation quality, and enhance patient 
experience. 
 
General objectives 
 
To determine the effectiveness of artificial intelligence (AI) chat bot in improving bowel 
preparation among colonoscopy patients in Hospital Tuanku Mukhriz (HCTM), compared to 
conventional instructions 
 
Specific Objectives 
1. To determine the effectiveness of AI chat bot in relieving anxiety among colonoscopy 
patients in HCTM, compared to conventional instructions. 
2. To determine the effectiveness of AI chat bot in improving satisfaction among colonoscopy 
patients in HCTM, compared to conventional instructions. 
Hypothesis  
Artificial intelligence (AI) chat bot can improve bowel preparation, anxiety level and patient’s 
satisfaction among colonoscopy patients in Hospital Tuanku Mukhriz (HCTM), compared to 
conventional instructions 
 
 
 

 
7 
Null hypothesis  
Artificial intelligence (AI) chat bot cannot improve bowel preparation, anxiety level and patient’s 
satisfaction among colonoscopy patients in Hospital Tuanku Mukhriz (HCTM), compared to 
conventional instructions 
Study Design 
This study is a prospective, block randomized controlled trial designed to evaluate the 
effectiveness of an AI-powered chatbot (ChatGPT) in improving colonoscopy preparation. 
Participants will be randomly assigned into two groups: an intervention group utilizing the chatbot 
and a control group receiving standard written or verbal instructions. The study will compare 
outcomes between these groups to assess the chatbot's impact on bowel preparation quality, patient 
satisfaction, and anxiety reduction. 
Study Population 
The study will recruit adult patients aged 18 to 75 years who are scheduled for elective 
colonoscopy at Hospital Canselor Tuanku Muhriz (HCTM). Inclusion criteria include patients with 
internet access and proficiency in using smartphones or web-based platforms. Participants must be 
able to comprehend one of the following languages: English and Bahasa Melayu and must have a 
valid medical indication for undergoing a colonoscopy. Participants must also have an educational 
background of at least SPM level. 
Exclusion criteria include patients with dementia, impaired memory, or other neurological 
conditions such as Alzheimer’s disease or severe stroke. Additionally, patients with a clinical 
diagnosis of anxiety or those experiencing gastrointestinal conditions such as diarrhea will also be 
excluded from the study. 
 
Sample Size 
Sample size of the study is calculated based on each study outcome to be studied (i.e., bowel 
preparation, anxiety, satisfaction, and medication adherence). 
  
Study Outcomes 
Sample Size Calculations 
Bowel Preparation (BBPS) 
Given that BBPS for colonoscopy patients who received AI 
instruction = 6.74 ± 1.25 points [Zhu], BBPS for colonoscopy 
patients who received conventional instruction = 5.97 ± 1.81 
points [Zhu], significance level = 0.05, power of study = 80%, the 

 
8 
sample size required = 82. This is calculated using Pocock's 
formula for two means. 
  
= 2 (1.25)2 (1.96 + 0.84)2 
                0.772 
  
= 41 per group  
  
= 82 for both groups 
Anxiety (DASS-21) 
No previous evidence found; hence, sample size cannot be 
calculated based on satisfaction. 
Satisfaction (PSQ-18) 
No previous evidence found; hence, sample size cannot be 
calculated based on satisfaction. 
  
 
Based on the above calculations, we will choose the largest sample size, which is 82 patients. To 
account for missing data and loss of follow-up, the sample size is inflated by 10%, hence, a final 
sample size of 92 (i.e., 82/0.9) patients is required. 
 
Materials 
The primary tool for this study is an AI chatbot (ChatGPT) customized with content specific to 
colonoscopy preparation from reputable medical sources. The chatbot provides detailed 
instructions on personalized dietary modifications, laxative use, step-by-step preparation 
guidelines and also motivational messages tailored to the existing bowel preparation protocol used 
in the clinic. A personalized script is provided to each participant in the intervention group, 
containing details such as their name, age, comorbidities, medications, and the date of their 
scheduled colonoscopy. The script concludes with essential instructions on what to do and know 
before the procedure. Available in two languages—English and Malay—the script ensures 
accessibility for diverse patient groups. Patients can input the script into the chatbot and ask 
questions for better understanding and to alleviate anxiety. Additionally, participants are required 
to complete a research consent form before participating in the study. 
The standard protocol for the study includes a colonoscopy consent form and the use of Fortrans, 
a polyethylene glycol-based laxative. The dosage involves one sachet diluted in one liter of water, 
to be consumed at specific intervals: 6:00 PM and 8:00 PM the day before the colonoscopy, and 
again at 6:00 AM on the day of the procedure. 

 
9 
The control group in this study serves as a benchmark for the intervention group. Patients in this 
group receive verbal instructions from healthcare workers at the clinic. These instructions include 
dietary modifications, laxative preparation and consumption, and general guidelines for 
colonoscopy preparation. Unlike the intervention group, the control group relies solely on 
traditional face-to-face communication without the integration of AI-driven tools. 
To assess the outcomes of the study, three validated assessment tools are utilized. The primary 
assessment tool is the Boston Bowel Preparation Scale (BBPS), which is used to evaluate the 
cleanliness of the bowel during the colonoscopy. This score is determined by blinded endoscopists 
to minimize bias. The secondary assessment tools include the Depression Anxiety and Stress Scale 
21 (Anxiety Scale), which measures patients' anxiety levels before and after the preparation 
process, and the Patient Satisfaction Questionnaire (PSQ), which evaluates the overall satisfaction 
of participants with the preparation process. Both the DASS 21 and PSQ are scored using a Likert 
scale to ensure standardization and comparability 
 
Methodology  
The study is conducted in a colorectal clinic, specifically on Tuesdays. Participants are recruited 
and divided into two groups: the intervention group and the control group. Each group follows a 
distinct preparation protocol, allowing for a comparative analysis of outcomes. 
In the intervention group, participants interact with the AI chatbot via a laptop. The process begins 
with participants receiving a personalized script that includes their demographic and medical 
details, such as name, age, comorbidities, current medications, and their scheduled colonoscopy 
date. The script concludes with a summary of what the participants should do and know before 
their colonoscopy, tailored to ensure clarity and relevance. This script is available in two 
languages—English and Malay—allowing participants to select their preferred language. 
Participants are instructed to input the script into the chatbot at the start of the session. From there, 
the chatbot provides comprehensive guidance on dietary restrictions, laxative usage, and step-by-
step instructions for the preparation process. Moreover, participants are encouraged to freely 
interact with the chatbot by asking questions to clarify doubts or address concerns. This interactive 
feature not only enhances understanding but also helps relieve anxiety by creating a supportive 
and reassuring experience. The chatbot’s motivational messaging further empowers participants 
to adhere to the preparation protocol. For those requiring additional support, clinic staff are 
available to assist during the interaction. Each session with the chatbot is designed to last between 
15 to 30 minutes, ensuring efficiency and minimizing disruption to clinic operations. 
The control group, in contrast, receives preparation instructions through traditional verbal 
communication from healthcare professionals. These instructions cover the same key areas as the 

 
10 
chatbot, including dietary modifications, laxative preparation and administration, and general 
preparation guidelines. However, the control group does not have access to the additional features 
of real-time interaction, personalized support, or motivational messaging provided by the chatbot. 
Data collection in this study focuses on both primary and secondary outcomes. The primary 
outcome, bowel cleanliness, is assessed using the Boston Bowel Preparation Scale (BBPS). 
Blinded endoscopists evaluate the cleanliness of the bowel during the colonoscopy, ensuring an 
unbiased assessment of the preparation process. Secondary outcomes include the reduction in 
patient anxiety levels and overall satisfaction with the preparation process. Anxiety levels are 
measured using the Depression Anxiety and Stress Scale 21 (Anxiety Scale), with pre- and post-
intervention scores compared to determine the impact of the intervention. Patient satisfaction is 
evaluated using the Patient Satisfaction Questionnaire (PSQ), which assesses various aspects of 
the preparation process. Both secondary outcomes are scored using a Likert scale, providing a 
standardized metric for analysis. Anxiety and satisfaction are assessed on the day of the procedure 
rather than at the time of intervention (appointment scheduling) because anxiety levels may not be 
significant early on but tend to peak closer to the procedure. Assessing these outcomes at the final 
stage ensures a more accurate evaluation of the intervention’s lasting impact on stress management 
and overall patient experience. 
Research Flow Chart 
 
 
 
 
 
 

 
11 
Statistical plan and analysis 
 
For this research, data analysis will involve both descriptive and inferential statistical methods to 
evaluate adherence, anxiety reduction, and the quality of colonoscopy preparation. The first step 
will be to conduct a descriptive analysis of participant characteristics, chatbot interaction metrics, 
and adherence rates. Categorical data, such as gender, ethnicities, types of diagnosis, etc, will be 
summarized using frequencies and percentages, while continuous variables, including anxiety 
scores and Boston Bowel Preparation Scale (BBPS), Depression Anxiety and Stress Scale 21 
(Anxiety Scale), Satisfaction PSQ-18, will be reported as means and standard deviations or 
medians with interquartile ranges, depending on the normality of the data. To assess the normality 
of the data, Kolmogorov-Smirnov test will be used. 
The Patient Satisfaction Questionnaire, PSQ-18 will be used to evaluate the satisfaction 
improvement in between the intervention group (i.e., receive AI instructions) and control group 
(i.e., receive conventional instruction), will be analyzed using independent t-test if data is normally 
distributed, otherwise Mann-Whitney’s test will be used.  
The Depression Anxiety and Stress Scale 21 (Anxiety Scale) will be used to evaluate anxiety 
reduction pre-procedure and post-procedure in both intervention group (i.e., receive AI 
instructions) and control group (i.e., receive conventional instruction). Paired samples t-tests will 
be conducted to determine whether there is a significant reduction in anxiety scores post-
intervention compared to baseline. If the data are not normally distributed, the Wilcoxon signed-
rank test will be applied instead. The null hypothesis is that there is no significant change in anxiety 
levels before and after using the AI chatbot. 
The primary outcome, colonoscopy preparation quality, will be measured using the BBPS during 
the procedure. Comparisons of BBPS scores across adherence levels or engagement groups will 
be analyzed using independent t-test (if data is normal) or Mann-Whitney’s test (if data is not 
normal). To control for potential confounders, such as age, gender, comorbidities, level of 
education, will be controlled using multivariate regression.. 
Finally, all analyses will include confidence intervals to provide precision estimates, and p-values 
will be reported to assess statistical significance (p < 0.05 ). This comprehensive data analysis plan 
ensures a robust evaluation of the chatbot intervention's impact on adherence, anxiety reduction, 
and preparation quality. All statistical analyses will be performed using SPSS version 28. 

 
12 
Budget Proposal  
Equipment and Supplies  
 
 
No. 
Item  
Quantity  
Unit Cost 
(RM)  
Total Cost (RM)  
Justification  
1.  
Chat Gpt Plus  
6 month  
$25 
(RM111.75)/
month  
RM 671 
ChatGPT Plus ensures 
reliable access, 
minimizing disruptions 
and response variability. 
It restricts sources to 
trusted, Malaysia-
approved resources for 
accurate colonoscopy 
guidance. With 
enhanced reliability and 
real-time support, it 
maintains study 
integrity, ensures high-
quality research, and 
prioritizes patient safety 
with medically approved 
advice from 
authoritative sources. 
2.  
Patient Honorarium 
96 patient 
RM 10/patient RM 960 
The patient honorarium 
budget values 
participants' time, 
encouraging enrollment 
and adherence. It boosts 
engagement, reduces 
dropouts, and enhances 
data reliability, ensuring 
the study's successful 
completion and valid 
findings 
 
Total 
 
 
RM  1631 
 
 
*Based on current currency 
 
Source: Geran Fundamental Fakulti Perubatan 

 
13 
Gantt Chart  
 
No. 
Task/Month 
Oct 
Nov 
Dec 
Jan 
Feb March Apr May June July Aug 
Sept 
1.  
First meeting with supervisor 
and discuss possible title for 
research  
 
 
 
 
 
 
 
 
 
 
 
 
2.  
Discuss literature review 
related with research title  
 
 
 
 
 
 
 
 
 
 
 
 
3.  
Budget proposal discussion  
 
 
 
 
 
 
 
 
 
 
 
 
4.  
Submission of proposal  
 
 
 
 
 
 
 
 
 
 
 
 
5.  
Present proposal at the 
department 
 
 
 
 
 
 
 
 
 
 
 
 
6.  
Submit proposal to ethics 
committee 
 
 
 
 
 
 
 
 
 
 
 
 
7.  
Present the proposal to the 
ethics committee if needed. 
 
 
 
 
 
 
 
 
 
 
 
 
8.  
Data collection  
 
 
 
 
 
 
 
 
 
 
 
 
9.  
Prepare manuscript and Data 
Analysis  
 
 
 
 
 
 
 
 
 
 
 
 
10.  
Manuscript submission  
 
 
 
 
 
 
 
 
 
 
 
 

 
14 
Appendix 1 
 
Proforma Form  
 
Section 1: Patient Demographics  
 
Name  
 
Age 
 
Gender  
 
MRN 
 
BMI 
 
Profession / Occupation 
 
 
Digital Literacy Level 
Basic/Intermediate/Advanced 
Comorbidities 
Diabetes Mellitus/Hypertension/Chronic Kidney 
Disease/Ischemic Heart Disease 
 
Others: 
Relevant medical history 
Previous history of constipation (Rome’s Criteria)/ 
Previous history of diarrhoea  
 
Others: 
Smoker 
Yes/No 
Alcohol drinker  
Yes/No 
Section 2: Bowel Preparation 
 
Dietary restriction before colonoscopy  
Yes/No 
Completion of laxative (Fourtrans) as 
instructed 
Yes/No 
 
If No state the reasons:  
Section 3: Colonoscopy Details 
 
Indication for colonoscopy 
 

 
15 
Dosage of sedation(midazolam&pethidine)  
Completion 
Complete/Incomplete 
 
If Incomplete state the reason: 
Time taken 
 
Diagnosis  
 
Bowel preparation method  
 
Boston Bowel Preparation Scores (BBPS)  
 
Finding  
 
Intervention during colonoscopy 
(Yes/No) 
If Yes state the indication: 
 
 
Histopathology examination (if available). 
Benign/Malignant.  
Adverse event 
Yes/No 
 
If Yes state the event: 
 
 
Section 4: Surveys and Questionnaires 
 
● State Trait Anxiety Inventory Questionnaire  
● Patient Satisfaction Questionnaire -18  
 
 
 
 
 
 
 
 
 
 
 
 
 

 
16 
 
Appendix 2 
 
Personalized scripts 
 
English  
Hi, my name is [Insert Name]. I am [Insert Age] years old and have the following medical 
conditions: [List Comorbidities Ex: Diabetes, Cholesterol, Blood Pressure]. I am currently on 
these medications: [List Medications]. I am scheduled for a colonoscopy on [Insert Date]. 
Please ensure that all the answers related with the colonoscopy procedure will be from KKM 
guidelines and a reputable journal.  
Can you tell me what I need to do or know before the colonoscopy? 
 
Malay 
Hai, nama saya [Masukkan Nama]. Saya berumur [Masukkan Umur] tahun dan mempunyai 
keadaan kesihatan seperti berikut: [Senaraikan Komorbiditi, CTH : Kencing manis, 
Kolesterol, Darah Tinggi]. Saya sedang mengambil ubat-ubatan ini: [Senaraikan Ubat]. Saya 
dijadualkan untuk kolonoskopi pada [Masukkan Tarikh]. Sila pastikan bahawa semua jawapan 
berkaitan prosedur kolonoskopi adalah berdasarkan garis panduan KKM (Kementerian 
Kesihatan Malaysia) dan jurnal yang bereputasi. 
Boleh tak anda terangkan apa yang saya perlu buat atau tahu sebelum kolonoskopi? 
 
 
 

 
17 
Appendix 3 
DASS-21 and PSQ-18 Questionnaires 
 
 
 

 
18 
 

 
19 
 

 
20 
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M.; Dekker, E.; Dinis-Ribeiro, M.; Ferlitsch, M.; et al. Bowel preparation for colonoscopy: 
European Society of Gastrointestinal Endoscopy (ESGE) Guideline—Update 2019. 
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