Developing an AI Pharmacy Chatbot for the Population of Hong Kong
- Conditions
- HyperglycaemiaHypertensionHyperlipidaemiaArtificial Intelligence (AI)
- Registration Number
- NCT07037563
- Lead Sponsor
- The University of Hong Kong
- Brief Summary
The primary objective of this study is to is to develop a conversational AI service (chatbot) in Hong Kong to assist patients and caregivers with inquires related to Hospital Authority (HA)-prescribed medications, and to evaluate its effectiveness in answering medication-related questions. The main questions it aims to answer are:
1. How satisfied are the patients and caregivers with this pharmacy chatbot?
2. How will the pharmacy chatbot impact eligible patients or caregivers on their (or the patient they are caring for) medication adherence, knowledge, and the consultation time with HA pharmacists?
There will be an intervention group and a control group:
1. The intervention group will be invited to use the AI Pharmacy Chatbot online for 7 days through WhatsApp, a commonly used social media platform in Hong Kong. They can inquire about their medications prescribed under HA and get instant, validated answers from the chatbot.
2. The control group will not use the chatbot during the intervention period.
To evaluate the chatbot's usability, researchers will measure patient satisfaction through usability questionnaires issued to the intervention group after their intervention period. Differences in medication adherence, medication knowledge, and HA pharmacist consultation time will also be measured between the intervention and control groups after the intervention period, to determine the chatbot's impact.
- Detailed Description
In this study, we aim to develop an artificial intelligent (AI) pharmacy chatbot and evaluate its effectiveness in answering questions related to prescribed-medications.
We will first develop an AI pharmacy chatbot prototype utilizing advanced technologies such as AI and natural language processing. By implementing Hospital Authority (HA) drug database and suggestions from Chief Pharmacist's Office (CPO), a Proof-of-Concept phase will be conducted to validate the chatbot's ability to address medication-related questions for locally prevalent chronic conditions in Hong Kong: hyperglycaemia, hypertension, and hyperlipidaemia. After completing the prototype development, we will invite pharmacists from HA for testing and evaluation of the prototype, further finetuning the chatbot based on their feedback.
Then we aim to enhance the usability of the chatbot by incorporating feedback from patients and caregivers. We aim to recruit a minimum of 280 patients receiving HA-prescribed medication on hyperglycaemia, hypertension, and / or hyperlipidaemia, or their caregivers. According to a previous study, the medication adherence of patients with chronic conditions in Hong Kong was about 61.63%. For an increase to 80% of adherence would require a minimum of 140 participants per group, with a significance level (α) of 0.05, a test power (1-β) of 0.8, and a cluster design effect (D) of 1.5.
Recruitment of patients and caregivers will involve collaboration with HA to identify eligible participants. Designated pharmacists and other healthcare professionals from HA will help distribute recruitment materials to eligible patients and caregivers and provide researchers a list of participants who express the willingness to participate.
Participants will be randomized in a 1:1 ratio to either the control group or the intervention group. Before the intervention period, they will all receive a consent form and an information sheet which provides details on the study. They will also be asked to complete a pre-study survey, which includes questions on socio-demographics, drug usage habits, medical history and experience with chatbots.
Participants in the intervention group will then be invited to use the pharmacy chatbot and receive a usability test guideline, with an emphasis on the chatbot's role as a support tool for medication inquiries. The intervention duration will last for 7 days and the control group will have no exposure to the chatbot during the period. After 7 days, a post-study survey will be conducted to all participants in both the intervention and control groups. Participants in the intervention group will also receive a usability questionnaire to feedback on the chatbot.
A convergent mixed methods design, combining qualitative and quantitative approaches, will be used to evaluate the intervention effect of the chatbot.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 280
- Patients aged 18 years or older diagnosed with either hyperglycaemia, hypertension, or hyperlipidaemia, and currently receiving medication prescribed by the Hospital Authority in Hong Kong.
- Caregivers aged 18 years or older for patients described above.
- There is no exclusion criterion, but they should meet the inclusion criteria above.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method Medical knowledge 1 week Before the intervention period, all participants will be asked whether they disagree or agree with the following statements on a 5-point Likert scale (Strongly agree, Agree, Neutral, Disagree and Strongly disagree):
1. I understand the main indications of my medications (or the medications of the patient I care for).
2. I understand the potential side effects of my medications (or the medications of the patient I care for).
3. I know how to take my medications (or administer the medications of the patient I care), including the dosage and frequency, thereby promoting my medication adherence.
4. I have concerns or worries about my medications (or the medications of the patient I care for).
5. I can obtain medication-related information efficiently and easily.
After the 7-day intervention period, they will be asked again the same questions.Medical adherence 1 week Before the intervention period, all participants will be asked whether they disagree or agree with the following statement on a 5-point Likert scale (Strongly agree, Agree, Neutral, Disagree and Strongly disagree): I know how to take my medications (or administer the medications of the patient I care), including the dosage and frequency, thereby promoting my medication adherence.
After the 7-day intervention period, they will be asked again the same questions.
- Secondary Outcome Measures
Name Time Method Response time 1 week We will calculate from the backend the chatbot's response time when responding to user's input (text, image and voice).
User satisfaction 1 week Participants in the intervention group will be asked whether they disagree or agree to the following statements on a 5-point Likert scale (Strongly disagree, Disagree, Neutral, Agree and Strongly agree):
1. The chatbot is simple to use and user-friendly.
2. The chatbot understands my queries and is able to address them.
3. The chatbot's responses are relevant and sufficient.
Moreover, they will also be asked to rate their overall experience with the chatbot on a 5-point Likert scale (Very Good, Good, Moderate, Poor, and Very Poor), and whether they will use the chatbot again in the future and whether they would recommend others to use the chatbot.Consultation with pharmacists 1 week Before the intervention period, we will ask all participants their frequency and consultation time with pharmacists. After the 7-days intervention period, we will ask them again whether they have visited any pharmacists during the time, and if so, the frequency and consultation time.
Trial Locations
- Locations (2)
Laboratory of Data Discovery for Health
🇭🇰Hong Kong, Hong Kong
The University of Hong Kong
🇭🇰Hong Kong, Hong Kong