Comparing the Effectiveness of AI Chatbot with That of Telephone Hotline (AI chatbot)

September 30, 2024 updated by: Dr. Patrick Ip, The University of Hong Kong

Comparing the Effectiveness of an AI Chatbot with That of a Telephone Hotline for Answering COVID-19 Related Issues

The COVID-19 pandemic has significantly impacted the wellbeing of people in Hong Kong, leading to social distancing policies and changes in healthcare service utilization. School closures and remote work have increased stress levels for parents and children. Vulnerable populations, such as low-income families and children with special needs, are at higher risk of maltreatment and mental health issues. Parental burnout has become a concern as parents juggle work, childcare, and education responsibilities. There is a need for research on the physical and mental health effects of COVID-19 on families and the potential role of AI in addressing these challenges. AI, particularly chatbots, can provide accessible healthcare information and support, aiding in early diagnosis and treatment. AI chatbots offer timely responses, accurate information, and continuous availability, making them valuable tools for remote health assistance. While AI chatbots are not without limitations, further research can help integrate them more effectively into healthcare services.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

The COVID-19 pandemic has had an unprecedented impact on the wellbeing of people in Hong Kong since the outbreak in December 2019. The Government has adopted social distancing policies to minimise the risk of infection. These include but are not limited to; school closure, remote working, and the prohibition of group-gatherings. These anti-infection measures have led to a change in pattern in the use of healthcare services and help-seeking activities. Studies have also shown that a dearth of socialisation leads to higher stress levels for both parents and children.

As school closure and remote work measures continue, both children and parents are under great pressure. UNESCO (2020) reported that over 1.58 billion children and youth in 200 countries were affected by school closure, as of mid-April 2020. Although the long-term effect of COVID-19 on children's and parents' mental health is unknown, cases of child abuse, neglect and exploitation have increased in the face of such unprecedented times. Low-income families or families with children with special education needs (SEN) are prone to children being maltreated and/or having mental health crisis . Parents who work from home are facing challenges of fulfilling a triple role: work, childcare and homecare. Worse still, children's lack of learning interests and motivation adds extra burden on parents as they take up the role of teachers. Parents are inclined to experience parental burnout, which is characterised by mental and physical exhaustion, with a feeling of hopelessness. Therefore it is clear there are strong societal needs for COVID-19 physical and mental health research. It is imperative to prevent potential and mitigate existing problems regarding parent-child relationship, parental stress and family functioning caused by COVID-19.

Consequently, exploring more easily accessible and efficient ways of dealing with potential and existing health problems (both physically and mentally) should be a priority. Artificial Intelligence (AI) in healthcare services has the potential to reduce the workload of healthcare workers by answering frequently asked questions through the AI system all from the comfort of the subject's home. Considering the potentially detrimental effect of COVID-19 on both children and parents it is important to fill the research gap as to how AI may serve as a platform for help-seeking, particularly during times of social distancing.

AI has been widely adopted in healthcare services in the past decade. The use of chatbots, in particular, has enhanced public engagement in health service all from the comfort of the subject's home. AI chatbots utilised natural language processing (NLP) to facilitate interaction with users in conversations, making appropriate medical advice accessible to the public. Intelligent algorithms in AI enables early diagnosis of disease and offers treatment techniques to those who may otherwise have been diagnosed too late. For instance, the U.S. Centres for Disease Control and Prevention (CDC) has launched a chatbot named Clara to help users access information on potential symptoms of coronavirus and help enable them to make decisions about the need to seek medical care). This is especially useful as it identifies high-risks groups in need of medical attention by triaging patients according to their symptoms, therefore reducing hospital visits for minor cases. It also provides support to family members of high-risk groups as to what measures can be taken to prevent infection and ways to relieve pressure in taking care of patients within their family.

AI chatbots merit attention in its prompt response to users' questions as it provides a service around the clock. In addition, answers provided by AI are considered more accurate than that of search engines, subject to the proficiency of data mining methods. These features are of significance as users are able to seek psycho-medical advice while practising social distancing, without face-to-face appointments with clinicians.

AI chatbots may serve as a self-help tool for gaining insights in dealing with both mental and physical conditions but it is far from perfection. The hope is that this study can contribute to making AI chatbots an integrated part of the health care service.

Study Type

Observational

Enrollment (Actual)

48

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Hong Kong
      • Hong Kong, Hong Kong, 0000
        • Department of Paediatrics and Adolescent Medicine, The University of Hong Kong

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

No specific population

Description

Inclusion Criteria:

  • Subjects who give consent to participate in the study.

Exclusion Criteria:

  • Subjects who do not give consent to participate in the study.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Control Group
Participants will be asked to consent to randomization on their first access to our system. Users ask questions covered by the question bank and specific questions not covered by the question through a telephone hotline.
Other: Telephone hotline
Participants will be asked to consent to randomization on their first access to our system. Users ask questions covered by the question bank and specific questions not covered by the question through a telephone hotline.
Intervention Group
Participants will be required to provide consent for randomization when they first access our system. Users can ask questions covered by the question bank, as well as specific questions not covered by the bank, through an AI chatbox.
Other: AI Chatbot
Participants will be required to provide consent for randomization when they first access our system. Users can ask questions covered by the question bank, as well as specific questions not covered by the bank, through an AI chatbox. The aim is to understand the significant difference between using AI chatbots and telephone hotlines to assist parents, as well as the effectiveness of AI chatbots compared to telephone hotlines.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
General Anxiety Disorder (GAD - 7)
Time Frame: 1 week within Pre-test, 1 week within Post-test
The Generalized Anxiety Disorder 7-item (GAD-7) questionnaire is a self-reported screening tool used to assess the severity of generalized anxiety disorder symptoms in adults. It consists of seven questions that ask about various symptoms commonly associated with generalized anxiety disorder, such as feeling nervous, anxious, or on edge.General Anxiety Disorder (GAD - 7)
1 week within Pre-test, 1 week within Post-test
Patient Health Questionnaire-9
Time Frame: 1 week within Pre-test, 1 week within Post-test
The Patient Health Questionnaire-9 (PHQ-9) is a self-administered questionnaire used to screen for and assess the severity of depression in patients. It consists of nine questions based on the criteria for diagnosing major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Patients are asked to rate how often they have experienced certain symptoms of depression over the past two weeks, with response options ranging from "not at all" to "nearly every day." The total score on the PHQ-9 can help healthcare providers determine the presence and severity of depression in a patient, as well as monitor their response to treatment over time.
1 week within Pre-test, 1 week within Post-test

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Service Satisfaction Survey
Time Frame: 1 week within Pre-test, 1 week within Post-test
A Service Satisfaction Survey is a method used by organizations to collect feedback from customers or clients regarding their satisfaction with the services provided. These surveys typically include questions that assess various aspects of the service experience, such as quality, responsiveness, professionalism, and overall satisfaction. The feedback gathered from these surveys can help organizations identify areas for improvement and make informed decisions to enhance their services.
1 week within Pre-test, 1 week within Post-test

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The University of Hong Kong

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 3, 2021

Primary Completion (Actual)

September 4, 2024

Study Completion (Actual)

September 5, 2024

Study Registration Dates

First Submitted

September 16, 2024

First Submitted That Met QC Criteria

September 30, 2024

First Posted (Actual)

October 1, 2024

Study Record Updates

Last Update Posted (Actual)

October 1, 2024

Last Update Submitted That Met QC Criteria

September 30, 2024

Last Verified

September 1, 2024

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • UW21-344
  • Collaborative Research Fund (Other Grant/Funding Number: University Grants Committee)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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