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Mental Health Chatbot : Empathetic Coach to Reduce Stress & Depression

Mental Health Chatbot : Empathetic Coach to Reduce Stress & Depression

The overnight development of natural language processing and conversational interfaces has brought automated chatbots to a cutting edge of this 21st Century.

The innovative approach of data scientists and developers has revolutionized chatbots into a more progressive way to alleviate mental illness problems, stress management, and psychological relief.

It is a matter of concern; According to World Health Organization (WHO), there are various mental disorders which are generally characterized by a combination of perceptions, emotions, abnormal thoughts, behavior, and relationship with others.

About 800 000 people die due to suicide every year; Suicide is the second leading cause of death among 15–29-year-olds.

According to the Scientific American reports of WHO, the economic cost of depression in the US is in the hundreds of billions annually.

Despite the access, Even the availability of competent health care and social services struggle to connect with those afflicted with mental illness conditions due to their hesitation and stigma surrounding the illness to avail the treatment.

To break the ice, various information technology firms have developed chatbot applications empowered with artificial intelligence for smartphones that aims to be the primary line of support for mental illness patients.

Welcome to the Makerobos expert column, this article will spill the beans about Mental Health Chatbots and their market in 2020.

What are mental health chatbots?

mental health chatbot is a conversational interface application developed to:

1. Make conversations with patients affected by a mental health issue. The primary objective of a mental health chatbot is:

a) To understand and manage their patient’s psychological state on their own as much as possible.

b) To connect the patients with mental well-being professionals during unfavourable events.

2. Instant chat support for 24/7 hours.

3. Deliver well detached analyzed statistics for the patients to self-regulate their psychological state.

4. Give recommendations to users regarding their psychological well-being.

ELIZA (1966) was the first mental health chatbot that originated from the beginnings of natural language processing.

This chatbot was created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.

Basically, ELIZA was good at talking with people, and it was evident to the concept “if a machine can impersonate a human then it is intelligent” Eventually, ELIZA evolved itself to be more than that.

Eliza mimicked conversation by using “pattern matching” and substitution methodology, which gave users an illusion of interaction with a real human being, although the process was automated one.

Despite its intuitive design, ELIZA was engaging enough to let the people speak out their issues (which is the most straightforward blueprint of delivering psychological relief); all of this resulted in groundwork for the future of healthcare chatbots.

Nowadays, the evolution of API with AI has turned chatbots from coupled of template phrases to modern well-being chatbots that integrate into the healthcare system and involve certified medical professionals.

These chatbots can automate specific processes to streamline the interaction between the patient/user and mental well-being professionals.

How do mental health chatbots work?

The mental health chatbots are developed to convey a conversation, not to lead it. The approach of a mental well-being chatbot resembles practicing tennis against a wall.

Their general functional framework includes Cognitive Behavioural Therapy (CBT), which is a form of interactive therapy designed to supervise mental illness issues by rearranging the way the patient perceives it, i.e., converting negative thoughts into positive thoughts.

The list of an interactive chatbot feature includes:

  • Prompting a topic for conversation.

  • Carrying on the interaction by asking directional questions.

  • Using follow-ups to facilitate responses.

The flow of a conversation is handled using the NLP algorithm, with intuitive Sentiment Analysis Features, which recognizes the keywords and terms proactively.

Each trigger word consists of its decision tree that is developed to gather information and deliver a viable resolution, i.e., a simple conclusion, a piece of advice, or contacting a preferred professional.

Above all, empathetic engagement is the critical design component in psychological well-being chatbots.

In the context of a Cognitive Behavioural Therapy (CBT) interface, empathetic engagement means:

Making the impression of a credible and trustworthy conversational partner that can give a sense of acknowledgment to the user and provide a detached point of view on things.

Apparently, the user remains aware of the artificial nature of a conversational interface. Hence, there is no need to go back to the drawing board to imitate a fully interactive human to human conversation. Instead, the chatbots should provide minimum credibility to enable the user’s suspension of disbelief.

Despite challenges, privacy and responsibility are some of the biggest concerns of mental well-being chatbots. As the entirety of user activity is related to sensitive information and personal matters; therefore, it is essential to address this issue.

Following are the most effective solutions for this issue:

  1. For every user-bot interaction, end to end encryption must be encouraged.

  2. The user profile in the application database should be anonymous.

Concluding Thoughts

There are various mobile applications to address psychological illness disorders. Still, they are relatively new, as the World Health Organization and the feedback of their growing number of users, there is a need of psychological health-oriented mobile applications for consumers.

The extensive use of mobile phones and internet connectivity has made these anonymous and private apps more accessible in assisting numerous patients at a time.

Nevertheless, there exist several vital issues that surround psychological health chatbots; it includes the difficulty to dispel the stigma of mental illness disorders. This is barring the mental illness affected patients from stepping forward to seek treatment.

Secondly, these mobile apps fail to provide any type of specific medical treatment. They are only at the first-line defense to alleviate the medical health symptoms, which is potentially a crucial step towards improving well-being, and the outbreak of COVID-19 is leveraging the capabilities of these apps on a larger scale.

Mostly, the available apps in the market are developed within academic research settings that rarely have the infrastructure to support and bring them wider to the market.

Another challenge for developers to leverage the efficiency of mental health chatbots revolves around natural language processing for text and speech.

Mental health is among those fields that always require cutting edge technologies to deliver a productive and frequently available service to everybody.

The functionalities of psychological health chatbots with conversational interface seem to be a viable solution capable of managing basic needs for anxiety and depression management.

Even if the ultimate benefit is encouraging people to speak out their worries and relieve stress — that’s already a giant step forward.

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