How to Build a Quality AI-Based Mental Health Counseling App

Because of the mental illness crisis that has worsened during the last ten years, digital solutions functioning on artificial intelligence offer hope for millions of people to improve their mental state, or at least to keep it balanced. Technology companies and scientific establishments have developed the applications ready to provide daily therapist sessions and support patients` treatment programs.

It seems AI-based solutions came at the right time. The Global Future Council Report on Health by the World Economic Forum confirms the integral participation of AI in mental disease treatment. According to the research, AI-based technologies will be used for more precise diagnostics and drug development. It is quite crucial because the US endures a significant shortage of psychiatrists, and this fact contributes to the crisis. More than 60% of US counties do not have a single psychiatrist working nearby. However, the area of mental health treatment is sensitive to the development world and requires a particular approach to building an effective chatbot app solution. 

The same WEF research on health reveals that one adult patient in 20 is misdiagnosed in the United States. So the difference AI-based apps make is their diagnosis support. Many of such solutions applied various features to reach the best results, like acceptance-commitment and cognitive-behavioral therapies, gamification, art therapy, mood training programs, etc. Each product is individual, of course.

However, it is crucial to consider the following features so that AI mental counseling app brings effective outcomes:

Personal control. For those who don`t suffer from mental disorders, mental health app can help track mood and cope with stress. But patients diagnosed with a disorder will need this function for tracking symptoms and progress. The feature monitors the state and feelings to understand the primary triggers for depressive episodes.

Support groups. By sharing their feelings and progress with others in a safe and stable environment, users get more support and understanding. What is more critical, such psychological groups have to be under therapists` control, so all the users must feel safe while using the app.

Sharing option. This function is essential when users feel the need to share progress with their therapists. It works both ways: while patients feel constant professional support, counselors control patients` state remotely and, therefore, timely address emergencies. Besides, some users need family support, and sharing feature helps in the case. 

All-kinds-of-messaging forms and chats. This function allows users to choose the most comfortable way of communication with the therapist, as some might feel anxious because of phone calls, for example. 

Notifications. The function is for the users who don’t have enough internal resources to remember about the necessity of mood monitoring. These notifications will encourage them to make a record about feelings or practice relaxation to maintain a balanced psychological state. At the same time, it is worth customizing the notifications settings not to get one more source of stress. 

Client-therapist match. AI-based counseling tool can recommend a suitable therapist based on the information users provide. This function is helpful for users who don’t know how to choose a perfect-match specialist.

Therapists` dashboard. The feature plays an advisory role for patients and their counselors. Here, therapists manage the cases, track on the progress, and store the information conveniently in one profile. 

More broadly, the scalability of AI-based counseling apps is both an advantage and a disadvantage. Like in the case of drug elaboration, we will need precise monitoring to ensure the app safety and effectiveness of treatment protocols.

Security. At the same time, AI technology might impose specific confidentiality issues. Accessing the sensitive details about each patient is a whole new type of risk, especially when those details involve the user’s data from social media. Developers should apply risk minimization solutions from the start, such as storing a minimum amount of user’s sensitive information, regular cleaning session records after they are analyzed, and encrypting data on the server.

Test groups. AI-based mental solution providers must also recognize its limitations, such as the tendency of machine learning to represent one population group. For example, if an AI-based counseling app, which uses speech to recognize mental illness, is trained on one particular demographic group speech samples, then working with patients outside that group might lead to false alarms. To avoid these risks, developers must recognize this trend and adhere to the strict standards applied in clinical research. Test groups have to include a broad population representation. 

Tool vs. personal. Someone may blame the digitalization of mental health services for being impersonal, but it is AI-specific anonymity that, in some cases, turns out to be a positive feature. Patients who feel discomfort when telling about their problems to an unfamiliar therapist communicate with AI-based tools with lower anxiety levels. Due to these advantages, AI-based mental counseling apps can help in recognition of undiagnosed mental disorders, accelerate the necessary treatment, and increase the chances of its favorable outcome.

After all, the future of AI-based counseling tools is promising: it will help therapists and patients work together by providing daily data, quickly identify alarming symptoms, and notify the therapist about emergency remotely.