Find out how artificial intelligence and data analytics can help solve current issues in mental healthcare.
Mental health is an important part of a person’s overall health and well-being. A mental health crisis can be not only a social problem but also an economic one for a country, as people with mental disorders have low productivity at work. In addition, unpaid informal caregivers who look after people with mental disorders incur significant costs.
According to the Centers for Disease Control and Prevention (CDC), more than one in five adult Americans with a mental illness, and about one in twenty-five adults has such serious mental illnesses as major depression, bipolar disorder, etc. At the same time, in the UK, one in four adults has at least diagnosable mental health problem in any given year.
Factors that hinder the solution include the high cost of psychotherapy and the shortage of mental health professionals. People are also reluctant to seek mental health help because they feel ashamed that their employers, neighbors, family, and friends may reveal their problems. Ethnic, bisexual, and non-binary minority groups may fear that if they seek mental health help, they will face prejudice and judgment from healthcare providers and society.
These and many other mental healthcare issues are acute and urgent today. The data analytics system with integrated AI can improve services for patients with various mental disorders.
What is AI-Enabled Healthcare Data Analytics?
Modern big data analytics solutions for healthcare use AI and machine learning (ML) to process historical data and make predictions. Traditionally AI analyzes historical and real-time data and makes predictions based only on the strategies it is programmed for.
There is another variant of artificial intelligence: generative AI. Unlike traditional AI, it creates statistically probable outputs based on unstructured data in response to a user prompt or question. Part of generative AI is a large language model (LLM). For a specific healthcare organization to effectively apply LLM technology to its business case, the LLM is trained on this organization’s data sets. So AI can generate customized answers.
Belitsoft experts define two types of LLM training: the retrieval-augmented generation (RAG) approach and fine-tuning. If fine-tuned LLMs are trained in the domain with dynamic data, this information may become outdated. In the case of RAG, LLM performance is higher since a search mechanism is added to the traditional generative capabilities of AI. With RAG, the system searches for information in large databases, selects the most suitable information for the request from the found information, and generates a new text based on it.
Developers use RAG for chatbots in the mental health field. These chatbots can quickly retrieve relevant info from clinical recommendations of mental health specialists and medical records via RAG.
First, friendly chatbots encourage users to describe themselves in their own words. Then, these chatbots process audio, video, and text messages through the AI-powered data analytics software and combine the answers to these questions. Third, they analyze mood, energy, and concentration symptoms in real time. Finally, they provide quantitative risk scores and other insights into user mental health.
How Can AI and Health Data Analytics Improve Patient Mental Health?
Help Users Cope with Symptoms of Anxiety and Depression
Patients often find chatbot responses to be higher quality and more than doctor responses. AI technology learns from a training dataset to adapt to the personality of its users and tailors further dialogue to carry out a series of therapies and speech exercises with them. Chatbots are also able to provide emotional support on demand at any time when a patient experiences anxiety or panic attacks. They analyze the symptoms the patient enters and look for keywords that can trigger a referral and direct contact with a human mental healthcare specialist.
Monitor the Patients’ Condition
Biobeat solution customers use wrist or chest devices with an AI system to collect data for early detection of deterioration in condition. The devices are equipped with a patented PPG sensor that can measure vital signs in real-time, such as pulse pressure, blood saturation, cardiac output, etc.
Early warning scale thresholds are configured for each patient and scenario. When the vital signs change, the system notifies them. For example, the device records that a person has slept little or has received physical or psycho-emotional stress. The analytics algorithms combine vital signs with digital therapy that measures the user’s cognitive functions and mood. The AI solution for healthcare can calculate a personal well-being score. This score can change throughout the day and is unique to each user. The analytics system compares this data with anonymous aggregated data from other users and provides users with predictive warnings that their mental well-being may be about to deteriorate. Alerted patients can take whatever action they feel is necessary in this situation: change their behavior or seek help from health services.
Identify Signs of Potential Mental Health Problems
The Alan Turing Institute implements a significant that uses AI to predict mental health problems. The system compares brain scans of people who suspect they may have dementia with scans of thousands of patients with confirmed dementia. The analytics algorithms can identify patterns in scans that even experienced neurologists might miss. Then AI compares them with the treatment results of patients in its database. The researchers conducted preclinical trials. In these trials, the AI could recognize dementia several years before symptoms appeared. By the way, the AI could make an insightful conclusion also on scans that did not show obvious signs of dementia. Early diagnosis can significantly improve patient treatment outcomes and slow the progression of the disease.
Make Mental Health Support More Accessible to People
A British study has a new AI-powered self-referral tool – the conversational chatbot Limbic. The chatbot has increased the number of people using the National Health Service (NHS) talking therapy services across England. There has been an increase in ethnic minority, bisexual, and non-binary groups.
The chatbot analytics algorithms collect user data and use algorithms to stratify at-risk patients. The chatbot uses questionnaires with a minimal set of data, determines the user’s condition, and identifies risks and people in crisis. With the data obtained, NHS mental health services can prioritize those who need mental health care the most. In addition, users who voluntarily seek help through a chatbot are less likely to interrupt treatment in the future. The psychological support team spends time more rationally and reduces burnout.
Bottom Line
Data analytics with integrated AI is a contemporary technology that can help people improve mental health. Providers who use it can detect mental illness early, customize treatment plans, or organize personalized interventions.