Artificial intelligence (AI) is reshaping the landscape of behavioral health by offering innovative solutions to longstanding challenges. From simplifying administrative tasks to improving client care, AI has the potential to further enhance the way we deliver mental health services.
“Artificial intelligence is about transforming raw data into usable information by digitizing, counting, and collecting it in meaningful ways.”
-Nicholas Chepesiuk, VP of Virtual Care, Qualifacts
In this article, we’ll explore the role of large language models (LLMs) in advancing AI, examine the current applications of AI for behavioral healthcare, and address common concerns surrounding their use.
Understanding What AI Can do For Behavioral Health Clinicians
AI programs execute tasks that traditionally required human intellect, like learning and problem-solving. At its core, AI is made of algorithms and models capable of analyzing data, recognizing patterns, and making predictions or decisions.
Expanding what AI can do for behavioral health clinicians is especially promising given the abundance of written data that this field generates, primarily in the form of client narratives, clinical notes, progress reports and treatment plans. This rich linguistic data provides AI models with a unique opportunity to learn patterns, identify trends, and extract valuable insights that can enhance clinical practice.
AI’s ability to process and analyze unstructured data can offer several benefits:
- Improved data extraction: Quickly extract relevant information from clinical notes, reducing the time-consuming task of manual review.
- Enhanced documentation efficiency: Automate routine documentation tasks, such as note generation and summarization, allowing clinicians to focus more directly on their clients.
- Standardized documentation: Ensure consistent and accurate documentation, improving compliance with clinical guidelines and regulatory requirements.
“Clinicians in behavioral healthcare live in words. We’re writing documents, we’re writing notes, and AI allows us to use all this information combined with other structured data in ways that create a much more robust and much faster way of getting evidence-based protocols.”
-Carol Clayton, Ph.D., Senior Associate, OPEN MINDS
What are LLM in Behavioral Health?
Large Language Models (LLMs) are a type of AI specifically designed to understand and generate human language. They are trained on massive datasets of text and code, allowing them to learn complex patterns and relationships within language. This enables LLMs to perform certain tasks like summarizing large amounts of text or generating paragraphs based on simple notes. The behavioral health industry has the highest use of language documentation of any specialty in healthcare, making it a particularly promising area for the application of this technology.
LLMs are currently used for tasks such as summarizing clinical notes so that providers can focus their attention on clients. Thanks to widespread commercial adoption and interest in this technology, AI note-writing capabilities have continued to advance significantly in the last few years. With further adoption across behavioral health and other sectors, AI capabilities will continue to improve from where they are today.
“A key goal of our data strategy, which is now evolving into an AI strategy, is to centralize our data in one accessible place. This will help us achieve our strategic objectives and fulfill our mission with Brightly.”
-Andrew Schewend, Chief Strategy Officer, Brightly
What are the major concerns about AI in behavioral health?
While AI offers significant benefits for the behavioral health industry, it is important to acknowledge and address the concerns associated with its use and how to use the technology responsibly. This includes:
- Security: LLMs handle sensitive client data, making it crucial to ensure robust security measures are in place to protect against unauthorized access and data breaches.
- Data privacy: Protecting client privacy is paramount in behavioral health. AI systems must be designed and implemented with strong data privacy safeguards to ensure all information is handled responsibly and meets HIPAA guidelines.
- Clinical validation and accuracy: It is essential to validate AI models to ensure their accuracy and reliability in clinical settings. Rigorous testing and evaluation are necessary to ensure that AI-powered tools provide accurate and trustworthy results.
- Clinical companion, not clinical replacement: AI should be viewed as a valuable tool to augment human capabilities, not as a replacement for human clinicians. It is important to maintain a human-centered approach to care, with AI serving as a supportive and complementary technology.
“Clinicians in behavioral healthcare live in words. We’re writing documents, we’re writing notes, and AI allows us to use all this information combined with other structured data in ways that create a much more robust and much faster way of getting evidence-based protocols.”
“Artificial intelligence is about transforming raw data into usable information by digitizing, counting, and collecting it in meaningful ways.”
“A key goal of our data strategy, which is now evolving into an AI strategy, is to centralize our data in one accessible place. This will help us achieve our strategic objectives and fulfill our mission with Brightly.”
How AI Can Enhance Clinical Productivity and Administrative Efficiency
Ambient Documentation
One of the most promising applications of AI in behavioral health is ambient documentation. This technology uses microphones to capture audio recordings of clinical interactions, which are then automatically transcribed using AI.
Ambient documentation enables clinicians to concentrate solely on their clients, fostering deeper therapeutic connections. By eliminating the distraction of notetaking, clinicians can actively engage in meaningful conversations and offer personalized care.
This shift in focus can significantly enhance client outcomes and satisfaction. When clinicians can establish genuine connections with their clients, they are better equipped to understand their individual needs, deliver targeted interventions, and cultivate trust, leading to better health outcomes.
Notetaking Support
AI-powered notetaking tools can significantly reduce the time spent on documentation tasks, such as progress or process notes, allowing clinicians to focus more on client care and less time worrying about administrative tasks. These tools can take transcriptions from an encounter, or a summary written by the provider to generate official documentation notes in various formats, such as BIRP, DAP, and SOAP. This is helpful for keeping documentation consistent within an organization, keeping insurance claim approval rates high.
By automating the process of note generation, AI can help clinicians to document their work more efficiently and accurately, reducing the risk of errors and omissions. This not only improves the quality of documentation but also helps to alleviate clinician burnout by reducing the time spent on administrative tasks and minimizing the need for working after hours.
What’s next in AI for Behavioral Healthcare?
The integration of AI into EHRs represents the next frontier in behavioral healthcare. By seamlessly incorporating AI-powered tools into EHR systems, we can unlock even greater potential for improving client outcomes and simplifying workflows. As AI continues to advance and become more sophisticated, its potential to transform behavioral health care will only grow, offering new opportunities for improving client outcomes and enhancing the efficiency of clinical workflows.
Moving away from fragmented point solution vendors and towards AI companions integrated within the core technology ecosystem is essential for realizing the full benefits of AI for behavioral health. This approach will ensure that AI tools are integrated with existing workflows and data systems, maximizing their effectiveness.
To learn more about what AI can do for behavioral health clinicians and how Qualifacts is at the forefront of this shift, we invite you to watch our webinar on the topic.
