Top 21 AI & Emerging Tech Use Cases for Behavioral Health in 2024

As we move through 2024, artificial intelligence (AI) is unlocking new possibilities in mental health and substance use disorder treatment. Over the past year, I have been working to collect as many AI use cases as possible for AI. The potential is truly immense, but the journey is just beginning. I constantly try to remind myself to tamper my unbridled enthusiasm with a balanced understanding of both its capabilities and limitations.

While many of the use cases highlighted below are still in the early stages of implementation, they illustrate the profound impact AI is and will continue to have on the future of behavioral health.

Top 21 Use Cases:

1. Rapid Assessment

AI can leverage audio, video, and text data to perform quick, comprehensive assessments of a patient’s mental state. By analyzing nuanced signals such as tone of voice, eye movement, fidgeting, and facial expressions, AI can shorten the delivery time of assessments traditionally done in written form such as the PHQ-9, GAD-7 and BARC-10. This multimodal approach allows therapists to identify deviations from baseline and adjust treatment strategies accordingly.

2.????? Crisis Management

AI can enhance crisis management by detecting emotional distress in patient communications through real-time sentiment analysis using multimodal capabilities (voice, video, etc.). This allows for timely crisis intervention and prioritization of high-risk patients. Machine learning algorithms can also identify patients who are at risk of complications, enabling targeted interventions. By measuring distress levels across a patient population, AI can help clinicians prioritize cases based on severity to assist in the prevention of emergency department admissions.

3.????? Clinician Training and Feedback

AI can analyze live therapy sessions, providing therapists with immediate, actionable feedback on their tone, body language, time spent talking versus listening, and even facial expressions. This feedback allows therapists to refine their techniques to increase treatment fidelity (AKA how consistently and accurately a clinician implements a specific treatment as it was intended). By continuously supporting professional development, AI ensures that therapists can offer higher quality care in every session.

4.????? Automated Follow-Up Care

Automatically send follow-up messages and check-in requests to ensure that patients adhere to their treatment plans and appointments. While maintaining regular contact, these AI-powered reminders can be customized to optimize timing and messaging, to increase individual patient engagement rates overtime.

5.????? Precision Medicine

AI’s predictive algorithms are advancing precision medicine by identifying the most effective diagnostic pathways, predict drug benefits and toxicities in medications like buprenorphine, and optimize follow-up care. In analyzing vast amounts of data, one size fits all medicine becomes a thing of the past. Even when it comes to therapeutic intervention and education, visual aids and therapeutic educational content like CBT learning modules commonly found across digital apps and interventions, can now easily be customized to align with a patient’s learning style, pace and progress.

6.????? Continuous Monitoring and Adjustment

AI can continuously monitor patient behaviors and adjust interventions in real time, ensuring that treatment plans are updated regularly to align with ongoing progress and changes in patient preferences. This dynamic approach allows for personalized, adaptive care that evolves alongside the patient, and is responsive to signs of stress or increases in cravings.

7.????? Patient Engagement and Retention

AI-powered asynchronous technology offers flexible scheduling and communication options, which can significantly improve patient engagement. Automated follow-up reminders can be optimized to reach patients at the most effective times, increasing their participation in treatment and follow-up services. This level of engagement can lead to better retention rates and successful outcomes.

8.????? Medication Management

AI can enhance medication management through remote monitoring and wearable devices that track adherence and detect side effects in real time. This ensures that pharmacotherapy is both effective and safe, helping to optimize treatment plans and reduce the risk of recurrence in symptoms that can be associated with medication non-compliance.

9.????? Relapse Prevention

AI can integrate data from wearables, geospatial analysis, multimodal inputs (video, voice, etc.), and historical patient data to anticipate potential elevations in risk, and then alert providers and support networks when patients experience elevated stress or are exposed to potential triggers, such as high-risk locations like a former dealer’s house. This proactive approach can triangulate data that allows for early intervention, helping to prevent a recurrence in symptoms and keep patients on the path to recovery.

10.? Overdose Prevention

Wearable devices (such as Google Watches, Apple Watches, etc.) equipped with pulse detection technology can monitor vital signs in real time to identify potential overdoses. AI can trigger immediate alerts to healthcare providers or emergency services, enabling rapid response and potentially saving lives. This application of AI is a critical advancement in overdose prevention, offering a lifeline to those at risk. While this functionality is currently unavailable in the U.S. due to FDA regulations, Google is rolling this feature out on its Google Watch 3 in Europe this year.

11.? Never Sleep Support

AI-powered chatbots can provide round-the-clock support and crisis intervention, especially useful at night, on the weekends or during holidays when human therapists are unavailable. These virtual chatbots can converse and connect individuals to additional services, aiming to reduce emergency department visits. By being available 24/7, AI ensures that help is always accessible, fitting seamlessly into patients’ lives and providing timely assistance whenever it’s needed.

12.? Reducing Administrative Burden

AI can significantly reduce the administrative burden on healthcare providers by automating routine tasks such as patient intake, record updates and scheduling. Robotic Process Automation (RPA) can streamline workflows, improve care coordination, and ensure that essential tasks like insurance paperwork and patient follow-ups are handled efficiently. This allows clinicians to focus more on patient care and less on paperwork.

13.? Notetaking and Documentation

AI-powered ambient listening technology can automate the process of notetaking and even draft treatment plans based on conversations during therapy sessions. Automated note-taking technologies are experiencing the fastest adoption among behavioral health providers, viewed as a low-risk entry point into AI. By analyzing audio data, AI can capture detailed patient notes and generate clinical documentation without interrupting the flow of the session. This not only saves time but can also improves the accuracy and completeness of medical records.

14.? Scalable Care Solutions

One of AI’s most significant strengths is its ability to scale healthcare solutions, making effective treatment accessible to populations that previously lacked access. By democratizing care, AI can bridge the gap between patients and quality behavioral health services, regardless of their location or socioeconomic status. This scalability seen in things like AI chatbots, remote monitoring technologies, AI-powered apps, and automated referrals ensures that more people can benefit from the latest advances in mental health and substance use disorder treatment.

15.? Uncovering Unknown Patterns

AI’s advanced unsupervised data analysis capabilities can uncover hidden patterns in substance use and mental health trends. By analyzing data across patient notes and subpopulations, AI can identify correlations and trends that might otherwise go unnoticed. This information can then be used to develop more effective prevention and treatment strategies. For example, what are the top 5 predictors of symptom recurrence, such as relapse, in women with alcohol use disorder?

16.? Error Prevention and Fraud Detection

Machine learning algorithms can identify patterns and anomalies in billing, preventing errors and detecting potential fraud. By automatically reviewing incoming faxes and verifying invoice accuracy before payment, AI can accelerate authorization processes and reduce the likelihood of financial discrepancies. This not only improves financial stability but also minimizes administrative burden and liability among under-resourced behavioral health providers.

17.? Financial Health Forecasting

AI can improve the financial health of behavioral health practices by predicting reimbursement cycles, identifying potential delays, and suggesting proactive measures to enhance cash flow management. Adaptive reimbursement models, powered by real-time data, can forecast healthcare costs and assess the risk profiles of patient populations with greater accuracy, ensuring that providers are financially stable and better prepared for the future.

18.? Optimized Resource Allocation

AI can optimize resource allocation by analyzing large datasets to identify staffing gaps, manage inventory, and forecast demand. For example, a generative AI-powered chat assistant could help healthcare administrators determine whether they have enough staff scheduled for upcoming shifts, allowing them to proactively close gaps and make strategic decisions accordingly. This could help monitor employee overtime to prevent staff burnout and better ensure that resources are available when and where they are needed most.

19.? Insurance Determinations

AI can assist in insurance determinations by predicting how long a patient may need care and at what level of intensity. While these predictions should be used to guide care planning rather than deny services, they can help providers allocate resources more effectively. Understanding which patients are at the highest risk of complications or require the most resources allows healthcare providers to act on that risk, ensuring that care is both fair and effective.

20.? Whole-Person Lifestyle Interventions

Large language models such as Claude, Gemini, or ChatGPT can go beyond traditional treatment by offering personalized lifestyle interventions like tailored nutritional advice, brainstorming around new activities, career advice or creating customized daily exercise routines. This planning support can help individuals take actionable steps to build recovery capital and overcome barriers related to social determinants of health, addressing multiple facets of a patient’s life important to achieving long-term recovery.

21.? Customized Self-Motivation Tools

AI can generate personalized creative content, such as motivational songs (my favorite: Udio), inspirational images (my favorite: NightCafe), stories of resilience, or even digital avatars (my favorite: HeyGen) of ourselves repeating positive affirmations. These tools can provide personalized encouragement, helping to prevent burnout among staff and provide ad-hoc emotional support for patients. By crafting narratives that align with a patient’s recovery journey, AI can help individuals visualize their progress, reinforce positive behaviors, and build resilience during challenges.

Here we are! These are the 21 AI use cases I’ve identified for 2024, and I look forward to updating this list in 2025 with your help!

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