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Implementing AI and Machine Learning in Behavioral Health Management

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We’re just at the cusp of some of the most fascinating and cutting-edge times in technology. The rise of artificial intelligence (AI) and machine learning (MI) have fundamentally impacted nearly every industry, and continued advancements will continue to accelerate worldwide invocation. 

With that, there is an undeniable intersection between technology and behavioral healthcare management. So, what will the future hold, and how will this affect current clinicians and their practices? Let’s get into what you need to know. 

Artificial Intelligence Vs. Machine Learning: Similarities and Differences

AI is a broad umbrella term that refers to how machines and computers engage in tasks that once required human intelligence. It’s no secret that AI has changed the landscape of nearly every modern field, and it’s likely that continued advances will continue evolving how companies operate. 

Machine learning (ML) is a specific type of AI that refers to how algorithms or statistical models shape how computers make decisions. ML can support performance without explicit programming, and this is done via either supervised, unsupervised, or reinforced learning. ML is responsible for trends like facial recognition, predictive text, and speech recognition.

ML is a significant component of many AI applications, and it’s still in its infancy stages. When done correctly, it allows companies to make accurate predictions and choices based on summarized data. For this reason alone, it’s become an essential part of AI systems.

Artificial Intelligence in Behavioral Health Management

AI has dramatically changed behavioral health management, and those changes have coincided with some fear and criticism. While these responses are valid (nobody likes the potential threat of a robot doing their job!), it’s equally important to consider the many benefits of AI. AI tools can augment healthcare professionals’ practices and support multifaceted processes, including assessment, diagnosis, and intervention planning. 

Customized treatment planning: Comprehensive treatment planning is the heart of effective healthcare. With that, AI can help clinicians create individualized plans that consider a client’s full scope of emotional needs, presenting issues, and medical history into account. The algorithms can also review historical client information to provide recommendations on treatment strategies. In-network clinicians may note that using AI can help them develop effective notes and treatment plans that adhere to each insurance provider’s requirements.

Advanced data analytics: AI can analyze significant databases to assess behavioral health trends. These tools can provide fast, efficient results, and such findings can help providers make informed decisions about their own interventions. Such strategizing may be particularly useful for newer companies or management teams looking to set policies in place. 

Increased accessibility: It’s no secret that there’s a shortage of effective mental health providers, and we have seen this shortage truly come to light since the COVID-19 pandemic. The widespread, mainstream use of virtual support has improved accessibility to treatment services for clients of all demographics.

Medication management: Medication adherence is also an essential part of treatment. AI can help with all features of this process, including sending clients automatic medication reminders, tracking compliance, and alerting providers if people deviate from their prescriptions. 

Machine Learning in Behavioral Health Management 

Machine learning supports behavioral health management by increasing the accuracy and execution of services. Here are some ways providers are taking advantage of this technology:

Earlier detection: ML algorithms can screen and analyze complete patient histories via their EHRs, self-reports, and old assessments. This information can then be used to predict potential diagnoses.

Predictive modeling: Predictive modeling refers to sourcing known genetics and environmental variables to present potential mental health risk factors. A prediction is not a guarantee, but it can offer a roadmap for clinicians and clients alike.

Natural language processing: Natural language processing (NLP) refers to how machines can interpret various text or speech patterns to increase awareness of one’s mental health. 

Personalized technology: Many app companies are using ML to create customized mental health apps that tailor their content based on a user’s progress or relevant needs.

Crisis intervention: Virtual assistants and chatbots created by ML technology can be instructed to provide immediate crisis intervention to high-risk individuals. This does not replace clinicians, but it can add supplemental support when time is of the essence.

Individualized interventions: ML can support mental health providers to conceptualize the best interventions for their treatment. These interventions may be predicted by data-driven insight, where the ML gleans information from various patterns and presented risk factors.

Getting Started at Navix Health 

At Navix Health, we pride ourselves on being industry pioneers in applying our one-of-a-kind Navix AI to mental health. Our AI tools offer comprehensive support to improve clinical documentation from intake to discharge. This software can be personalized to fit your specific needs, and it is always designed with simplicity in mind. 

Navix Health offers comprehensive practice management software consisting of our dynamic electronic medical record system, top-notch customer relationship management (CRM), and email suite that will streamline your healthcare practice. We offer a 24/7 support team for all our clients- this allows you to focus on what you do best. 

Contact us today to schedule your custom demo.

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