Machine Learning and Healthcare: Advances in Behavioral Health

Healthcare has long been a central hub for technological innovation, with providers across specialties relying on the latest tools to continuously deliver better experiences and outcomes. Equipped with electronic health records (EHRs), diagnostic and clinical decision support tools, and advanced treatment technologies, providers can offer even more personalized, effective care — and this is especially apparent in behavioral health.

The ongoing expansion and use of artificial intelligence (AI) offers behavioral health providers new opportunities for further enhancing the provision of care. Backed by machine learning algorithms, many AI solutions can improve both diagnosis and treatment, enabling providers to better assess and manage mental health conditions, substance use disorders, and intellectual or developmental disabilities (IDD).

With machine learning and healthcare rapidly evolving, it’s essential for providers to understand how these technologies can augment and supplement their work.

What Is Machine Learning in Behavioral Health? 

Machine learning is a specific part of the AI umbrella that primarily analyzes large datasets to identify patterns, classify data, and make predictions. Once patterns are identified, the algorithm remembers the pattern and applies the result to data presented in the future. A recent review of machine learning and behavioral healthcare found that within behavioral health, these solutions typically employ three types of learning:

• Supervised learning, which evaluates data to predict health outcomes. 
• Unsupervised learning, which uses unlabeled data to train machines. This model learns from the data, discovers the patterns and features in the data, and returns the output.   
• Reinforcement, which trains the machine to take suitable actions and maximize its rewards based on the specific environment it is acting in, e.g., a robot selecting the correct peg to put in a hole.
  

Thanks to its ability to identify and, in some cases, act on data-informed patterns, machine learning offers significant opportunities for providers to improve individual care journeys and population health research. 

AI in Mental Health, Substance Use, and IDD Diagnostics

One of the most prominent and promising opportunities that machine learning offers behavioral healthcare is advancing diagnostic efficacy. Providers and researchers have been using machine learning solutions to help inform earlier and more accurate diagnoses, which enable more timely interventions and preventive care such as educating and counseling patients on health and lifestyle changes, monitoring and adjusting treatment plans, and managing the progression of disorders — even slowing that progression down, reversing it, or preventing disease onset altogether.

A 2021 study, for example, used machine learning and medical diagnostic AI to examine questionnaires from 700 participants to identify around 30 variables that strongly predict the prevalence of future substance use disorders. Other AI in mental health solutions are helping identify characteristics that can proactively help in the accurate diagnosis of conditions like schizophrenia or Fragile X syndrome for inherited intellectual disabilities. 

Medical diagnostic AI solutions like these work by training on large datasets to find patterns and organize disorganized data. Then, the models use these insights to predict the likelihood of future conditions based on specific characteristics, thus helping providers diagnose behavioral health disorders as early as possible. 

Improving Treatment Quality With Machine Learning

In addition to enhancing diagnostics, machine learning can also support individual treatment and care plans. Solutions like Core Clinician Assist: Symptom Tracking empowers providers by both tracking an individual’s symptoms over time and connecting those symptoms with associated diagnoses. Such a solution can scan provider notes at high speed to surface hard-to-find symptoms, aiding in providers’ abilities to target care to their clients’ unique needs.

Other services employ machine learning to analyze an individual’s mental health needs via a questionnaire and then match that person with an appropriate therapist.

Further research on machine learning’s ability to better ensure high-quality treatment is driving encouraging results. One research team is using deep learning solutions to improve imaging procedures, reducing the time it takes to complete some scans from 10 minutes to one or two minutes. Making the imaging experience faster and more comfortable, these researchers say, will be critical to ensuring clients feel safer and more comfortable throughout their treatment journey. 

Artificial Intelligence and Population Health

Behavioral health providers are increasingly focusing on offering equitable and effective care by identifying both an individual’s needs and larger population-based trends. To better recognize and analyze these social determinants of health (SDOH) or health-related social needs (HRSNs) — the economic, social, and environmental conditions in which people live — providers can turn to advanced machine learning solutions like Core’s SDOH Tracking algorithm. This machine learning and healthcare tool — which, alongside Core’s Symptom and Diagnosis Tracking algorithm, is offered as both a feature in the Cx360 platform and a standalone algorithm available via application programming interface (API) — examines SDOH to help predict health risks for individuals and populations.

Pinpointing SDOH, undiagnosed chronic diseases, and population risk with machine learning solutions like those from Core enables behavioral health providers to design better interventions and allocate resources more effectively across populations. It’s also helping providers more acutely understand both their clients’ conditions and their future needs.

A Georgia Tech artificial intelligence and population health study, for example, examined nearly 1.5 million Reddit posts of individuals with substance use disorders. The AI solution was able to identify not only what kind of alternative treatments people were using to address opioid addictions, but also how they’re using those treatments. Other studies are using machine learning and healthcare tools to find key — but difficult-to-identify — features associated with Down syndrome and gene mutations associated with various developmental disabilities.

The Future of AI in Healthcare

As machine learning and healthcare tools evolve, behavioral health providers seeking to optimize their care delivery and operations will have increasingly accurate and helpful solutions at their fingertips. With the advancement of AI research, they’ll also be better equipped to use them.

Leading solutions, like Core’s Cx360 platform with embedded AI tools, are at the forefront of this work. On top of tracking symptoms and SDOH, this platform includes several AI and mental health, substance use, and IDD features that enhance clinical care and backend processes: Ambient dictation analyzes provider notes, revenue cycle management tools create smarter billing processes, and anomaly detection spots clinical problems before they impact clients.

There’s even more good news: These cutting-edge tools are only the beginning. Like other AI and machine learning solutions, Core’s platform is rapidly advancing, giving providers more time and resources for doing what they do best: caring for clients.

Contact Core to learn more about the innovative AI tools that can help you improve your clients’ experiences and outcomes.