Artificial intelligence (AI) has been rapidly growing and advancing in recent years and has already started to impact health care, including the treatment of substance use disorders (SUDs). While there are trends in AI that are currently shaping the health care industry, this article will focus on two in relation to substance use disorder treatment: machine learning and predictive analytics.
Machine learning is an AI that allows computer systems to automatically learn and improve from experience without being explicitly programmed. In health care, machine learning is being used to analyze vast amounts of patient data, including medical records and images, to identify patterns and provide personalized treatment plans. Each individual with a substance use disorder is unique in the biopsychosocial presentation.
Machine learning enables the individual’s unique characteristics to be the driving force in their care. The impact on health equity alone is astounding as, historically, health research has largely focused on white men, which has resulted in a significant lack of diversity in clinical trials and research studies. This lack of diversity can have significant implications for developing and implementing treatments and interventions, as different populations may respond differently to various interventions.
Predictive analytics, which involves using AI and machine learning algorithms to analyze large data sets to identify patterns and make predictions about future events, also contributes to the personalization of SUD treatment. Rather than a cookie-cutter approach based on group norms, which we know from above has been restrictive, data unique to the individual is used to predict health patterns and risk of crisis events, enabling proactive interventions to avert health crises.
SUDs can be challenging to manage and treat, and traditional approaches often fall short. These focus predominantly on reactive responses, putting the onus on struggling individuals to take a proactive approach. This is where AI comes in; when a clinician meets with a patient, for example, they have only a small window into how that individual is doing, a few of the puzzle pieces, when all of them complete the whole person. Even with their desperate best efforts, the patient may find it challenging to have an accurate picture of critical pieces of information about themselves, and the clinician, even with an initial appointment survey, can only understand so much about the status of that individual.
Without a clear picture, it is challenging for providers to offer personalized care that is urgently needed and for the person to better manage their disease or disorder. This is particularly challenging with mental health, including substance use and addiction, where complex internal and external factors contribute significantly to how individuals are faring. AI can augment our ability to perceive, analyze and respond with greater speed, accuracy and pattern detection, enabling us to improve care efficacy and outcomes without additional time and effort on the part of the individuals providing care. Artificial intelligence takes all of the puzzle piece inputs and helps piece them together to provide the output, a clearer picture that can tell us what’s going on and help direct the response.
For example, Behaivior’s proprietary Recovery™ platform utilizes AI, behavioral health technology and wearables to predict and prevent return to use for individuals in or pursuing recovery from a substance use disorder. Utilizing wearable devices and pattern-recognition machine learning algorithms, Recovery™ streams real-time data to care providers and screens individuals for physiological and behavioral warning signs they are at increased risk for use or return to use, which enables the opportunity for immediate intervention and support.
In addition to eliminating health disparities by personalizing care, Behaivior’s Recovery™ platform is also helping care providers with patient retention and risk management. Behavioral health crises can be challenging for care providers to manage, and it is essential to have access to real-time data and insights. Behaivior’s Recovery™ platform provides care providers with the information they need to help manage patient risk and improve outcomes. This technology helps care providers build stronger relationships with their patients or clients, ultimately leading to better health outcomes.
It’s clear that AI presents significant opportunities for health care providers to enhance person-centered and evidence-based care. From more personalized care planning to preventing return to use, AI is revolutionizing the way we approach the management of SUDs. With continued investment and development, AI has the potential to transform how we approach care for people with SUDs and wellness overall, including improving outcomes for the entire health care ecosystem.