NNews December 2023

33 December 2023 Benefits of AI-driven Tools in Behavioral Health • Greater efficiency – Saving providers time with research and documentation • Population management – More consistent use of evidence-based practices across like populations • Improved clinical outcomes – Perhaps in a shorter time span, meaning improved quality of life sooner for the people served • Ability to predict cost – “Length of episode” and “cost of episode” at the onset of the episode, giving us a benchmark of care for which to aim How AI and Machine Learning are used for behavioral health in EHRs today: • Wearables and client devices • Chatbots and personal care • Behavior and mood trackers • Data transformation and analytics • Treatment support According to theWorld Economic Forum, AI is improving mental health therapy by: • Providing quality control – Clinics use AI to analyze the language used in therapy sessions through natural language processing (NLP), a technique throughwhichmachines process transcripts to ensure the delivery of high standards of care. • Refining diagnoses and assigning the right therapists – AI is helping doctors to recognize mental illness earlier and to make more accurate choices in treatment plans. • Monitoring patient progress and altering treatment when necessary – AI can help identify when a treatment change is needed or if it is time for a different therapist. • Justifying cognitive behavioral therapy (CBT) instead of medication. In the behavioral andmental care space, AI can be seen in : • Predicting the risk of relapse • Eliminating negative influences • Tracking treatment • Finding moral support • Starting treatment The Future Direction of AI in Behavioral Health Many behavioral healthcare providers are not yet ready to accept AI andmachine learning as resources for delivering services. Some see themas removing the human element from care. AI takes advantage of all data collected over time, analyzes it and elevates it to a point where it is useful by indicating patterns and predictive outcomes that allowus to learn fromour past. As with all data, what you get out is only as good as what you put in, so if data entered is biased or subjective, the AI suggestions would be, as well. Although we cannot reach complete objectivity, there is a lot to be learned from experience captured in our databases. AI is a tool in a toolbox. It does not take the place of provider experience and intuition; it aids it with objective, real-time data for better clinical decisionmaking. Please contact Qualifacts today at info@qualifacts.com to learn more