AI Is Here to Stay: Adopting AI for Behavioral Health Service Line Strategy
Editor’s note: Associate Consultant Catherine Hjerpe and Principal Jayme Zage, PhD, contributed to this post.
Artificial intelligence–based solutions have quickly moved from being a novelty to providing novel solutions to complex health care challenges. And while these solutions may only be scratching the surface of the value they will deliver over time, this exponential improvement has positioned artificial intelligence (AI) to disrupt nearly every aspect of the clinical enterprise. In fact, at this very moment, AI is not only already being utilized, but it is also improving clinical care across service lines.
Let’s take a look at how the behavioral health service line is utilizing AI applications today.
Behavioral Health: AI Aims to Increase Efficiency, Effectiveness and Access
The demand for behavioral health services is rising, and physician shortages are increasing—the combination of which are driving researchers to investigate how AI can increase access and improve efficiency of mental health care.
Currently, researchers at University of Colorado Boulder are applying AI to psychiatry through a speech-based mobile app to detect speech changes that may show mental health decline, such as those that can occur with schizophrenia or depression. The researchers envision these types of AI tools providing mental health professionals with data-driven insights, as well as a way to keep an eye on patients from hundreds of miles away. Already in use, the BioBase app by BioBeats tracks physiological data to learn how people’s behaviors impact their well-being and then offers advice and personalized courses for coping with mental health stressors. These approaches focus not only on increasing earlier behavioral health interventions and proactively deploying behavioral health resources, but—more importantly—on increasing access to mental health services by allowing patients to be remotely monitored by clinicians and providing them with personalized resources for coping with mental health issues on their own.
But what about the efficacy of behavioral health treatment? Taliaz, an Israel-based company, has developed an algorithm that analyzes a patient’s DNA and mental health history, along with additional patient data, to predict the efficacy and potential adverse reactions of antidepressants with 80% accuracy. As a result, mental health specialists will be able to take a more data-driven approach to diagnosing and treating mental health conditions, which means patients will be more likely to benefit from the initial course of treatment. This more targeted approach will cut down on the number of patient visits required to establish the correct course of treatment and, ultimately, increase the efficiency and effectiveness of behavioral health care.
Service Line AI Adoption Recommendations
Overall, organizations must evaluate existing operations and practices before introducing AI and understand that to realize the maximum benefits of any emerging technology, a significant care redesign effort is necessary. It is essential to create proactive, supportive and consistent care pathways that clinicians can use to guide interventions when AI identifies patients who could benefit from preventive or immediate health care.
When it comes to services lines specifically, however, Sg2 recommends:
- Avoid unintended consequences of AI adoption. In mental health, this may take the form of driving additional demand that cannot be supported, which negatively impacts access to care.
- Align your AI utilization to service line–level priorities within your organization. For behavioral health, improve access to care and aid in triaging.
- Deploy a portfolio approach to your AI strategy. There will be some AI solutions that are more administratively oriented, clinically driven or operationally impactful. Additionally, various AI solutions have variable financial impacts. Structure your AI strategy to ensure you are balancing AI solutions that drive revenue or cost savings with those that may be an expense to the system but are critically important to delivering efficient, effective clinical care.
As AI has grown in sophistication, it has quickly become one of the most widely discussed, analyzed and invested-in technologies in the health care industry. It has also created a very challenging and complex environment for health care leaders who are attempting to determine when to invest in AI and how to develop their organizations’ AI capabilities.
Reach out to Sg2 for help with your AI strategy, and stay tuned for more from us on service lines and AI.