Artificial intelligence (AI) is transforming the healthcare industry. Machine-learning algorithms and software are now performing tasks that were once exclusively reserved for human beings—with profound implications.
Nowhere is this truer than in the realm of data analytics. Today the use of AI in gathering and interpreting data can help us more accurately detect cancer in its earliest stages, identify and develop new pharmaceutical drugs, and improve the effectiveness of preventative health interventions; and these are just a few examples of how AI-informed data analytics is revolutionizing healthcare.
What AI Technologies Can Do
AI technologies like machine learning, natural language processing capabilities, and predictive analytics can improve outcomes in the behavioral health field, too. Machine learning is one of the more common forms of AI; using statistics, it fits models to data and trains these models so that it can learn from them to make predictions. Natural language processing applies a statistical and/or semantic approach to speech recognition, text analysis, and translation; this makes it possible to interpret and classify electronic health data such as clinical notes, patient assessments, and patient interactions. Predictive analytics is the use of data, statistical algorithms, and machine learning to forecast outcomes.
What follows are just a few of the ways that these technologies can improve behavioral healthcare outcomes.
Better Delivery of Care
AI technologies that gather data on users’ actions and employ machine learning, natural language processing capabilities, and/or predictive analytics can improve behavioral health providers’ delivery of care. These advanced capabilities can provide a “feedback loop.” This feedback loop allows healthcare administrators to sift through reams of data to determine how delivery of care needs to improve. Depending on the platform, they might even receive specific recommendations about what changes they should implement.
For example, a system like AI-powered Salesforce analyzes the data gathered during the course of treatment against historical trends and other sources. Based on this observed data and analysis, it can predict outcomes and recommend specific actions. This feedback is a “loop,” in that this process of gathering and interpreting data repeats itself—and as it repeats itself, it refines its recommendations based on new incoming data.
AI can also help to ensure timely delivery of care, by allowing healthcare administrators to analyze inquiries regarding appointments or admissions and then more accurately forecast the need for providers.
More Administrative Efficiency
Some of these same forecasting capabilities can also increase administrative efficiency. First, by detecting seasonal and other patterns in the demand for services, they help healthcare organizations staff appropriately for changes in their patient census. A bit like a crystal ball, AI can determine based on the type of incoming call whether the patient will admit in three days or five days. AI can do this forecasting with more depth of information and forecast further into the future because it has more information than what humans can carry in their brain.
Second, the use of AI can ensure that providers’ time is being used optimally. For example, with AI-powered data analysis, we can take a deep dive into our providers’ appointment schedules and make recommendations about the best length of appointment or grouping of appointments. This way providers are being utilized at their maximum capacity when they are on site.
At the same time, with the added scheduling intelligence of AI, we can reduce the problem of provider burnout and high staff turnover. (High burnout rates among therapists and psychiatrists have historically been a problem in the behavioral health industry, but a 2021 report revealed how much worse the problem is now, thanks to the pandemic.)
With electronic health records (EHRs) that have an integrated AI software, it is also possible to analyze the data, look for patterns, and program suggestions based on the data (for patient care, scheduling, etc.). For instance, the platform can be instructed to make adjustments based on level of care, location of patients in the facility, and other criteria.
Improved Patient Outcomes
Patient outcomes also improve in at least three key ways with the application of AI to analyze data.
Care That Targets Individualized Treatment Needs
AI enables an organization to look at its successes with certain types of patients who may be achieving positive life outcomes and a reduction in symptoms. AI can chart data patterns to determine what interventions may be working well for these patients and other factors.
Similarly, if certain patients do not show a reduction in anxiety, depression, and/or cravings, for example, AI can chart data patterns to determine what types of patients those are. This way an organization can further refine care for the particular needs of this demographic. If the young female heroin addict is not achieving a positive outcome, we can revise our programming to make it more targeted to her treatment needs. Then we can monitor through AI whether those new interventions are improving the patient outcome.
More Accurate Diagnosis
AI can also be programmed to diagnose addiction and mental health conditions with greater accuracy. For instance, its diagnostic capabilities might help a psychiatrist or clinician differentiate symptoms of drug-induced psychosis from schizophrenia. (Because the two conditions share key symptoms in common, they can be harder to diagnose.) Greater accuracy of diagnosis helps to ensure that the patient is receiving the right treatment as soon as possible.
Better Long-Term Preventative Health
After discharge from treatment, the priority for patients is to stay healthy and avoid relapse in the long term. Here, too, AI can support better outcomes, by collecting data on patient status and identifying patterns in that data that can clarify whether intervention needs to happen. Say, for example, that a mental health patient begins to miss medication appointments. AI can recognize this pattern and forecast relapse, thereby making early intervention more possible.
Ultimately, AI will only work as well as the data that you put in. This limitation notwithstanding, the better patient outcomes, delivery of care, and administrative efficiency (among other advantages) help to explain why AI is the future of data analysis in behavioral healthcare.