Successful medical treatment starts with an early and accurate diagnosis. It’s the key to developing the right care plans and achieving better outcomes.
But in the behavioral healthcare space, providers face acute challenges when diagnosing mental health conditions, substance use disorders, or intellectual or developmental disabilities (IDD). Clients often present with highly subjective symptoms or, for various reasons, are not able to clearly explain those symptoms, making it difficult for their provider to determine the right diagnosis.
In the primary care and medical system space, the challenges are even more acute. Many types and levels of providers, including nurses, physicians, counselors, home health workers, care managers and others, are entering data at different points in time in the patient's treatment. A provider is not able to take the time to go through all notes over an extended period to ascertain behavioral health and social determinants of health (SDOH) data.
That’s why many providers are employing artificial intelligence (AI) tools to supplement their work. AI diagnosis and symptom tracking solutions can analyze data from a variety of sources, including clinical notes from electronic health records, to help providers better understand the symptoms clients are experiencing. Providers can then use the insights drawn from these AI-powered aids to diagnose conditions earlier and more accurately — improving outcomes and saving more lives in the process.
There are many factors that make diagnosing behavioral health conditions challenging. Mental health conditions, one study found, are complex and strongly associated with a client’s sociocultural environment. Many people also have co-occurring disorders.
Diagnosing these disorders is highly dependent on client reporting and communication with providers. It’s not guaranteed that the client will always provide a complete picture of what they're feeling or the events that have led them to seek help, so the subjective nature of their experience and the provider’s assessment can lead to a misdiagnosis. In fact, one foundational study revealed that misdiagnosis rates for major depressive disorders reached nearly 66% and those for bipolar disorder was close to 93%. A more recent examination found that 39% of clients with severe psychiatric disorders were misdiagnosed.
Diagnostic processes for substance use disorders are also highly manual, subjective, and time-consuming. Tests like the Drug Abuse Screen Test and the NIDA Drug Use Screening Tool require clients and providers to answer a set of subjective questions to arrive at a diagnosis. Similarly, IDD diagnostics often rely on guardian reports via questionnaires, making it challenging for providers to identify first-hand symptoms.
Emerging AI diagnosis tools offer providers opportunities to reduce the subjectivity often involved in behavioral health diagnostics to improve their accuracy.
Providers are the most essential part of any behavioral healthcare process, but in recent years, staffing shortages have stretched their time with clients thin. At the same time, increasing numbers of people have searched for professional help. As such, interest in AI diagnostics is steadily increasing, as providers across specialties realize the benefits these technologies offer. Let’s look at some of the advantages to using AI clinical decision support tools.
Early diagnostic screening is critical: The sooner a provider can diagnose a behavioral health condition, the better they’re able to implement a treatment plan and manage symptoms before they progress.
AI diagnosis solutions are enabling providers to identify symptoms and conditions more quickly, primarily by analyzing large datasets to find diagnostic patterns. Many mental health therapists, for example, are using AI-based psychological assessment tools to analyze medical histories, provider notes on client behaviors, and other data to drive more precise diagnoses earlier in the clinical process. These solutions can help determine which kinds of therapy would be most effective based on the client’s diagnosis.
Artificial intelligence is also proving to be a powerful resource for substance use disorder diagnoses. Machine learning solutions are able to analyze various data inputs to identify and categorize early signs of addiction.
Behavioral health and medical providers can use AI clinical decision support solutions to improve the efficacy of their diagnoses and care regimens. AI-powered decision support systems (DSS), for example, are machine learning solutions that can detect mental health, substance use, and IDD disorders with an 89% accuracy rate, and then assist providers in making the best clinical decisions possible for their clients.
Researchers in Hawaii are employing similar AI clinical decision support technology to diagnose and treat adolescent developmental delays like autism and ADHD through AI-powered video games.
Technologies like these support providers in not only making earlier, better diagnoses, but also in creating care plans that address each client’s unique needs.
Any behavioral health provider knows that no two clients are the same, even if they present with the same symptoms or live with the same conditions. Addressing each client’s distinct experience can enable providers to tailor treatment plans to them — and AI can assist in this process as well.
Some researchers have used AI and machine learning solutions, for example, to help providers create personalized interventions for clients with IDD and autism, while others are tracking clients with substance use disorders to try to prevent emergencies and provide tailored coaching.
With an AI-based psychological assessment, providers can access more individualized data about their clients, enabling more personalized care.
AI diagnosis and symptom tracking solutions are quickly becoming a central element of advanced behavioral healthcare. They’re supplementing the care process by enabling providers to identify conditions earlier, make more informed clinical decisions, and individualize care plans.
To determine whether AI is right for their practice, providers need to first analyze how diagnostics are functioning with their clients. What’s the practice’s overall diagnostic accuracy rate? What kinds of conditions are proving difficult to identify? Which clients are getting lost in the system due to challenging or inaccurate diagnoses?
Behavioral health and medical providers who identify a need for AI diagnostic tools should turn to leading applications like Core Clinician Assist: Symptom Tracking. This solution uses natural language processing (NLP) to scan provider notes at high speed and scale to identify trending symptoms expressed by the patient at any point in care across a provider organization or a system of care. Additionally, the solution offers:
Behavioral health symptoms trending
Intuitive diagnosis visuals that can aid in the clinical decision-making process
The ability to contextualize issues and understand the socioeconomic circumstances that contribute to treatment challenges with the Core Clinician Assist: Social Determinants of Health Tracking tool
Easy integration with existing electronic health records and care management platforms via industry-standard application programming interfaces (APIs) in accordance with the Cures Act
Diagnosis recommendations associated with each set of identified symptoms (coming soon)
The Symptom Tracking and HRSN Tracking AI solutions are just two of the innovative behavioral health AI solutions developed by Core Solutions. To learn more about how these and other Core applications can help you improve clinical experiences and outcomes, contact us today.