The behavioral health landscape faces a number of significant challenges, primarily stemming from a severe shortage of providers and an increasing demand for services. As seen in recent years, the need for behavioral health has skyrocketed across all demographic groups.
This imbalance between supply and demand has resulted in long wait times, difficulty accessing care, and in some cases, patients not receiving necessary treatment.
Andy Flanagan is CEO of Iris Telehealth, a provider of telepsychiatry services and technology. He holds a Master of Science in Health Informatics from Northwestern University Feinberg School of Medicine. His previous experience includes three stints as CEO, as well as founding a SaaS company and holding senior positions at Siemens Healthcare, SAP, and Xerox.
We interviewed Flanagan to discuss the challenges in behavioral health, how behavioral health providers can leverage AI risk models to ensure patients are matched with the most appropriate clinician at the right time, how AI can dramatically improve the efficiency of an already overburdened behavioral health workforce, and how AI can improve the profitability of delivering behavioral health services, including telemedicine.
Q. What are the challenges in the current behavioral health landscape? And where do telehealth and AI fit in?
ONE. One of the most pressing problems is the inefficient allocation of resources. Currently, our health care system often operates on a first-come, first-served basis, which does not always correspond to clinical urgency.
We do not effectively prioritize patients based on risk or severity of need. This means that a person with a serious mental illness may be placed in line behind others with less urgent needs, potentially leading to worse outcomes and increased emergency room visits.
This is where telehealth and AI come in as potential game changers. Telehealth has proven its worth, especially in behavioral health. About 55% of behavioral health encounters now take place online, and that number has not declined post-pandemic as it has in other areas of healthcare.
This trend is occurring because telehealth removes many barriers to care – patients do not need to miss work, travel to appointments, or face the stigma that can arise when attending an in-person mental health clinic. This increases patient satisfaction and facilitates better clinical outcomes.
AI, on the other hand, is still in its early stages, but shows great promise. One of the most exciting applications in healthcare is patient triage and resource allocation. AI algorithms can analyze patient data to determine risk levels and prioritize care accordingly, meaning we could move from our current first-in, first-out model to one where patients who need the most urgent care are seen first.
This approach has the potential to significantly improve outcomes and reduce the burden on emergency services.
Additionally, AI can help predict gaps in outpatient access and supply-demand imbalances within a health system or clinic population by provider type, time of day, and acuity level. This predictive ability can help health systems optimize staffing and scheduling to increase productivity and patient satisfaction.
Finally, AI can help address the provider shortage by augmenting the capabilities of current clinicians. For example, AI can handle routine administrative tasks, freeing up more time for clinicians to interact with patients. It can also help clinicians make more informed decisions about patient care.
AI and telehealth have great potential, but they are not a silver bullet. We need to think carefully about how we deploy these technologies. We should be wary of generative AI applications that could compromise patient privacy or data security.
Instead, we should focus on machine learning applications that use discrete, anonymized data to improve care delivery without risking patient information.
Telehealth has proven its value in increasing access to care – but combined with the effective and responsible use of AI, it promises to deliver more efficient, high-performing, and personalized mental health services. We must leverage these technologies to enhance, rather than replace, human care, always with a focus on improving patient outcomes and experiences.
Q. How can behavioral health providers leverage AI risk models to ensure patients are matched with the most appropriate clinician at the right time? And how does telehealth fit in here?
ONE. AI risk modeling in behavioral health involves analyzing a wide range of patient data to assess clinical urgency and care needs, including factors such as previous diagnoses, medication history, frequency of health care use, social determinants of health, and even real-time data from wearable devices or patient-reported outcomes.
By processing this complex network of information, AI can generate a comprehensive risk score for each patient, providing insights into their current mental health status and potential future risks.
This risk stratification allows providers to move beyond a first-come, first-served model of care delivery. Instead of having patients wait in line based solely on when they request an appointment, AI can help prioritize based on clinical need.
For example, a patient with a history of suicide attempts and recent crisis events could be flagged for immediate intervention, even if they request an appointment later when the person has milder symptoms. This approach ensures that limited clinical resources are allocated where they can have the most significant impact, potentially preventing mental health crises and reducing emergency department visits.
AI can also match patients with the most appropriate clinician based on their specific needs and the clinician’s expertise. So a patient struggling with both depression and substance use disorder could be matched with a clinician who specializes in dual diagnosis treatment. This strategy could lead to more effective treatment outcomes and higher patient satisfaction.
Furthermore, telehealth allows for more flexible scheduling, complementing the AI risk model’s ability to prioritize emergencies. If a high-risk patient needs to be seen quickly, telehealth makes it easy to get them into a doctor’s schedule, potentially even the same day. This ability to respond quickly can be crucial in preventing mental health crises and ensuring continuity of care.
As these AI risk models become more sophisticated and widely adopted, we may see a shift toward more proactive, preventative behavioral health care. Instead of waiting for patients to reach out when they are in crisis, providers could use AI to identify patients who might benefit from early intervention and reach out proactively.
Q. How can AI dramatically improve the efficiency of an already overburdened behavioral health workforce? And how does this help telehealth providers?
ONE. One of the most promising applications for AI-enhanced workforce efficiency is in administrative and documentation tasks. Behavioral health professionals spend a lot of time on paperwork, charting, and other administrative tasks.
AI-powered tools can streamline these processes, potentially using natural language processing to generate clinical notes from recorded sessions or automating insurance coding. This allows clinicians to focus more energy on direct patient care, potentially increasing the number of patients they can see without compromising quality.
AI can also serve as a powerful decision support tool for clinicians. By analyzing clinical data and keeping up to date with the latest research, AI systems can make evidence-based treatment recommendations tailored to each patient’s unique circumstances. But AI systems should not replace clinical judgment.
For example, AI systems can flag potential drug interactions or suggest alternative treatments based on a patient’s history and symptoms. However, it is always the clinician who decides the appropriate level of care.
For telehealth providers in particular, AI-powered chatbots and virtual assistants can handle initial patient intake by collecting basic information and conducting a preliminary assessment before the patient meets with a clinician. These clinical support tools ensure that the provider has a comprehensive overview of the patient’s situation as soon as the telehealth session begins.
Q. Please discuss how AI can improve the profitability of delivering behavioral health services, including telemedicine.
ONE. AI improves operational efficiency, optimizes resource allocation, and expands access to care—all of which impact health system profitability. AI algorithms can analyze patient data, historical patterns, and real-time factors to optimize clinician scheduling and workload. This optimization can reduce no-show rates and improve clinician efficiency.
AI can even help identify patients at risk of dropping out of treatment or those who might benefit from more intensive services, allowing for proactive intervention.
We also know that effectively leveraging this technology will enhance profitability by automating many time-consuming administrative tasks with algorithms to support documentation, billing, and coding processes – reducing the administrative burden on clinicians while minimizing errors and improving revenue cycle management.
AI can streamline the entire virtual care process – from patient intake to follow-up care coordination – allowing providers to focus more on direct patient care and potentially take on more patients in a given time frame.
AI-powered predictive analytics identifies trends in patient needs, treatment outcomes, and operational metrics to help guide strategic planning, resource allocation, and service expansion. Telehealth providers can leverage this capability to identify underserved markets or optimal times to offer certain services, leading to increased market share and revenue growth.
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