The behavioral health landscape faces several significant challenges, primarily stemming from a severe shortage of providers and increasing demand for services. As has been seen in recent years, there’s been a surge in behavioral health needs across all demographics.
This mismatch between supply and demand has led to long wait times, difficulty accessing care, and, in some cases, patients going without necessary treatment.
Andy Flanagan is CEO of Iris Telehealth, a telepsychiatry technology and services provider. He holds a Master of Science in Health Informatics from the Feinberg School of Medicine at Northwestern University. His prior experience includes being a three-time CEO, as well as founding an SaaS company and holding senior-level positions at Siemens Healthcare, SAP and Xerox.
We interviewed Flanagan to discuss the challenges in behavioral health, how behavioral healthcare providers can leverage AI risk models to ensure patients are matched with the most appropriate clinician at the right time, how AI can significantly improve the efficiency of the already overwhelmed behavioral health workforce, and how AI can enhance the profitability of delivering behavioral healthcare services, including telemedicine services.
Q. What are the challenges on the behavioral health landscape today? And where do telehealth and AI fit in?
A. One of the most pressing issues is the inefficient allocation of resources. Currently, our healthcare system often operates on a first-come, first-served basis, which doesn’t always align with clinical urgency.
We’re not effectively prioritizing patients based on their risk levels or severity of need. This means that someone with a critical mental health condition might be waiting in line behind others with less urgent needs, potentially leading to worse outcomes and increased emergency department visits.
This is where telehealth and AI come into play as potential game-changers. Telehealth already has proven its worth, particularly in behavioral health. About 55% of behavioral health encounters now happen virtually, and this hasn’t declined post-pandemic like in other areas of healthcare.
This trend is occurring because telehealth removes many barriers to care – patients don’t need to take time off work, travel to appointments or deal with the stigma that might come from visiting a mental health clinic in person. It’s a patient satisfier and an enabler of better clinical outcomes.
AI, on the other hand, is still in its early stages, but shows immense promise. One of the most exciting applications in the healthcare space is in patient triage and resource allocation. AI algorithms can analyze patient data to determine risk levels and prioritize care accordingly, meaning we could move away from the current first-in, first-out model to one where the patients who need care most urgently get seen first.
This approach has the potential to significantly improve outcomes and reduce the strain on emergency services.
Additionally, AI can help predict gaps in outpatient access and the supply-and-demand imbalance 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 existing clinicians. For instance, AI could handle routine administrative tasks, freeing up more time for clinicians to interact with patients. It could also help clinicians make more informed decisions about patient care.
AI and telehealth offer tremendous potential, but they’re not silver bullets. We need to be thoughtful about how we implement these technologies. We should be wary of generative AI applications that might compromise patient privacy or data security.
Instead, we should focus on machine learning applications that use discrete, anonymized data to improve care delivery without putting patient information at risk.
Telehealth already has proven its value in increasing access to care – but paired with effective, responsible AI usage, it holds the promise of more efficient, effective and personalized mental health services. We must leverage these technologies to enhance, rather than replace, human care, always keeping the focus on improving patient outcomes and experiences.
Q. How can behavioral healthcare 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?
A. 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 healthcare utilization, social determinants of health, and even real-time data from wearable devices or patient-reported outcomes.
By processing this complex web of information, AI can generate a comprehensive risk score for each patient, providing a nuanced understanding of their current mental health status and potential future risks.
This risk stratification allows providers to move beyond the traditional first-come, first-served model of care delivery. Instead of having patients wait in a queue based solely on when they requested an appointment, AI can help prioritize based on clinical need.
For instance, a patient with a history of suicide attempts and recent crisis events might be flagged for immediate intervention, even if they requested an appointment after someone with 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 also can 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 might be matched with a clinician who specializes in dual diagnosis treatment. This strategy can lead to more effective treatment outcomes and higher patient satisfaction.
Furthermore, telehealth allows for more flexible scheduling, which complements the AI risk model’s ability to prioritize urgent cases. If a high-risk patient needs to be seen quickly, telehealth makes it easier to slot them into a provider’s schedule, perhaps even on the same day. This rapid response capability 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 could see a shift toward more proactive, preventive behavioral healthcare. Instead of waiting for patients to reach out when they’re in crisis, providers could use AI to identify patients who might benefit from early intervention and reach out proactively.
Q. How can AI significantly improve the efficiency of the already overwhelmed behavioral health workforce? And where does this help telehealth providers?
A. One of the most promising applications for AI-enhanced workforce efficiency is in administrative and documentation tasks. Behavioral health professionals spend a considerable amount of time on paperwork, charting and other administrative duties.
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 of their energy on direct patient care, potentially increasing the number of patients they can see without compromising quality.
AI also can serve as a powerful decision support tool for clinicians. By analyzing clinical data and staying up to date with the latest research, AI systems can provide evidence-based treatment recommendations tailored to each patient’s unique circumstances. But AI systems shouldn’t replace clinical judgment.
For example, an AI system might flag potential drug interactions or suggest alternative treatment approaches based on a patient’s history and symptoms. However, it’s always up to the clinician to determine the appropriate level of care.
For telehealth providers specifically, AI-powered chatbots and virtual assistants can handle initial patient intake by gathering basic information and conducting preliminary assessments before a patient meets with a clinician. These clinical support tools ensure the provider already has a comprehensive overview of the patient’s situation right when the telehealth session begins.
Q. Please discuss how AI can enhance the profitability of delivering behavioral healthcare services, including telemedicine services.
A. AI improves operational efficiency, optimizes resource allocation and expands access to care – all of which affect a health system’s bottom line. AI algorithms can analyze patient data, historical patterns and real-time factors to optimize appointment scheduling and clinician workloads. This optimization can reduce no-show rates and improve clinician efficiency.
AI can even assist in identifying patients at risk of dropping out of treatment or those who might benefit from more intensive services, allowing for proactive interventions.
We also know that effectively leveraging this technology enhances profitability by automating many time-consuming administrative tasks using algorithms to assist with documentation and 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 workflow – from patient intake to follow-up care coordination – allowing providers to focus more on direct patient care and potentially see more patients in a given time frame.
AI-driven predictive analytics identify trends in patient demand, treatment outcomes and operational metrics to help guide strategic planning, resource allocation and service expansion. Telehealth providers could leverage this capability to identify underserved markets or optimal times to offer certain services, leading to increased market share and revenue growth.
Follow Bill’s HIT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
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