The behavioral health landscape faces several significant challenges, primarily due to a severe provider shortage and growing demand for services. As seen in recent years, there has been an escalate in behavioral health needs across all demographic groups.
This mismatch between supply and demand results in long waiting times, difficulties in accessing healthcare and in some cases patients not receiving necessary treatment.
Andy Flanagan is CEO of Iris Telehealth, a provider of telepsychiatry technology and services. He holds a master’s degree in health informatics from the Feinberg School of Medicine at Northwestern University. His prior experience includes three CEO positions, as well as founding a SaaS company and holding executive positions at Siemens Healthcare, SAP, and Xerox.
We interviewed Flanagan to discuss the challenges of mental health, how behavioral health providers can operate AI-based risk models to ensure patients are matched with the most appropriate provider at the right time, how AI can significantly improve the efficiency of an already overburdened mental health workforce, and how AI can escalate the profitability of behavioral health care delivery, including telemedicine.
Q. What are the challenges in today’s behavioral health landscape? And where does telehealth and AI fit into all of this?
AND. One of the most pressing issues is the ineffective allocation of resources. Currently, our healthcare system often operates on a first-come, first-served basis, which does not always align with clinical urgency.
We don’t effectively prioritise patients based on risk level or severity of need. This means that someone with a critical mental health condition may be waiting in line behind others with less urgent needs, potentially leading to worse outcomes and more visits to the emergency department.
This is where telehealth and AI come into play as potential players. Telehealth has already proven its worth, especially in behavioral health. About 55% of encounters with people with mental health problems are now taking place virtually and this has not disappeared after the pandemic, as in other areas of healthcare.
This trend is occurring because telehealth removes many barriers to care—patients don’t have to take time off work, travel to appointments, or deal with the stigma that can come with visiting a mental health clinic in person. This satisfies patients and enables better clinical outcomes.
AI, on the other hand, is still in its early stages but shows great promise. One of the most electrifying applications in healthcare is 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 patients who need care the most are seen first.
This approach can significantly improve treatment outcomes and reduce the burden on emergency services.
In addition, AI can facilitate predict gaps in outpatient access and supply-demand imbalances in a health care system or clinic population by provider type, time of day, and acuity level. This predictive capability can facilitate health systems optimize staffing and scheduling to escalate productivity and patient satisfaction.
Finally, AI can facilitate address the provider shortage by augmenting the capabilities of existing clinicians. For example, AI can handle routine administrative tasks, freeing up more time for clinicians to interact with patients. It can also facilitate clinicians make more informed decisions about patient care.
AI and telemedicine offer great potential, but they are not miracle cures. We need to be thoughtful 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 operate unobtrusive, anonymized data to improve the quality of healthcare without compromising patient data.
Telemedicine has already proven its value in increasing access to care – but when combined with effective, responsible operate of AI, it holds the promise of more productive, effective and personalized mental health services. We must operate these technologies to enhance, not replace, human care, always focusing on improving patient outcomes and experiences.
Q. How can behavioral health providers operate AI risk models to ensure patients are matched with the most appropriate provider at the right time? And how does telehealth fit into this?
AND. AI-powered risk modeling in behavioral health involves analyzing a wide range of patient data to assess clinical urgency and care needs, including factors such as prior diagnoses, medication history, frequency of healthcare operate, social determinants of health, and even real-time data from wearable devices or patient-reported outcomes.
By processing this sophisticated web of information, AI can generate a comprehensive risk assessment for each patient, providing detailed knowledge of their current mental health status and potential future risks.
This risk stratification allows providers to move beyond the customary “first-come, first-served” model of care delivery. Instead of forcing patients to wait in line based solely on when they requested an appointment, AI can facilitate prioritize based on clinical need.
For example, a patient with a history of suicide attempts and recent crisis events might be flagged for immediate intervention, even if they requested a visit after someone with milder symptoms. This approach ensures that constrained clinical resources are allocated where they can have the greatest impact, potentially preventing mental health crises and reducing emergency department visits.
AI can also match patients with the most appropriate doctor based on their specific needs and the doctor’s expertise. So a patient struggling with both depression and substance operate disorder could be matched with a doctor who specializes in treating dual diagnosis. This strategy can lead to more effective treatment outcomes and greater patient satisfaction.
In addition, telemedicine allows for more elastic scheduling, which complements the AI risk model’s ability to prioritize urgent cases. If a high-risk patient requires a quick visit, telemedicine makes it easier to schedule them with a provider, perhaps 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 could see a shift toward more proactive, preventative behavioral health care. Instead of waiting for patients to come forward when they’re in crisis, providers could operate AI to identify patients who might benefit from early intervention and proactively reach out.
Q. How can AI significantly improve the efficiency of an already overburdened behavioral health workforce? And where does it facilitate telehealth providers?
AND. One of the most promising applications for AI-enhanced workforce productivity is administrative and documentation tasks. Behavioral health professionals spend significant 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 frees up 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 physicians. 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. However, AI systems should not replace clinical judgment.
For example, an AI system can flag potential drug interactions or suggest alternative treatment approaches based on a patient’s history and symptoms. However, it is always up to the physician to determine the appropriate level of care.
For telemedicine providers, AI-enabled chatbots and virtual assistants can handle initial patient intake by gathering background information and conducting initial assessments before the patient meets with a physician. These clinical support tools ensure that the provider already has a comprehensive overview of the patient’s situation when the telemedicine session begins.
Q. Please discuss how AI can improve the profitability of behavioral health care delivery, including telemedicine services.
AND. AI improves operational efficiency, optimizes resource allocation, and expands access to care—all of which impact the healthcare system’s bottom line. AI algorithms can analyze patient data, historical patterns, and factors in real time to optimize appointment scheduling and physician workload. This optimization can reduce no-shows and improve physician efficiency.
AI could even facilitate identify patients who are at risk of treatment dropout or who would benefit from more intensive services, allowing for proactive intervention.
We also know that effective operate of this technology increases profitability by automating many time-consuming administrative tasks using algorithms to support documentation, billing and coding processes – reducing the administrative burden on physicians while minimizing errors and improving revenue cycle management.
AI can streamline the entire virtual care process—from patient intake to follow-up care coordination—so clinicians can focus more on direct patient care and potentially see more patients in a given time.
AI-powered predictive analytics identifies trends in patient demand, outcomes, and operational metrics to facilitate strategically plan, allocate resources, and expand services. Telehealth providers could operate this capability to identify underserved markets or optimal times to offer specific services, leading to increased market share and revenue growth.