5 Things You Should Know About Tardive Dyskinesia

In a recent piece from Psychology Today, tardive dyskinesia (TD) is brought into sharper public awareness as both a prevalent and often underdiagnosed condition linked to antipsychotic use. The article explores the need for more consistent screening practices and highlights how AI-powered tools, like Videra Health’s TDScreen, are improving early detection and access to care. It also stresses the physical, emotional, and social burden TD places on individuals, and how timely intervention can significantly reduce its impact. With advanced screening technology and FDA-approved treatments, the future of TD care is shifting from reactive to proactive. Read the full article here.

Bridging Domains: The Untapped Potential of AI-Driven Assessment

In a compelling piece from TechBuzz News, Videra Health’s Brett Talbot explores how AI-driven assessment—originally designed for behavioral health—is unlocking new possibilities in the detection and monitoring of movement disorders. The article highlights how shared challenges across disciplines, like episodic data gaps and subjective ratings, can be addressed by AI’s continuous, multimodal analysis. This convergence signals a future where digital biomarkers create more precise, scalable, and patient-centered tools across the healthcare spectrum. As AI continues to bridge domains, it holds the potential to dramatically reshape both clinical practice and pharmaceutical innovation. Read the full article here.

Navigating the APA’s AI Ethical Guidance

As a practicing clinician who has witnessed firsthand the evolution of our field, I view the American Psychological Association’s Ethical Guidance for AI in the Professional Practice of Health Service Psychology through both clinical and leadership lenses. This guidance arrives at a crucial moment—not as a restriction on innovation, but as a framework that validates what many of us in clinical practice have been advocating for: technology that respects the sacred nature of the therapeutic relationship.

In Short....

The APA offers ethical guidance for using AI in psychology. Key points: be transparent with clients, guard against bias, protect data privacy, validate AI tools, maintain human oversight, and understand legal responsibilities. AI should support—not replace—professional judgment. Continue on for more.

The Clinical Reality

In my years of practice, I’ve seen how administrative burdens can erode the time we have for what matters most—connecting with and helping our patients. When the APA reports that 10% of practitioners are already using AI for administrative tasks, I’m not surprised. What concerns me is ensuring we’re using these tools in ways that enhance, rather than compromise, the quality of care.

The guidance speaks directly to the tensions many clinicians feel. We want efficiency, but not at the cost of accuracy. We seek innovation, but not if it undermines the trust our patients place in us.

The Primacy of Informed Consent

The APA’s emphasis on transparent informed consent reflects a fundamental truth about therapeutic relationships: they’re built on trust and transparency. Patients have the right to understand every aspect of their care, including when and how AI tools are involved. This isn’t bureaucracy—it’s respect for patient autonomy and an extension of the collaborative approach that defines good therapy.

Clinical Judgment Remains Supreme

What heartens me most about the guidance is its clear stance that AI should augment, not replace, clinical judgment. As clinicians, we bring years of training, intuition, and human understanding that would be difficult for an algorithm  to fully replicate. The guidance affirms that we must remain the “conscious oversight” for any AI-generated content or recommendations.

Accuracy as an Ethical Imperative

The APA’s call for critical evaluation of AI outputs aligns with our professional obligation to “do no harm.” Every note we write, every assessment we make, becomes part of a patient’s story. We cannot abdicate our responsibility to ensure that story is told accurately and with integrity.

What This Means for Clinical Practice

From a clinical perspective, implementing these guidelines requires us to:

Maintain Our Clinical Voice:

Whether using AI for documentation or assessment support, we must ensure that our clinical reasoning and the unique understanding we have of each patient remains central to all records and decisions.

Protect the Therapeutic Space:

The therapy room—whether physical or virtual—must remain a sanctuary. Any technology we introduce should enhance the sense of safety and confidentiality that makes healing possible.

Consider Diverse Populations:

The guidance reminds us to be vigilant about how AI tools may differentially impact various populations. As clinicians, we must advocate for tools that are tested across diverse groups and remain alert to potential biases.

Embrace Continuous Learning:

Just as we pursue continuing education in clinical techniques, we must commit to understanding the tools we use. This isn’t about becoming technologists—it’s about maintaining competence in our evolving field.

The Opportunity Before Us

The APA’s guidance doesn’t close doors; it opens them responsibly. I see opportunities to:

  • Reduce the documentation burden that keeps us at our desks instead of with patients
  • Enhance our ability to track treatment progress and outcomes
  • Support clinical decision-making with evidence-based insights
  • Extend quality mental healthcare to underserved communities

But each of these opportunities must be pursued with clinical wisdom and ethical clarity.

A Personal Reflection

I entered this field because I believe in the transformative power of human connection. Nothing in the APA’s guidance changes that fundamental truth. Instead, it challenges us to ensure that as we adopt new tools, we do so in service of that connection.

I’ve seen too many technological promises in healthcare fall short because they were designed without clinical input or implemented without clinical wisdom. The APA’s guidance helps ensure we don’t repeat those mistakes in mental health.

Moving Forward as a Clinical Community

As clinicians, we have a unique responsibility in this moment. We must:

  • Share our experiences openly, both successes and concerns
  • Advocate for our patients’ needs in the development of AI tools
  • Hold ourselves and our tools to the highest ethical standards
  • Remember that behind every algorithm is a human being seeking help

To My Fellow Clinicians

I know many of you approach AI with a mixture of hope and hesitation. That’s appropriate. The APA’s guidance gives us permission to be thoughtful, to ask hard questions, and to demand that any tool we use meets the ethical standards we’ve sworn to uphold.

This isn’t about resisting change—it’s about shaping it. We have the opportunity to ensure that AI in mental healthcare develops in ways that honor our professional values and serve our patients’ best interests.

The therapeutic relationship has survived and adapted through many changes in our field. With the APA’s ethical guidance as our North Star, I’m confident we can navigate this new frontier while keeping that relationship at the heart of everything we do.

After all, in a world of increasing technological complexity, the simple act of one human being helping another remains as powerful—and as necessary—as ever.

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

How AI Expands Care When Care Demands Continues to Rise

Healthcare provider using AI mental health platform on tablet while speaking with patient, illustrating how AI in mental healthcare amplifies human care

As we recognize Mental Health Awareness Month this May, we find ourselves at a critical juncture where AI in mental healthcare offers promising solutions. The need for mental health services continues to grow at an unprecedented rate, while provider shortages and burnout intensify. According to data from across the healthcare landscape, 47% of the U.S. population now lives in an area with a mental health workforce shortage, and wait times for appointments often stretch beyond three months.

At Videra Health, we’ve been tackling this challenge head-on, working with providers who face the daily reality of trying to deliver quality care despite limited resources. Our experiences have revealed a fundamental truth: we cannot simply produce more clinicians fast enough to meet the growing demand. Instead, we must find innovative ways to expand the reach and impact of our existing clinical workforce.

The Human Understanding Gap: Where AI in Mental Healthcare Makes a Difference

The core of effective mental healthcare has always been human connection and understanding. Providers need to know not just what their patients are saying, but how they’re feeling, their emotional state, and whether they’re at risk. Traditionally, this understanding has been limited to in-office interactions, creating significant blind spots in patient care journeys.

What happens when a patient struggling with depression has a difficult week between appointments? How can a substance use disorder treatment center identify which discharged patients are at risk of relapse? How do we ensure that individuals experiencing suicidal ideation are identified and supported before reaching crisis?

These questions highlight what I call the “human understanding gap” – the critical information about patient wellbeing that falls through the cracks between formal care touchpoints.

AI in Mental Healthcare: Building Bridges, Not Replacements

This is where thoughtfully designed AI systems can make a transformative difference. At Videra, we’ve seen firsthand how clinical AI can serve as a bridge that extends human care, rather than replacing it.

Our platform uses video, audio, and text assessments powered by artificial intelligence to understand patients in their own words and on their own time. By analyzing facial expressions, voice patterns, language, and behavioral indicators, we can identify signs of emotional distress, suicidal language, medication adherence challenges, and other critical indicators that might otherwise go unnoticed between appointments.

The results have been profound. In one behavioral health system implementation, we’ve seen that patients with higher engagement in post-discharge monitoring demonstrate significantly stronger recovery outcomes. Another community mental health center utilizing our technology reduced crisis alerts by 64% after just two weeks of proactive monitoring.

Amplifying Human Care, Not Replacing It

The most important lesson we’ve learned is that effective clinical AI doesn’t aim to replace human providers – it amplifies their capabilities and extends their reach. By handling routine clinical assessments and identifying at-risk patients, AI creates a force multiplier for clinical expertise, allowing providers to direct their specialized skills where they’re needed most.

For example, our automated assessment system can engage thousands of patients consistently and frequently to identify those with acute needs, before, during or after care. Our note-taking technology reduces documentation time, giving clinicians more face-to-face time with patients. And our monitoring tools provide continuous support between appointments, creating a safety net that would be impossible to maintain manually.

This clinical enhancement works alongside our workflow solutions, which address a separate but complementary need. While clinical AI focuses on assessment and insights, our workflow tools tackle the administrative burdens that consume valuable provider time.

The result is a multiplier effect on care capacity. Providers using these integrated AI-powered clinical and workflow tools can effectively support more patients without sacrificing quality of care – in fact, they can often deliver better outcomes by focusing their expertise where it’s most needed.

Looking Forward: The Future of AI in Mental Healthcare

As we look ahead, I believe we’re only beginning to tap into AI’s potential to address the growing mental health crisis. Future developments will likely include:

  • More sophisticated risk prediction models that can identify potential issues before they become crises
  • Deeper integration with treatment pathways to provide personalized care recommendations
  • Enhanced accessibility tools that break down barriers to care for underserved populations
  • Advanced training systems that help new clinicians develop expertise more quickly

At Videra Health, we’re committed to advancing these innovations responsibly, always keeping the human connection at the center of our work. Because ultimately, the goal isn’t to build AI that replaces humans – it’s to build AI that helps humans help more humans.

A Call to Action

As we observe Mental Health Awareness Month, I encourage healthcare leaders to consider how AI can extend your organization’s capacity to deliver care. The mental health crisis isn’t waiting, and neither should we.

We need to embrace tools that allow us to do more with our existing resources, reaching patients when and where they need support. By implementing AI in mental healthcare thoughtfully, we can ensure that more people receive the care they need, when they need it most.

Together, we can build a future where technology and human connection work in harmony to meet the growing demand for mental healthcare – not by replacing the invaluable work of clinicians, but by amplifying their impact and extending their reach.

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

Discovery Behavioral Health boosts care and revenue with patient engagement efforts

Healthcare IT News highlights how Discovery Behavioral Health is advancing patient care and operational efficiency through post-discharge engagement powered by AI. By implementing a white-labeled version of Videra Health’s video-based assessment platform, Discovery extended support beyond the walls of its facilities—gaining real-time insights into patient recovery and unlocking new opportunities for early intervention and re-engagement. This proactive, tech-enabled approach exemplifies the shift toward continuous, personalized care across the behavioral health spectrum. With measurable improvements in patient outcomes and provider efficiency, the article shows how innovation can meaningfully transform the recovery journey.
Read the full article here.

Combating Maternal Depression: Providing More Care Options for New Mothers with AI

AI Maternal Depression Screening Transforms Healthcare

Maternal depression affects approximately 1 in 5 women in the United States, and for many, their struggles go undetected and untreated. As we recognize National Maternal Depression Day, it’s critical that we acknowledge both the prevalence of this condition and the innovative solutions emerging to address it.

At Videra Health, we’ve witnessed firsthand how the lack of diagnosis of postpartum depression (PPD) can impact new mothers in what would normally be a joyous time. Often unrecognized and undiagnosed, PPD often slips past providers as new mothers transition from OBGYN in their first few postpartum weeks to their primary care physician for longer-term care. It is in this period of transition between providers that PPD symptoms often appear, and why it is so challenging to identify the symptoms that trigger most screenings for PPD. The gap in care not only impacts mothers but resonates throughout families and communities.

The Hidden Costs of Untreated Maternal Depression

Beyond the emotional toll, untreated postpartum depression costs payors 90% more in healthcare expenses.1 These costs extend beyond immediate medical care to include long-term impacts on child development and family wellbeing. Early detection and intervention are essential not just for emotional recovery but also for financial sustainability in our healthcare system.

How AI Is Transforming Maternal Mental Health Care

The traditional approach to maternal mental health screening often falls short. Brief questionnaires during limited check-ups may not capture the full picture of a mother’s experience, especially when stigma prevents honest reporting.

At Videra Health, we’re leveraging AI technology to create a more comprehensive, accessible approach:

  • Continuous Monitoring: Rather than relying solely on point-in-time assessments during medical visits, our AI-assisted platform enables consistent check-ins throughout the perinatal and postnatal periods.
  • Multi-Modal Assessment: Our technology analyzes video, text, and audio responses, providing deeper insights beyond standard questionnaires like the Edinburgh Postnatal Depression Scale (EPDS). These additional signals help identify emotional distress that might otherwise go unnoticed.
  • Early Risk Detection: Our platform identifies mothers at higher risk before symptoms escalate to crisis levels. This proactive approach allows for intervention at the earliest signs of struggle.
  • Seamless Provider Integration: When our system detects concerning patterns, it automatically alerts healthcare providers, ensuring timely clinical support without requiring additional staffing resources.

Real Impact for Mothers and Families

What makes this approach particularly powerful is its ability to reach mothers where they are. New parents often struggle to attend in-person appointments due to childcare challenges, physical recovery, and overwhelming schedules. Our asynchronous video assessment platform allows mothers to engage with care on their own time, from the comfort of home, using their personal devices.

The data we’ve gathered demonstrates real impact:

  • Earlier identification of mothers needing support
  • Increased engagement in treatment when needed
  • Significant improvements in maternal emotional wellbeing
  • Better medication adherence for those requiring pharmacological intervention
  • Reduced emergency interventions and hospitalizations

Looking Forward: A Multi-Faceted Approach

While technology offers promising solutions, addressing maternal depression requires a comprehensive approach. Technology should complement, not replace, human connection. Our goal is to enhance the relationship between mothers and healthcare providers by providing more touchpoints and deeper insights.

As we commemorate National Maternal Depression Day, let’s commit to leveraging every tool available—including innovative AI technologies—to ensure that no mother suffers in silence. By combining compassionate care with advanced technology, we can create a future where maternal depression is identified early, treated effectively, and ultimately, prevented wherever possible.

Every mother deserves support during one of life’s most challenging transitions. Through innovation and commitment, we can make that support more accessible than ever before.

1 Dagher, R.K., McGovern, P.M., Dowd, B.E., & Gjerdingen, D.K. (2012). “Postpartum depression and health services expenditures among employed women.” Journal of Occupational & Environmental Medicine, 54(2):210-215.

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

How AI-Driven Innovation is Transforming Tardive Dyskinesia Detection and Patient Care

man shaking hand holding glass of water

As we recognize Tardive Dyskinesia (TD) Awareness Week, I’m reflecting on the significant challenges faced by both patients living with TD and the healthcare providers dedicated to their care. TD remains underdiagnosed despite affecting millions of Americans taking antipsychotic medications, with its involuntary movements often mistaken for nervousness, aging, or other conditions. This gap in recognition represents not just a clinical challenge, but a deeply human one that affects quality of life and treatment outcomes.

The Challenge of TD Detection

Tardive Dyskinesia presents unique screening challenges even for experienced clinicians. Traditional AIMS (Abnormal Involuntary Movement Scale) assessments, while valuable, face several real-world limitations:

  • Assessments are often rushed in busy clinical settings
  • Training and experience among raters is inconsistent
  • Unconscious biases can affect evaluations (such as assumptions about which populations are affected)
  • Ongoing monitoring is inconsistent due to limited capacity

Our research and work with clinics show that when different people check the same patients for TD symptoms, they often don’t agree on what they see. Even trained clinicians who haven’t been calibrated together achieve a Cohen’s Kappa score of only 0.37 (±0.05), suggesting significant inconsistency. Even after calibration, this improves to just 0.57 (±0.03). In other words, humans are inconsistent even when rating the same patient and symptoms.

This variability in assessment doesn’t just represent a data problem—it directly impacts patient care, potentially delaying crucial interventions that could improve quality of life.

How AI-Powered TD Screening Improves Detection Accuracy

At Videra Health, we’ve developed TDScreen, an AI-powered solution designed to address these challenges head-on. TDScreen uses advanced video analysis technology to evaluate patients for potential TD symptoms quickly and efficiently. Patients can take the screener on their own time, on their own device. The results have been remarkable, with our AI model demonstrating a Cohen’s Kappa of 0.61—outperforming even calibrated human raters.

TDScreen’s performance metrics show its power as a clinical support tool:

  • AUC (Area Under Curve): 0.85 (0.83 – 0.97)
  • Sensitivity/Specificity = 0.79 (0.70 – 0.93)/0.82 (0.73 – 0.92)
  • Precision/Recall = 0.81 (0.73 – 0.92)/0.79 (0.70 – 0.93)

These numbers represent more than just statistical achievements—they translate to real-world benefits:

  • Consistency: Unlike human raters whose assessments may vary, AI provides the same evaluation standards every time
  • Accessibility: Patients can complete assessments from home on their own devices
  • Efficiency: Providers save valuable clinical time while increasing screening frequency
  • Earlier detection: Subtle symptoms can be identified before they become pronounced
  • Continuous monitoring: Regular assessments track symptom progression or improvement

Moving Beyond Detection to Comprehensive Care

While TDScreen isn’t intended as a standalone diagnostic tool, it represents a significant advancement in meeting the standard of care for TD screening. The American Psychiatric Association guidelines recommend regular monitoring for TD in patients taking antipsychotics, but time constraints often make this challenging to implement consistently.

Our AI-assisted approach helps bridge this gap by making regular screening more feasible, ultimately leading to earlier intervention when symptoms are detected. This approach aligns perfectly with measurement-based care principles, providing objective data to inform treatment decisions.

The Human Element of Technology

It’s important to emphasize that TDScreen and our broader AI platform aren’t about replacing clinician judgment—they’re about enhancing it. By automating routine screenings, we free healthcare providers to focus on what matters most: the human connections and complex decision-making that drive quality care.

One provider using TDScreen shared, “I love it, I had a patient that took the screener, and it picked up on movements I would have missed. It works great!”

This Tardive Dyskinesia Awareness Week, I invite healthcare providers to consider how AI-powered tools like TDScreen can help them meet and exceed the standard of care for TD screening and monitoring. By embracing these innovations, we can collectively improve outcomes for the millions of patients at risk for or living with tardive dyskinesia.

At Videra Health, we’re committed to continuing this important work, combining advanced technology with compassionate care to transform how we understand, detect, and manage TD. Together, we can make significant progress in addressing this challenging condition and improving the lives of those affected by it.

To learn more about TDScreen and to sign up to use TDScreen at no cost in your practice, visit TDScreen.ai

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

AI’s Role in Behavioral Health and Ethical Patient Care with Loren Larsen

How can AI deepen behavioral health care without crossing ethical lines? Loren Larsen, CEO of Videra Health, reveals how AI-driven conversations, facial analysis, and patient follow-ups are reshaping mental health treatment—and why model cards are crucial for transparency. Tune in to The Business of AI in Healthcare podcast to explore the fine line between innovation and patient trust. Listen to the full episode here.

Ethical Implementation of AI in Mental Healthcare: A Practical Guide

In a recent article published by The AI Journal, the conversation around AI in mental healthcare takes an essential turn—focusing not only on its transformative potential, but on how to implement these tools responsibly. As clinicians adopt AI to improve efficiency and outcomes, ethical principles like transparency, equity, and patient autonomy must remain central to the process. This guide emphasizes that ethical implementation isn’t a one-time decision, but a continuous journey that requires trusted partners and thoughtful oversight. Ultimately, AI should enhance—not replace—the deeply human nature of mental healthcare.
Read the full article here.

5 Ways AI Can Help Mental Health Clinicians Manage a Growing Caseload

As mental health providers face mounting caseloads and rising demand, AI offers a path forward by enhancing efficiency without sacrificing personalized care. This article highlights how AI-powered screening platforms, rooted in measurement-based care, are helping clinicians prioritize high-risk patients, automate routine tasks, and extend their reach beyond the office. The result is more time for meaningful client interactions and stronger therapeutic relationships—hallmarks of quality care. With the right implementation, AI serves not as a replacement, but as a trusted partner in the delivery of smarter, more responsive mental health services.
Read the full article here.

AI in Clinical Trials: Moving Beyond Traditional Endpoints in Bipolar Disorder Research

AI-powered bipolar disorder clinical trial monitoring dashboard showing patient assessment data and predictive analytics

AI bipolar clinical trials are revolutionizing how pharmaceutical companies develop and test new treatments for bipolar disorder. With Videra Health’s advanced predictive algorithms and multimodal assessment capabilities, researchers can now capture deeper insights into patient symptoms, mood fluctuations, and treatment responses.

Traditional Challenges in Bipolar Clinical Trials

Historically, bipolar disorder clinical trials have relied heavily on periodic assessments and self-reported data. These traditional methods often miss crucial mood fluctuations between clinical visits, creating significant gaps in our understanding of treatment response. The subjective nature of these assessments can also introduce inconsistencies that impact trial outcomes.

How AI is Revolutionizing Bipolar Clinical Trials

Through our partnerships with leading pharmaceutical companies, Videra Health has witnessed firsthand how AI-powered video and voice analysis is transforming bipolar disorder research. These AI bipolar clinical trials enable continuous, objective measurement of symptoms without increasing the burden on clinical sites or patients.

The integration of artificial intelligence in these studies creates multiple advantages:

  • Continuous Monitoring: AI algorithms analyze voice patterns and facial expressions to detect subtle mood changes between formal assessments
  • Objective Data Collection: Reducing reliance on subjective self-reporting with quantifiable measurements
  • Enhanced Patient Engagement: More natural interaction methods improve trial retention rates
  • Reduced Site Burden: Automated assessments collect more data without additional clinical staff time
  • Earlier Intervention Opportunities: Rapid identification of mood shifts allows for timely protocol adjustments

Real-World Impact on CNS Drug Development

AI bipolar clinical trials aren’t just generating more data – they’re producing higher quality, more meaningful insights that can accelerate drug development. In recent collaborations, our AI assessment platform identified early treatment responses that traditional methods missed until weeks later.

The implications extend beyond bipolar disorder research. The same AI methodologies show promise for trials involving depression, schizophrenia, and other serious mental health conditions where subtle behavioral changes can indicate treatment response.

Looking Forward: The Future of AI in CNS Research

As we connect with fellow innovators at Reuters Pharma USA during Bipolar Awareness Month, it’s clear that AI bipolar clinical trials represent just the beginning of a transformation in CNS research. The integration of artificial intelligence with traditional clinical methodologies isn’t replacing human expertise – it’s enhancing our ability to understand complex mental health conditions and develop more effective treatments.

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

How AI is Empowering Providers with Early Detection of Mental Illness

AI is transforming mental health care by enabling earlier, more precise detection of symptoms that might otherwise go unnoticed until crisis points. This thoughtful piece explores how technology can continuously monitor subtle behavioral shifts—augmenting clinical expertise and allowing providers to intervene sooner. By analyzing data across multiple modalities and comparing patients to their own baselines, AI supports a shift from reactive treatment to proactive care. The result is better outcomes, lower costs, and a future where clinicians are empowered—not replaced—by intelligent tools.
Read the full article here.

Breaking Down Barriers: How CCBHCs Can Lead Healthcare Access Innovation

As we gather for the NACBHDD Legislative and Policy Conference in Washington DC, I’m struck by the critical conversations around innovation and access to behavioral healthcare. The conference agenda highlights what many CCBHC leaders already know – we’re facing multiple, interrelated challenges: medical debt, administrative burdens, workforce shortages, and coordination with justice systems.

At Videra Health, we’re seeing CCBHCs tackle these challenges through innovative approaches to care delivery. Key trends emerging from our partnerships include:

The upcoming Medicaid discussions at the conference are particularly relevant. As the largest payer of behavioral health services, changes to Medicaid structure will significantly impact CCBHCs’ ability to serve their communities. We must ensure that technological innovation aligns with policy evolution to support, not hinder, access to care.

Looking ahead, CCBHCs are uniquely positioned to lead healthcare transformation. By combining policy advocacy with practical innovation, we can create more accessible, efficient, and effective behavioral health systems.

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

Transforming Clinical Trials: The Power of Multi-Modal AI Assessment

Multimodal AI clinical trials dashboard showing video, voice and text analysis

As pharmaceutical companies seek to develop new treatments for behavioral health and movement disorders, traditional trial designs often struggle to capture the full spectrum of patient experiences and treatment effects. The challenge isn’t just gathering more data – it’s gathering the right data at the right time.

Through our work supporting clinical trials, we’ve found that combining multiple modes of AI analysis – video, voice, and text – provides a more comprehensive understanding of treatment response. This integrated approach allows research teams to:

  • Track subtle changes in movement patterns that may indicate treatment effects
  • Analyze speech patterns for cognitive and emotional indicators
  • Monitor facial expressions for signs of distress or improvement
  • Capture patient-reported experiences in their own words
  • Document symptoms consistently between site visits

This comprehensive approach benefits multiple stakeholders:

For Research Teams:

  • More frequent assessment points without increasing site burden
  • Earlier detection of safety signals
  • Objective measurement of subjective experiences
  • Improved protocol compliance monitoring

For Patients:

  • More natural assessment experience
  • Fewer in-person visits required
  • Ability to report symptoms in real-time
  • Multiple channels for sharing their experience

For Sponsors:

  • Richer data for efficacy analysis
  • Better engagement and retention
  • More complete safety monitoring
  • Potential for novel endpoints

As we look to the future of CNS drug development, moving beyond traditional endpoints isn’t just about technology – it’s about better understanding the patient journey. Multi-modal AI assessment helps bridge the gap between periodic site visits, providing a more complete picture of treatment impact.

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

2025 Trends in Behavioral Health Technology – Part 2

Two hands - one human, one robotic - pointing to 2025

As discussed in part one of this series, the behavioral health technology landscape is undergoing a profound metamorphosis, with artificial intelligence (AI) and digital technologies reshaping how behavioral health professionals train, deliver care, and interact with patients. Here’s the last four trends I think will impact the behavioral healthcare space in 2025.

Intelligent Training and Skill Development

Continuous improvement has always been the hallmark of exceptional clinical practice. Now, AI is revolutionizing how behavioral health professionals refine their craft. Imagine a world where every therapy session becomes a learning opportunity—not through traditional supervision alone, but through intelligent, nuanced feedback systems that can analyze communication patterns, emotional resonance, and therapeutic techniques with unprecedented depth.

Advanced AI tools can now listen to therapy sessions, providing granular insights into communication effectiveness. These systems don’t just provide mechanical feedback; they offer sophisticated analysis of therapeutic alliance, helping clinicians understand subtle interpersonal dynamics that might otherwise go unnoticed. Simulated training environments allow practitioners to practice with AI patients, creating safe spaces to experiment with diverse therapeutic approaches and develop skills for treating populations they might find challenging.

This isn’t about replacing human supervision but augmenting it. By reducing time and cost barriers associated with traditional training methods, these technologies democratize professional development, allowing more practitioners to access high-quality skill enhancement.

The Emerging Landscape of Digital Therapeutics

The regulatory landscape for digital behavioral health tools is rapidly evolving. The FDA’s increasing approval of digital therapeutics and CMS’s recent Medicare billing codes represent a watershed moment. What was once considered experimental is now becoming mainstream healthcare.

Digital therapeutics are no longer peripheral technologies but integrated healthcare solutions. Much like traditional prescriptions, clinicians can now “prescribe” FDA-approved digital applications. This represents a fundamental shift in how we conceptualize behavioral health treatment—expanding therapeutic interventions beyond traditional in-person or telehealth models.

However, this emerging ecosystem is not without risks. The proliferation of behavioral health apps has created a complex marketplace where marketing claims often outpace clinical evidence. Consumers and practitioners must develop sophisticated digital literacy, distinguishing between rigorously tested interventions and unsubstantiated digital offerings.

Navigating the Ethics of AI in Therapy

The potential for AI to automate risk assessment and even conduct preliminary therapeutic interactions is tantalizing. Yet, this technological frontier demands careful navigation. While AI tools can provide initial screenings and support, they cannot—and should not—replace the profound human elements of therapeutic relationships.

We are witnessing the early stages of what might become a regulatory “Wild West” in digital behavioral health. Expect increased scrutiny, with regulatory bodies working to establish clear guidelines that protect patient safety while allowing technological innovation.

A Holistic View of Technological Integration

These trends are not isolated developments but interconnected elements of a broader transformation. They represent a holistic reimagining of behavioral healthcare—where technology serves as an empowering tool, not a replacement for human connection.

The most successful organizations will be those that view these technologies not as standalone solutions but as integrated components of a comprehensive care strategy. Success will depend on maintaining a delicate balance: leveraging technological capabilities while preserving the irreplaceable human elements of empathy, nuance, and genuine therapeutic connection.

Embracing Responsible Innovation

As we move deeper into 2025, the behavioral health landscape stands at a critical juncture. The technologies emerging today have the potential to democratize behavioral healthcare, reduce systemic barriers, and create more personalized, effective treatment modalities.

Yet, with this potential comes profound responsibility. Our challenge is not merely to adopt new technologies but to do so thoughtfully, ethically, and with an unwavering commitment to patient well-being.

The future of behavioral health is not about technology replacing human care—it’s about technology expanding and enhancing our capacity for compassion, understanding, and healing.

Deep Dive Into the Trends

Curious about how these technologies impact care, how the regulatory landscape is changing to meet the new paradigm, or how AI can help super-charge efforts to bring new medications to market? Join our webinar on January 31, 2025 at 3PM ET / 12PM PT to discuss 2025 trends and what it means for healthcare.

Check our blog for the latest discussions on AI in healthcare, behavioral health, life sciences and clinical trials.

2025 Trends in Behavioral Health Technology, Part 1

Two hands - one human, one robotic - pointing to 2025

The First Three Trends

As we enter 2025, the behavioral health technology landscape is on the cusp of a revolution, with artificial intelligence (AI), digital tools, and innovative approaches poised to dramatically reshape how mental health services are delivered, accessed, and personalized. I expect these first three key emerging trends will fundamentally alter the healthcare ecosystem.

Expanding Access and Democratizing Behavioral Health Care with Technology

Digital health technologies are emerging as powerful democratizing forces in healthcare delivery. For populations historically marginalized—rural communities, economically constrained individuals, and underserved demographic groups—AI and digital platforms represent more than technological solutions. They are bridges to care, pathways to understanding, and tools of empowerment.

These technologies are not about replacing human connection but extending its reach. By breaking down geographical, economic, and systemic barriers, they create opportunities for more inclusive, accessible behavioral health support. Intelligent systems can now provide initial screenings, offer preliminary support, and guide individuals towards appropriate resources with unprecedented sensitivity and efficiency.

The Precision Medicine of Behavioral Health

 The era of one-size-fits-all treatment is rapidly dissolving. Artificial intelligence is ushering in a new paradigm of precision behavioral healthcare, where treatment plans are as unique as the individuals receiving them. By analyzing complex, multi-dimensional datasets, AI can now recommend care pathways with a level of personalization that was once the domain of highly specialized, resource-intensive approaches.

This isn’t about algorithmic replacement of clinical judgment but about providing clinicians with powerful, nuanced tools for understanding and supporting patient well-being. Each recommendation is a collaborative insight, bridging technological sophistication with human empathy.

Navigating the Ethical Considerations in the Human-Technology Interface

As we embrace these transformative technologies, we must remain vigilant about maintaining the core ethical principles of healthcare. Artificial intelligence and digital tools are powerful assistants, not autonomous decision-makers. They augment human capability, illuminate hidden insights, and create opportunities for more profound, more personalized care.

The most successful approaches will be those that view behavioral health technology not as a replacement for human interaction but as a sophisticated tool for enhancing our collective capacity for understanding, compassion, and healing.

Read part two of my Top Trends for 2025 here.

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Meet The Disruptors: Loren Larsen Of Videra Health On The Five Things You Need To Shake Up Your Industry

Loren Larsen, CEO of Videra Health, shares his perspective on what it takes to truly disrupt an industry in this thoughtful Authority Magazine interview. Drawing on decades of experience in healthcare technology and AI, Larsen reflects on the challenges of innovating in a highly regulated, human-centered field like mental health. He discusses how Videra Health is leveraging video-based AI to enhance early detection and support clinicians without replacing the essential human connection. Throughout the piece, he emphasizes the importance of trust, empathy, and deep domain expertise in building solutions that actually move the needle.

Read the full article here.

Empowering Decisions: How AI Data Insights Are Shaping TD Treatment Approaches

Tardive dyskinesia (TD) is a serious movement disorder affecting individuals taking antipsychotic medications for extended periods. It’s characterized by involuntary, repetitive movements of the face, tongue, or limbs. Early detection and intervention are crucial for managing TD and improving patient well-being. However, traditional methods of identifying TD risk often rely on subjective observations and patient reporting, leading to missed diagnoses and delayed treatment.

Here’s why this is a growing concern: Antipsychotics are widely prescribed, with an estimated 1.2% of the US population taking them. This translates to a potential prevalence of TD between 800,000 and 1.6 million individuals. Disturbingly, estimates suggest only around 5% of these individuals receive treatment for TD symptoms, despite the availability of medications like Austedo and Ingrezza.

The challenge lies in early detection. Traditional methods are often insufficient, and devoting enough in-person time for frequent monitoring can be difficult for busy mental health professionals. This is especially true with the rise of telemedicine and increasing patient loads. As a result, TD often goes undetected until later stages, when the involuntary movements may become permanent.

This is where Artificial Intelligence (AI) is revolutionizing the field of tardive dyskinesia management. AI-driven mental health screening platforms offer a powerful tool to empower healthcare professionals with data-driven insights, leading to more proactive and effective TD treatment approaches.

How AI Platforms Identify TD Risk:

AI-powered screening platforms go beyond traditional methods by analyzing a wider range of data points to create a more comprehensive picture of a patient’s TD risk. Machine learning algorithms sift through vast amounts of historical data, identifying patterns and correlations between specific factors and the development of TD. This allows the platform to generate individualized risk profiles for each patient.

Videra Health utilizes video assessments, where AI can detect subtle movement patterns that may be difficult for even experienced psychiatrists to identify without extensive testing. This is especially beneficial in identifying early symptoms of TD, which could result in earlier intervention and ultimately, better patient outcomes.

It’s important to note that while there’s no known cure for TD, early detection allows for treatment approaches that can manage symptoms and potentially prevent them from worsening. Medications like Austedo and Ingrezza can help alleviate some symptoms, and early intervention can significantly improve a patient’s quality of life. Additionally, failing to properly screen for TD can lead to liability issues for healthcare providers, as patients with undetected TD may experience significant physical and emotional distress.

Benefits of AI-Driven TD Risk Assessment:

By leveraging AI data insights, healthcare professionals can enjoy several advantages:

  • Proactive Identification: Early detection is key to managing TD. AI platforms can identify potential risk factors as symptoms begin to manifest, allowing for preventative measures and early intervention.
  • Data-Driven Decision Making: AI provides objective, data-driven insights to support clinical judgment. This empowers healthcare professionals to make more informed decisions about treatment plans and medication adjustments.
  • Personalized Care: AI risk profiles enable a more personalized approach to TD care. By understanding each patient’s unique risk factors, healthcare professionals can tailor treatment plans to maximize effectiveness and minimize side effects.
  • Improved Patient Outcomes: Early detection and intervention can significantly improve patient outcomes. By proactively managing TD risk, healthcare professionals can help patients maintain their quality of life and well-being.
Empowering Patients with AI Tools:

Beyond empowering healthcare professionals, AI can also empower patients in their own healthcare journey. AI-powered platforms can:

  • Increase Patient Engagement: When patients feel their concerns are addressed proactively, it fosters trust and collaboration with their healthcare providers. 
  • Self-Assessment Tools: Some platforms offer self-assessment tools that allow patients to track potential symptoms and report them to their healthcare provider. This fosters a collaborative approach to managing TD risk.
The Future of AI in TD Management:

As AI technology continues to evolve, we can expect even greater advancements in the field of TD treatment. Here are some exciting possibilities:

  • Real-Time Monitoring: AI-powered wearable devices might continuously monitor patients for subtle changes in movement patterns, allowing for real-time TD risk assessment.
  • Additional Predictive Analytics: In addition to the existing AI-powered screening tools, advanced algorithms might predict the likelihood of TD development based on a patient’s specific medical history and genetic makeup, enabling even more proactive approaches.

AI-driven mental health screening platforms are transforming the landscape of TD management. By empowering healthcare professionals with data-driven insights and fostering patient engagement, AI offers the potential for earlier detection, personalized treatment plans, and improved patient outcomes in the fight against tardive dyskinesia. As AI technology continues to develop, the future of TD management holds great promise for improving patient well-being.

The Power of Listening: How AI Assessments Capture the Patient Voice and Transform Care Management

Traditionally, care management relies heavily on questionnaires and standardized assessments. While these tools provide valuable information, they often miss the richness of the patient experience, especially in mental health. Subtle cues in a patient’s voice, facial expressions, and body language can offer deeper insights into their well-being, which can be missed by traditional methods.

The Power of AI in Capturing the Patient Voice and Monitoring Progress

Mental Health Screening and Beyond: As a healthcare professional, I’ve often heard the sentiment: “If I could just see the patient for a few seconds, I’d get a much better sense of how they’re doing.” Traditional methods can lack nuance, relying solely on self-reported information.

This is where AI-powered video, text, and audio assessments excel. Mental health screening platforms leverage AI to capture the patient in ways that go beyond what traditional methods can achieve, providing a more accurate picture of a patient’s well-being. But the power of AI extends past the initial screening.

These assessments can be used for ongoing monitoring throughout a patient’s care journey. Patients can share their experiences in their own words, from the comfort of their own environment, during regular check-ins or engage using an AI-powered virtual support agent. These tools eliminate potential bias or apprehension that might arise in face-to-face interactions and provide a wealth of data for ongoing monitoring. 

According to a recent study conducted by Videra Health with Serious Mental Illness (SMI) patients, 90% of those surveyed expressed a desire to use the Videra Health app/platform on a monthly basis for monitoring their mental health condition. This high percentage was consistent across all levels of depression severity. Additionally, 95% of the patients reported having a positive experience while using the platform.

Furthermore, in the same study, 32 providers were also surveyed about their willingness to use an app/platform for monitoring their patients’ mental health. An overwhelming 92% expressed interest in learning about the severity of their patients’ symptoms through the platform, while 96% liked the convenience, timeliness, and access to screening that it provided.

In essence, AI can act as your virtual eyes and ears, offering those crucial “20 seconds” to gauge a patient’s emotional state and overall well-being. This empowers you to intervene early, personalize treatment plans, and ultimately deliver better care to your patients.

Transforming Care Management with Continuous Monitoring

By capturing the patient voice and analyzing these nonverbal cues during ongoing check-ins, AI assessments offer significant benefits for care management across all healthcare organizations, including behavioral health centers:

  • Improved Patient Engagement and Adherence: The ability to share their experiences in their own words empowers patients and fosters a more collaborative relationship with their care team and build trusts. Regular check-ins using AI can also help with adherence to treatment plans.
  • Early Detection of Changes and Relapses: By analyzing subtle variations in speech, facial expressions, and body language during monitoring, AI can help identify potential changes in a patient’s condition or signs of relapse at an earlier stage, allowing for timely intervention and improved patient outcomes.
  • Empowering Providers to Deliver More Effective Care: The rich data gathered by AI assessments provides care managers with a more comprehensive understanding of a patient’s condition and progress over time. This empowers them to personalize treatment plans, adjust interventions as needed, and ultimately deliver more effective care to more patients.
AI Analyzes the Nuances for a Holistic View

AI technology goes beyond simply capturing words; it analyzes the subtle nuances of communication, including:

  • Facial expressions: Tiny changes in facial expressions, like micro-expressions of sadness or anxiety, can be detected by AI and provide valuable insights into a patient’s emotional state.
  • Speech patterns: AI can analyze speech patterns for changes in tone, pitch, or fluency, which can be indicators of depression, anxiety, or other mental health concerns.
  • Body language: Crossed arms, fidgeting, or lack of eye contact can all be indicators of a patient’s emotional state. AI can detect these subtle cues and provide valuable insights to care managers.
AI Integration for Seamless Monitoring:

AI technology seamlessly integrates with existing care management workflows. For instance, consider chronically ill or at-risk patients who participate in regular care management calls. AI-powered video and voice technology can be implemented alongside these calls, offering a deeper understanding of the patient’s mental health beyond simply discussing physical symptoms. This empowers care teams to provide more comprehensive support and improve overall patient well-being throughout the care journey, not just during initial screenings.

AI assessments represent a significant leap forward in patient care management. By capturing the patient voice and analyzing subtle cues during ongoing monitoring, this technology empowers healthcare organizations to deliver more effective, personalized, and efficient care. As AI continues to evolve, we can expect even greater advancements in the field of patient-centered care.

If you would like to see one of Videra Health’s services in action, please visit TDScreen.ai and sign up to take a free virtual screening.