Patient Safety & Quality Healthcare (PSQH): The Importance of Between-Visits Appointments and Mental Health

In a recent feature for Patient Safety and Quality Healthcare (PSQH), our CEO Loren Larsen highlighted the significance of in-between appointments and their impact on mental health.

The US faces a mental health crisis with surging demand and limited access to care. Traditional models struggle with long wait times and a lack of monitoring between appointments.

This article explores how telehealth and AI can bridge the gap:

  • Remote video appointments: Increased accessibility and reduced no-shows.
  • Patient-recorded videos: Capture mental health episodes and track progress.
  • AI analysis: Analyze video/audio to identify urgent cases and prioritize care.

This hybrid clinician/AI approach has the potential to:

  • Reduce strain on mental healthcare systems.
  • Meet patient needs more effectively between appointments.
  • Offer earlier intervention and improved outcomes.

Read the full article here

The Transformational Role of AI-human Collaboration

In an age where some systems are still transforming from paper records and assessments, the meteoric rise in tools using Artificial Intelligence risks having some providers left behind.  It doesn’t have to be this way.  For the past few years Videra Health has focused on helping mental health providers transform their business and use technology and AI to generate better results for their patients and bottom line.  The capability of the technology is changing so rapidly it may be out of date between when I post it and when you read it, but as of now I want to share some of the core opportunities I see for how AI can help us drive our much needed revolution in mental health.  As we continue to understand the human mind better, AI can play an indispensable role in not only screening for, diagnosing mental health issues but also recommending effective treatment methods and even predicting future occurrences .

In this blog, we explore how AI will reshape the future of mental health care, specifically discussing how we can measure mental health more accurately, provide automated advice to clinicians, continually improve AI, and reduce inherent human bias. It will also delve into the concept of AI-human collaboration to achieve optimal results.

Enhanced Measurement of Mental Health Using AI

Assessing mental health has always been challenging due to its subjective nature, making it difficult to measure accurately. AI, however, provides a potential solution to this conundrum. Through the application of machine learning algorithms, AI can analyze a vast array of data, including language patterns, behavior changes, social media usage, and even biometric data. This holistic view enables AI to identify subtle patterns and correlations that might be missed by human practitioners, potentially leading to earlier detection and more accurate diagnosis of mental health conditions.

Automated Advice to Clinicians for Improved Care

AI also offers promise in augmenting the capabilities of mental health clinicians. By leveraging vast amounts of data, AI can generate insights about treatment efficacy based on a myriad of factors such as demographics, specific symptoms, and past treatment responses. For example, AI could help determine which interventions work best for patients with specific comorbid conditions, or which medications are least likely to result in unwanted side effects for a particular patient.

Moreover, AI can be used to monitor patient progress in real-time, providing ongoing feedback to clinicians and enabling them to adjust treatment strategies as needed. This could lead to more personalized, dynamic care plans that adapt to the changing needs of each patient.

AI’s Continuous Improvement and Bias Reduction

One of the unique aspects of AI is its capacity for continuous learning and improvement. As more data becomes available and as algorithms are refined, AI’s accuracy and predictive capabilities are expected to improve. Over time, this will make AI an increasingly powerful tool for mental health care.

Furthermore, AI can help reduce bias in mental health treatment. While human clinicians can be influenced by a range of factors such as their own experiences, prejudices, or the limited number of cases they have been exposed to, AI can offer a more objective view. By analyzing a vast number of cases and variables, AI can provide a more balanced and data-driven perspective, thereby mitigating the risks of human bias.

The Power of AI and Human Collaboration

While AI holds great promise, it’s essential to remember that AI is a tool, not a replacement for human clinicians. The future of mental health care should not be a choice between AI and human clinicians but rather a harmonious integration of the two.

AI can manage and analyze vast amounts of data, identify patterns, and generate insights far beyond human capabilities. However, human clinicians bring critical components to the table that AI currently can’t replicate – the ability to empathize, to understand context beyond data, and to provide the human touch that is so integral to mental health care.

By combining the strengths of AI and human clinicians, we can create a synergistic partnership that brings the best of both worlds to the table. This approach has the potential to deliver more accurate diagnoses, more personalized treatment plans, and ultimately, better outcomes for patients.

The future of mental health care is undoubtedly exciting. As AI continues to evolve and integrate with human clinicians, we can expect a significant shift towards more proactive, personalized, and effective mental health care. However, it will be important to navigate this journey with caution, ensuring ethical use of AI and preserving the inherent human aspect of mental health care.

Improving Outcomes and Quality of Care with Measurement-Based Care in Behavioral Health

Behavioral health, encompassing mental health and substance use disorders, is a critical and growing concern for individuals and healthcare systems globally. In the United States alone, staggering statistics highlight the prevalence: one in five adults experiences a mental illness annually, and nearly 20 million battle substance use disorders (National Institute on Mental Health, 2020). These conditions not only impact individual health and well-being but also strain the healthcare system, leading to high costs and diminished quality of care.

Fortunately, a groundbreaking solution – Measurement-Based Care (MBC) – empowers healthcare providers, payors, administrators, and behavioral health professionals to fundamentally transform care delivery. This blog post explores the role of technology in advancing MBC practices, demonstrating its contribution to improved outcomes, cost reduction, and proactive care.

The Advantages of Measurement-Based Care:

MBC is a systematic approach to treatment that involves measuring progress and outcomes through standardized assessments and surveys. Technological integration enhances MBC by providing healthcare providers with precise and personalized insights into patients’ conditions, unlocking several advantages:

  • Enhanced Patient Outcomes through Precision and Personalization: Digital video assessments, a cornerstone of MBC, enable detailed and accurate analysis of a patient’s condition. This precision allows for personalized care plans built upon individual needs, leading to improved outcomes. A study by Health Services Research found that MBC significantly improved patient outcomes compared to traditional care, with a 62.4% increase in the probability of achieving positive outcomes.
  • Cost Reduction in Behavioral Health Care: Beyond improved outcomes, MBC can lead to long-term cost savings for both providers and payors. As MBC allows for continuous monitoring and tracking of progress, it helps identify issues early on and prevents unnecessary hospital visits or interventions. A study published in the American Journal of Managed Care found that implementing MBC in community mental health centers led to a significant reduction in hospital visits and general healthcare costs. In addition, MBC helps reduce redundancies in treatment and ensures that patients receive the most appropriate and effective care, resulting in cost savings over time.
  • Proactive Intervention and Continuous Monitoring: MBC facilitates proactive intervention and continuous monitoring, significantly impacting patient outcomes and cost reduction. With the help of remote patient monitoring, behavioral health professionals can identify potential issues before they escalate and provide timely interventions. A study by the Agency for Healthcare Research and Quality found that implementing MBC interventions in community health centers resulted in a 32% reduction in psychiatric hospitalizations.

Innovations in Technology Elevating MBC:

Technology serves as a key driver of progress in MBC practices, and continuous innovation further elevates its effectiveness:

  • Natural Language Processing (NLP) for Enhanced Patient Insight: NLP utilizes algorithms to analyze patient language data, providing crucial insights into mental state and potential risks. By differentiating genuine risk from benign statements, NLP helps prevent clinician overload and accurately identify individuals requiring intervention. Early risk identification can significantly improve patient outcomes and reduce healthcare costs.
  • Broader Data Analytics for Comprehensive Behavioral Health Monitoring: MBC goes beyond NLP, utilizing broader data analytics such as electronic health records, insurance claims, and pharmacy data to gather insights into a patient’s behavioral health. Leveraging large language models and data science can further refine MBC strategies, tailoring them to individual patient needs. This holds immense potential for proactively identifying and addressing behavioral health needs in the population.

Operationalizing MBC within Healthcare Systems:

Healthcare systems must effectively operationalize MBC practices to fully realize their benefits. This involves training and educating healthcare teams on MBC protocols, while integrating digital tools into existing workflows. Additionally, engagement from payors and policymakers is crucial for supporting the adoption and sustainability of MBC practices.

The Future of Healthcare: Innovations in MBC and NLP:

The future of healthcare is bright and brimming with possibilities, especially in the field of behavioral health. As technology advances, we can expect even more groundbreaking developments in MBC and NLP, transforming how we deliver and experience healthcare. With the potential of MBC to enhance patient outcomes and reduce costs, it has the power to revolutionize the healthcare landscape entirely.

MBC, coupled with technology, offers a powerful solution for improving patient outcomes, reducing costs, and delivering proactive care in behavioral health. As the healthcare industry evolves, embracing these innovative technologies and strategies is essential for expanding the boundaries of patient care. By combining the power of AI, data science, and human expertise, we can create a brighter future for behavioral health and overall healthcare.