Mental, Behavioral, & Brain Health Startups: Q2 2022 Market Update
THE HEALTHCARE TECHNOLOGY MARKET
While publicly-traded growth equities have already suffered severely as a result of rising interest rates, healthcare venture capital fundraising has continued at a healthy pace despite the macroeconomic challenges. Part of why investors in healthcare venture capital continue to attract capital is the swift collapse of frothy valuations due to a general shift away from risk assets.
Anecdotally, we surveyed a few brokers for secondary shares of three mental health tech unicorns. The average discount (from each company’s previous round) was ~41% and ranged between 33% and 44%. While venture investors remain cautious illustrated by reduced investments levels, over the next few years — these slashed valuations represent a buying opportunity.
Beyond interest in healthcare innovation by venture investors, we have seen big tech make moves in health as well. Examples include Amazon’s acquisition of One Medical and Apple’s internal investment in healthcare detailed in their 80-page report published in July 2022.
Even though interest in healthcare technology will likely sustain in the long-term, SVB reported that “Q2 investment in healthcare technology dropped 40% from Q1.”
According to SVB:
Early-stage was highlighted by larger series A financings, mostly in clinical trial enablement. Later-stage financings fell shy of 1H 2021’s blockbuster record. While mega-rounds ($100M+) continued, that activity declined significantly in Q2. Alternative care investment declined while provider operations investment remained strong.
The short-term outlook for healthtech is not expected to match the exuberance of 2021. SVB predicts healthtech investment pace to continue to slow in 2H 2022.
THE LONG-TERM FUTURE OF MENTAL HEALTH INNOVATION
Of course, mental, behavioral, and brain health innovation (the focus of GIMBHI) are niches within healthcare technology. Over the last few years, we have seen mental, behavioral, & brain health startups attract a slightly larger share of funding (compared to total healthtech funding). With that said, other market dynamics have more or less tracked to dynamics seen in the larger healthcare technology industry. While in the short-term there will certainly be volatility and change across the healthcare technology landscape, I thought we’d shift our focus to the future.
Healing, written by former NIMH director Dr. Tom Insel, gives us some ideas for the direction and future potential of mental, behavioral, and brain health innovation (Chapter 10: Innovation):
Objective measurement and biomarkers
“There are no biomarkers, indeed no validated objective measurements tools, for people with mental illness, as there are for those with diabetes and heart disease. But technology offers innovative tools for measurement and for treatment based on measurements. Beyond that, technology could ultimately offer us a range of digital interventions and improved care managements, or smart tools to solve some of our greatest challenges, such as the problems of access, quality, and precision we visited earlier in the book”
Solutions addressing SMI
“Many digital mental health companies founded by software engineers, are developing financially successful companies that match patients to therapists or provide online meditation apps, but so far there is little innovation that is reducing death and disability for people with SMI.”
Privacy and regulation
“Many of the critical analyses for mental health could be done in the phone, so the data never leave unless the owner decides to share […] Beyond consent and privacy, there is currently no regulatory framework for ensuring quality or compliance with best practices.”
Leveraging media as a gateway for care
“While social media sites have increasingly become a haven for doom scrolling, privacy hacking, and toxic positivity — could they also be a gateway for care? After all, social media sites are where people are connecting.”
Chatbots for mental health
“Not only could a single online [chat]bot serve millions of users, it gets better as it scales. Will people find this approach acceptable?”
CHALLENGES AND OPPORTUNITIES FOR MENTAL HEALTH CHATBOTS
Data Input
One limitation of chatbots is access to certain types of data. When a chatbot interacts with you, the chatbot could plausibly have access to an entire database of texts, physical activity patterns, social media usage. And it’s not just the data — it’s the analysis and interpretation of that data. For example, based on texting frequency, social media usage, sentiment analysis of language, physical activity, location tracking, medical history, and other data, the chatbot may ascertain that you went through a depressive episode last week, and it may adjust interactions accordingly. This is all assuming a chatbot has access to passive data collected through a mobile device. However, a chatbot could plausibly be missing lots of data that a human therapist would typically consider and integrate into care. Complex social & cultural context, body language, and other social & physical data that chatbots are not equipped to analyze and integrate yet.
Trust & Credibility
But even more importantly, a chatbot may not be able to build rapport and trust between a human patient to get access to get important patient-reported information. There are many factors that affect trust and credibility with a patient and human therapists may be better at building this than a chatbot. And trust and credibility with patients is critical because not surprisingly studies show that trust and credibility are integral in driving positive outcomes.
Currently, we are speaking to researchers about the prospect of leveraging AI for hyper-personalization of chatbots & avatars to optimize outcomes. Studies show that voice, attire, disposition, personality type, environment, gender, age, attractiveness, appearance, and many other factors affect patients’ perceptions of healthcare providers and ultimately affect adherence and outcomes. At GIMBHI, we believe the future of AI chatbots in mental health ultimately includes the development & refinement of personalized 3D avatars. Synthetic media technology has advanced to the point that it’s possible to generate realistic video and speech of avatars ranging from cartoon-looking robots to avatars that look and sound like real humans. 3D avatars will achieve trust with users beyond what is capable with simply an AI chatbot. In the healthcare setting, trust and credibility is integral to adherence and achieving outcomes.
Psychotherapeutic Approach
Currently, chatbots are somewhat limited in what psychotherapeutic approaches they employ. Most chatbots employ a variant of behavioral therapy. The objective of CBT is to reinforce positive behaviors and reduce negative behaviors. Psychoanalytic therapy deals with the complexities of the unconscious mind, memory, and personality. We have yet to see chatbots that “practice “psychanalytic therapy.
NLP Models
Foundation models (typically open-source pretrained language models) basically define the limits of what chatbots can do, unless a chatbot developer builds their own NLP model. Examples of these would be OpenAI’s GPT-3 and Google’s LaMDA. Researchers at UPenn published their findings of the biases of language-based models in AI mental health applications. “NLP models may entirely miss out cultures who express their suffering through vocabulary that differs from the existing standards found in the medical literature.”
Conclusion
As chatbots continue to become more sophisticated with the advancement of NLP technology and language models, it seems that these will compete primarily with digital health apps and interactive software which offer less interactivity. Digital health apps have notoriously low adherence rates, but we believe chatbots which offer on-demand personalized interaction will become preferable to users. In the long run, the question is can AI chatbots seamlessly mimic real human interaction and relationships? If the answer is yes, then that certainly will extend to the patient-therapist relationship as well.
CHARTS
GIMBHI LANDSCAPE
Rather than categorizations focused on the business model or target client, our methodology is product-focused. For example, Unmind is described as a workplace mental health platform that empowers employees to measure, understand, and improve their mental wellbeing. We put Unmind in the Interactive Software category because the platform offers self-guided programs and interactive courses focused on mental health. The Wellness category includes startups focused on mental, emotional, and cognitive wellness which would include meditation, mindfulness, positive psychology, sleep, and emotional well-being, among other areas. Measurement, testing, and diagnostics include startups such as Lineagen (a startup that provides genetic evaluation services for autism and developmental delay) and Neurotrack (a developer of digital cognitive health solutions to help individuals assess and monitor their cognitive health). Interactive Software includes companies that develop software to help treat or cope with mental health disorders, intellectual disabilities, or developmental disabilities. However, this category would exclude prescription software therapeutics. The Provider Tools category includes tools primarily targeted at mental healthcare providers such as Valant (EHR behavioral health software for private practices). Digital Therapeutics includes startups that have developed digital therapeutics or have stated an intention to develop digital therapeutics. We use the category of Tech-Enabled Treatment Platforms to include startups that leverage technology to provide mental healthcare but still offer traditional, in-person care delivery. This includes startups like TwoChairs and Eleanor Health. Patient Tools include tools created for patient or consumer use. The Telehealth bucket includes startups primarily focused on telehealth with a focus on mental health such as Talkspace and AbleTo.
Subscribe to our research & market intelligence, and join our network. Feel free to reach out to our founder, Shivan Bhavnani (shiv@gimbhi.com) with any questions, comments, or ideas.
