This is part of a series I’m launching called “Tech Markets”, in which I will analyze various tech markets’ current state, including its major players, problems and opportunities for incumbent entrants.
What is behavioral health? It consists of:
- Clinical behavioral health problems that is typically diagnosed by a therapist ex. Depression, Bipolar disorder, Schizophrenia etc.
- Mental health improvement/self-improvement: Combat stress, gain more Happiness/Purpose in life etc.
Let’s focus on clinical behavioral health first.
The major stakeholders include:
- Indirect: Insurance providers ex. Aetna Insurance
- Indirect: Government providers ex. Medicare
- Supply: Therapists/Licensed Clinical Social Workers/Counselors/Psychiatrists
- Supply (Location): University Counseling Centers/Private Practices/Outpatient departments within Hospitals
- Demand/Users: Patients diagnosed and seeing a therapist
- Demand/Users: Patients undiagnosed but seeking mental healthcare (both users who are aware and unaware)
Each stakeholder faces different problems in their own area and their interactions with each other.
- Therapist: don’t have enough data on patients outside therapy sessions
- Insurance providers: would love to give their clients more resources
- Patients: get lost in the process of transferring from inpatient to outpatient behavioral clinics
- Hospitals: not advanced enough for high-tech solutions
- Patient-facing: Lantern.io, Talkspace, 7 Cups,
- For therapists: TAO
- For Insurance providers: Quartet Health
Opportunity (Market Stats):
The mental health tech market is expected to reach $2.31 billion by 2022 (CB Insights), and the predictive health market is expected to reach USD $19.5 billion by 2025 (Grand View Research, Inc.) There is projected to be $237 million in funding to mental health companies in 2017 (CB Insights).
Opportunity (My Opinion):
I believe there are many opportunities for growth in this market. Statistics show that most people face a mental health problem at some point in their life, so a tool that caters to this problem is definitely needed.
- Expansion in China:
Mental health in China is just beginning to gain traction. There is large opportunity for growth, especially tools in diagnosing and educating the public about this issue.
- More data for therapists:
Currently, various counseling centers either collect data in an ineffective way (as USC’s Engemann Center does with its weekly survey questions) or don’t collect at all. Without understanding users’ behavior during the rest of the week, it is difficult to provide and create a targeted treatment plan. Possibly, the current strategies are ineffective but there is no way to track or evaluate that other than asking the patient, whom may be unreliable as a data source (if mental illness gets in their way).
- Better prediction (aligned with AI and machine learning):
It would be especially powerful to have an AI-powered predictive tool for bipolar disorder, especially because it is so difficult to predict manic episodes while they are so devastating on the patient’s loved ones and family members.
- Collaboration with corporations & university centers
With corporate wellness on the rise and becoming more important especially as a PR move, companies are more willing to dedicate dollars to funding wellness initiatives to attract good talent and show that the company values work-life balance and cares about its employees.
Currently, companies such as Headspace, Stop Breathe & Think, and others are optimizing on this demand by creating corporate solutions (integration with Slack/Spotify).
My Own Venture Idea:
“SmartMood”, an AI-powered personal mental health tool for patients and therapists, which includes a mobile app with sensors for tracking patterns of user data and a web or mobile analytics dashboard in which patients can opt-in to sharing their data with therapists or family members. Using sensors in the user’s devices (such as mobile phone or wearables) that tracks aggregate patterns of the users’ call activity, location, and sleep, the software can predict potential triggers of disordered episodes (ex. when fewer texts are sent, or there is suddenly lower physical activity), and suggest recommendations – activities or tips that the user or therapist suggested. This creates “smart” predictions and personalized treatment, which would be more reliable.
Ideally, this solution would be marketed towards counseling centers, which include either private practices, university centers, or outpatient departments at hospitals, that would pay for a subscription annually or monthly depending on the number of patients, and their patients would either use the app for free or pay a small fee (ex. $5) to cover the cost. This cost could also be covered by insurance providers or even universities and schools that may want to help their students/clients.
Let me know if you have any comments or opinions on this. Thanks!