Mental health is in the spotlight right now, particularly in light of recent events. The National Alliance on Mental Illness estimates that approximately one in five adults experiences mental illness in a given year, and one in five youth aged 13–18 experiences a severe mental disorder at some point during their life. And the early years are critical, as half of all chronic mental illness begins by age 14.
Those numbers inflate drastically when examining low-income populations. Mental health disorders contribute to school dropouts, suicides and incarceration, at a staggering cost: over one-third of students with a mental health condition age 14–21 and older will drop out of school.
Mental health issues stem from a variety and combination of factors — genetic, environmental and behavioral — but undoubtedly, require highly-trained, personalized mental health care to generate positive progression and outcomes. Unfortunately, the current healthcare system targeted at low-income populations often results in fragmented and shallow care.
Psychologist Dr. Yared Alemu is trying to change these outcomes with the help of data analytics. For over a decade, Alemu worked closely with and studied the publicly-funded mental healthcare system and saw firsthand how it failed to solve the issues seen in children in low-income communities.
“Innovation in the behavioral healthcare ecosystem, focused on the most vulnerable and the least fortunate individuals, has been considered high-risk and attracted limited technological innovation, funding and expert attention,” says Alemu. “The ongoing lack of innovation has contributed to an epidemic of mental health disorders that incapacitated youth from low resource communities.”
Alemu believes that the first problem is a lack of measurement. Mental health professionals dispatched by government-funded organizations to these communities fail to track outcomes over time, resulting in a dearth of data around what does or doesn’t work.
In 2016, Alemu founded TQIntelligence, a data analytics health platform specifically geared towards mental health professionals. The dashboard uses a clinically-validated 26-question survey taken by the provider, the parent and the child immediately following treatment.
Rather than focusing on diagnosis, which Alemu says can be inconsistent and ineffective, the TQIntelligence platform tracks severity and functionality of the patients.
“The DSM [the diagnostic handbook for mental disorders and illnesses] isn’t even applicable for these cases,” he says.
The platform’s machine learning algorithm provides recommendations to the provider based on what it has learned in the past.
TQIntelligence is also soon rolling out a feature to analyze voice to identify emotional distress. The provider will collect a 90-second voice sample from the patient, and the platform uses indicators in the voice (unrelated to the content of what they’re saying) to corroborate the treatment plan.
“Voices are one of those human universals that really aren’t used to the extent that they should be, especially in behavioral health,” says Alemu.
Instead of focusing on charging the patients or insurers, TQIntelligence targets publicly-funded organizations that work solely in low-income communities. They employ an annual SaaS revenue model to provide caregivers in these organizations with the platform and tools; they also train the caregivers on how to use it.
The startup is currently in the midst of two pilots, one in an inner city urban area and one in a rural setting. Between the two, Alemu says they can reach 8,000 patients; currently they’re working with about 1,000 through a few dozen caregivers.
To scale, they may require more capital. Initially funded with $150,000 by Nashville’s healthcare-focused JumpStart Foundry fund, they are looking into both institutional venture capital funding and SBIR grants. They also hope to transition into paid pilots sooner rather than later, once they have demonstrated the efficacy and need for their platform.
