After being inspired by a Stanford course, four undergraduates teamed up to tackle important deficiencies in mental healthcare while expanding access and reducing costs.
After seeing inadequate mental healthcare profoundly affect family and friends, Maurice Chiang, MS ’20 was determined to find a better way. Guided by the Stanford Byers Center for Biodesign’s need-based approach to innovation, Chiang worked with a team of fellow Stanford graduates to build a new model for mental healthcare delivery.
Chiang, along with Benson Kung, BS ’20, Aaron Kappe, MBA ’20, and Jin Woo Yu, BS ’20, developed a system that uses data to inform treatment decisions, telemedicine to improve patient access and care continuity, and a direct-to-consumer approach to bypass traditionally poor mental health insurance coverage and misaligned care incentives. Their goal: drive better outcomes for patients at a significantly reduced cost.
Chiang, who earned his undergraduate degree in bioengineering before completing a master’s in computer science, used the biodesign process to understand problems in mental healthcare before conceptualizing a solution. He first learned this systematic approach to health technology innovation as a student in Bioengineering Senior Capstone Design.
"The Capstone course prepares engineering students for the real world by asking them to identify an important problem, understand it from the perspectives of all involved and then develop a novel, technology-based solution," said course co-leader Ross Venook, PhD , a bioengineering lecturer and the assistant director of engineering at Stanford Biodesign.
"In the course, Maurice and his team had worked on a project to reduce opioid dependency, so when he brought up mental health in a subsequent meeting, I was surprised. My surprise turned to curiosity and then excitement as Maurice described the unmet clinical need he was pursuing and the way he was applying the biodesign tools we used in class to this project," said Venook, who became an informal advisor to the team.
Chiang started by interviewing hundreds of patients, providers, payers and others. "I wanted to thoroughly understand the ecosystem so I could find a way to improve care for patients while also delivering value to the other stakeholders," Chiang said.
Using data to drive first-line treatment decisions
Based on his research, one of the first problems Chiang set out to address was the way antidepressants are prescribed to mental health patients. "There is significant variability in the way people respond to these medications," he said. "Psychiatrists approach this largely by trial and error, meaning they try something, wait to see how it works and then try something else." As a result, patients can spend months struggling with ineffective medications and/or adverse side effects.
"This is the same approach that has been used since the 1980s," Chiang said. "I wanted to use data to advance patient care and prescribing practices." He explained that while it’s not yet possible to use a patient’s genetic information to create a personalized medical regimen, there are ways to use genetic testing to better understand how a patient will metabolize drugs.
The key to this is the six enzymes (proteins) that are largely responsible for breaking down drugs in the body. These enzymes aren’t identical in everyone; in fact, most people have variations of one or more of them that affect the way their bodies process medications. Because differences in certain genes correspond to the differences in the enzymes, genetic testing can identify the variants and help predict individual medication response.
"For example, if a person has a variation that causes them to metabolize an antidepressant more slowly than average, the antidepressant will stay in their system longer, increasing the likelihood that it will cause side effects like nausea and fatigue," said Chiang. "If the physician is aware of this, they can modify that patient’s prescription proactively."
Working with teammate Kung, Chiang also sought to leverage data to help providers understand how patients similar to theirs had responded in the past. "The National Health Service in the UK has one of the world’s largest psychiatric clinical datasets," said Chiang. "We use longitudinal data drawn from years of patient health records and other variables to paint a picture of patient outcomes on certain medications over time." This information, along with the genetic tests, helps drive more informed treatment decisions that give patients a better chance of receiving a medication that works for them the first time, resulting in fewer adverse side effects, lower costs and faster recoveries.
Improving access and continuity of care
Another problem Chiang set out to address was access. "It typically takes three to four weeks to get a first appointment to see a provider, and follow-up visits can be six to 12 weeks apart," he said. To shorten that timeline, the team began experimenting with telemedicine. "With virtual visits, we can connect a patient to a provider within a day," said Chiang.
To improve care continuity, a care manager is assigned to check in with the patient regularly. "There’s a lot to be gained from a non-therapist support person," said Ronald Albucher, MD , the former director of Counseling and Psychological Services at Stanford’s Vaden Student Health Center, who serves as an advisor to the team. "Improved support is associated with a reduction in symptoms, especially when someone is going through a crisis or a new onset mental health problem."
The team also tackled the high cost of treatment. "Insurance coverage for mental healthcare is frequently inadequate," said Albucher. "Patients are required to choose from a small number of in-network providers, only to find out that most aren’t taking new patients or are retired. It makes it hard to find a provider who is a good match. And then there is often a cap on the number of visits."
To eliminate these problems, the team decided to use a direct-to-consumer business model. This approach, in combination with virtual visits, strips out billers, payers, offices and most administrative costs, and makes it possible to deliver comprehensive mental healthcare that includes genetic testing and medications for a flat fee of $99 per month.
The business model is largely the brainchild of Kappe, who got interested in entrepreneurship while a student in the graduate-level Biodesign Innovation course. Kappe said it was the most impactful course he took at the Stanford Graduate School of Business. "The course demystified the process of starting a company by breaking it down into components - from understanding the need you’re trying to solve, to how different stakeholders interact, how to think through regulatory paths and options and how to develop a go-to-market plan that makes the business sustainable," Kappe said.
The company they formed is called Prairie Health. Kappe leads its business strategy and growth. Chiang is the CEO, Kung heads research and development, and Yu heads product and engineering. All four are co-founders, and share interwoven connections to Stanford Engineering, Stanford Biodesign, the Stanford Ventures Technology Program and ASES, a global student entrepreneurship program.
"It’s a very interesting model that hasn’t been tried before," said Albucher. "They are doing an impressive job of trying to address important deficiencies in mental healthcare."
Chiang said he and his team members are trying to "walk the walk" when it comes to providing value-based care. "A patient with anxiety and depression incurs an average of $5,000 to $8,000 more in medical claims per year," Chiang said. "If we’re able to improve patient outcomes three times faster, that’s better for the patient and a significant cost savings."