Quartet Health



About 50% of the US adult population suffers from a physical condition, and a further 33% of those also have a mental illness. However, although there are people who suffer from both, it is often only the physical condition that gets treated. Studies have shown that people with physical health issues (heart failure, for example) and an untreated or undertreated behavioral health issue (such as depression or anxiety) cost 2-3x more for treatment of their physical conditions. Per 2012 data, those patients accounted for almost $300B annually in excess health care spend, mostly attributable to use of medical (as opposed to behavioral) services. Given that this is the total expected cost savings that a potential solution would provide to patients and insurance companies, we believe this is a reasonable estimate of the market size.




Quartet Health fixes this problem by bringing data-driven predictive analytics and recommendations to connect untreated or under-treated patients with physical ailments to the correct mental health providers, leading to a comprehensive treatment plan. Quartet accomplishes this by tapping into big data to identify people within a primary care system with undiagnosed or untreated mental health conditions. Quartet Health partners with insurance providers to analyze millions of insurance claims and flags patients with comorbidities or who have not been treated for behavioral health issues; it can combine results from behavioral health screenings with the patient’s data to reveal who might benefit from behavioral healthcare. It also matches those patients with local behavioral health providers who accept their insurance and who can meet both face to face and via telemedicine. Going forward, it then notifies the primary care doctor and follows up to make sure patients keep to their appointments, checks clinical results, and calculates the cost of care.



Since hospital systems and insurers pay for Quartet’s platform, it’s free to use for patients as well as primary-care doctors and behavioral health specialists and hence using Quartet’s system lowers costs. Through partnerships with insurers and healthcare systems, health plans pay per member, and compensation is tied to quality of care and cost reduction. The shift from fee-for-service to value-based payment rewards providers for patient outcomes, compelling them to provide superior holistic treatment.


This also positively impacts patients’ lives because their treatment will be more comprehensive, effectively targeting both the physical and mental conditions, enabling them to live their lives more comfortably without need for return hospital visits and unnecessary expensive bills. The tool will allow doctors to proactively diagnose mental issues before they become exacerbated, leading to a) effective treatment of the mental condition and b) positive side effects in assisting the recovery of the source physical issue.


Competitive Landscape


Although the use of augmented judgment in diagnosing mental illnesses has a lot of potential, it is still in its early stages. For instance, NeuroLex is pioneering a computer model that predicts the onset of diseases, such as psychosis and schizophrenia, by collecting and analyzing patients’ speech samples for patterns (i.e. pauses in the words, use of determiners, etc.) that can be indicative of each disease. Other companies that operate in this space include New York-based AbleTo, which announced a $36.6 million raise for its behavioral health platform, U.K.-based Ieso Digital Health, which raised $24 million for a platform that offers psychological therapies and cognitive behavioral therapy, and Talkspace, which has raised $59 million to offer on-demand virtual therapy via instant messaging. Related to this are meditation platforms such as Headspace, which recently closed a $36.7 million round, while Y Combinator alum Simple Habit raised a small $2.5 million round to help the world destress.


To the best of our knowledge, however, the only other company taking a similar approach in using predictive analytics at scale to better align patient, provider, and insurance outcomes is Clover Health. We feel Quartet Health can compete with Clover because of its exclusive focus on behavioral health, as opposed to Clover, whose stated goal is overhauling the entire health insurance industry using big data.




Quartet Health’s ability to create and extract value is tightly coupled to both the quality of its predictions and the quality of behavioral healthcare that patients receive. If they can improve their models’ precision without decreasing recall, they will identify more patients for whom they can improve care while reducing health providers’ costs. If they can improve health outcomes for identified patients, say by improving its matching to the right healthcare providers or by increasing patient compliance, this will further extend those benefits. To improve their predictions, Quartet Health should refine its algorithm by: (1) collecting more data on patients with confirmed physical and mental illnesses (so that causal patterns which would otherwise go unnoticed are identified) and (2) increase both the number of data points and types of data collected for each “potential” patient. Besides hospital records and current symptoms, studies have shown that an individual’s social media activity can predict suicidal intent or indicate mental illnesses. As such, sentiments, online behavior and notable changes in the way someone interacts with peers can be additional data points a patient can choose to provide to aid in diagnosis. The risk is that patients may feel their privacy has been compromised. To mitigate this, Quartet Health could first solicit doctor and patient approval, primarily monitor public activity, and only connect directly with the peers/friend-group of high-risk individuals.


Additionally, to improve patient outcomes, Quartet Health should incorporate more feedback loops so that it can provide recommendations based on what patients in the same age group and with similar conditions and medical records, found to be effective. Ways to do this include:


  • App-Doctor: The patient can answer questions and post daily logs on mental progress, and the application can give further instructions on what to do based on these inputs, ideally forgoing the need for a doctor in less-critical cases.
  • Patient-driven questionnaires: Have patients answer questionnaires when they are with their primary care doctors, and then use answers to immediately match patients to specific mental health professionals, drug treatments, etc. This has the added benefit of gathering data directly from patients.




















Siddhant Dube

Eileen Feng

Nathan Stornetta

Tiffany Ho

Christina Xiong

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