Launch HN: Osmind (YC S20) – software for better mental health therapies
Jimmy and I met at Stanford in a healthcare IT class at Stanford last year where I wrapped up my MBA and Jimmy is on leave from medical school. We’ve dedicated our personal and professional efforts to healthcare - I led operations and business at AI-driven neuroscience biotech Verge Genomics (S15) and Jimmy founded multiple healthcare nonprofits across digital health, care delivery, and research. Like many, we’ve seen too many of our loved ones fail to find the right mental health treatment for them. We realized we want to build something to help -- and we’re optimistic that neuroscience and psychiatry are on the verge of a revolution. We’ve built Osmind to maximize access to innovative mental health treatments for those who need it the most.
Over 20M Americans who suffer from treatment-resistant mental health conditions, which means they’ve tried and failed two or more conventional treatments. Oftentimes, finding the right mental health treatment means years of trial & error and suffering. On top of that, patients with treatment-resistant mental health conditions annually cost $900B+ in direct medical spend, twice as much as people with less severe versions of the same conditions. Researchers and doctors just haven’t been able to find mental health therapies that work well (conventional antidepressants have an estimated ~30% effectiveness rate). This is because pharma lacks sufficient understanding of mental health pathophysiology while clinicians lack the right data on what treatments work best for which people.
We approach this problem in two ways: 1) we build software for doctors and 2) generate insights for better development of therapies, treatment algorithms, and diagnostics.
First, we sell an electronic health record (EHR) to doctors working with treatment-resistant mental health patients. Our EHR enables doctors to measure how patients are doing in between appointments via an integrated patient mobile app to drive personalized, improved clinical decision-making. For example, we can use data science to automatically detect symptom exacerbation or improvement (from patient-reported outcomes or functional metrics such as activity levels) and get them in for treatments at the right time. This is a rarity for EHRs, especially in treatment-resistant mental health, which is known to lack evidence-based practices and consists of a difficult-to-treat patient population. Our EHR also automates administrative tasks such as collecting intake forms and getting reimbursement from insurance companies. Our ultimate goal is to make recommendations to the doctor on what treatments work best for people based on objective criteria such as their past medical history, demographics, and more. That way, doctors can deliver the best possible care, and patients can get better. We launched the software in June and are serving over 125 clinics nationwide covering over 20,000 patients receiving FDA-approved psychedelic medicine, neuromodulation, and general psychiatry treatment.
Second, we can extract insights from the software to find better and more precise ways of treating individuals. Our software above aggregates clinical, patient-reported, digital, and biological information, which has never been done at scale in mental health. For example, establishing more objective predictors of depression and correlating them with treatment impact can help us diagnose people more precisely and determine what makes one type of treatment better than the other. We can use this information to better design clinical trials that actually succeed, potentially saving billions of dollars of sunk costs to develop therapies that work. New innovation in mental heal...
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[ 3.3 ms ] story [ 76.9 ms ] threadI'm curious - how does your data consider racial biases when it comes to mental health?
Additionally, does your data take in consideration social determinants of health and health inequalities?
Thank you for bringing up the issues around racial biases when it comes to mental health. We hope to address this in multiple ways: 1) trying to incorporate objective forms of information such as digital and biological data that theoretically should be less biased 2) giving back to the community by supporting providers who serve underrepresented communities (we provide our software for free or significantly discounted to these community practices and are working with a number of them already)
And yes, we aim to incorporate various types of demographic information including social determinants of health.
It's great to be using quantitative measures, and almost a necessity, but as someone in this area who has done clinical work as well as research, I can say from personal experience EHR in mental healthcare has fumbled repeatedly with handling the complexities of behavioral information.
Good to see attention in this area though.
If a platform could separate my personally identifiable bits from my measurement data stream and if I participate in sending anonymous track of my health -
- at a large scale, there would be enough data to derive patterns and then insights (like you mention)
- I could take the generated insights and apply them as overlay on top of my own personal data again
In theory my personal bits never leave my app. But I contribute to a larger data driven improvement of health.
Let's stay in touch somewhere if you want.
Sounds good on paper but I am not sure if I would want processes that heavily collect psychometric data and automate insurance to coexist in the same company. It is a matter of a flip of a switch (e.g. repealing of ACA) before insurance companies might legally have access to that psychometric data, with sweet profits left to this company. Even though right now they are in HIPAA land, since that data probably doesn’t qualify as therapists notes, it won’t have the same protections.
My second issue is overfocus on treatments like ketamine, psychedelics etc. I get that they target treatment resistant population, but maybe having this additional psychometric data would have rendered more conventional treatments efficacious to begin with? Or this would be a therapy modality in itself? (Pervasive technology) Or maybe it is going to be completely neutral because the extra effort required from therapist to analyze and interpret this data is not going to be worth it, or the AI won’t match the sophistication of the problems? Biomarkers for mental ails is indeed the holy grail, but the signal to noise ratio with what we have without getting invasive is pretty low.
Sounds like right now the focus of the compnay is pretty diffuse waiting to pivot on whatever proves most profitable, which is perfectly fine. Hopefully it will end up being something useful to the humanity too.
One area where such tool could be extremely helpful is using supplements(and their lower side effects profile) where appropriate in order to minimize drug use.
That doesn't mean they aren't helpful, and some psychiatrists already use them.
And in particular, when were talking about vitamins, minerals that the body naturally gets , the risk profile is quite good.
The problem with patient-reported outcomes or other metrics, is that you are relying on the patient. This trap already exists for the vast majority of people suffering from mental health issues in the United States: in order to conquer your issues you basically need to not have them in the first place. Navigating (maybe battling) insurance, out-of-pocket bills, finding doctors who will take you seriously, and so forth, is really taxing, and when you are already struggling it can be Too Much.
I saw, second-hand, some of the surveys that Stanford sent to their disabled patient, which I would suspect would be used in the same way. The surveys are tiring, exhausting, and frustrating. It's frustrating to be asked the same questions over and over again while you tread water, and have to admit the same shitty answers, whether it's pain or depression or any other miserable symptom.
I am curious what you are doing here that is wholly different from what most institutions are already doing.
Most disability corps only care about maximizing revenue, and will look for any excuse (legal or not) to cut off a client. Often times they require full access to medical records, so what sort of information is in this records can be, for lack of a better term, life or death.
If a whether or not a patient left the house might be tracked and eventually accessed by an insurance agent, I would be extremely hesitant to use this product, and I know private disability insurance advisors that would be very afraid of this sort of thing, on behalf of their clients.
Google says your records are "anonymized" when they drop the last octet of your IP address. What do you mean when you say "anonymized"?
As an outsider who doesn’t know either of you I get concerned by:
>Jimmy and I met at Stanford in a healthcare IT class at Stanford last year where I wrapped up my MBA and Jimmy is on leave from medical school
It sets a red flag this being a MBA project, especially given neither of you have worked as a professional mental healthcare worker.
I don’t know either of you, but just sharing my initial honest impression based on your post.
There is a lot of stuff above about the size of the market and your technology, but not enough about why you’re so driven to solve this problem.
Hope you find this honest feedback helpful. (Edit format)
> We care deeply about patient and clinician privacy as well. Our platform is HIPAA-compliant and protected by end-to-end encryption. We work with independent third parties to verify our compliance and security. Patients own their health data and have the right to all of it. Any analysis we do is always on anonymized and aggregated information and never traceable back to an individual or clinic. We openly state that our mission to advance new treatments to the patients and doctors we work with and have found that the whole field is motivated to help - everyone realizes it’s an all-hands-on-deck movement.
do you have further infos?