Therapy at Web Scale, and other innovation in mental health
I’ve spent quite a bit of time looking at the mental health space. Part of this interest comes from an analysis of the health sector. We look for markets with low barriers to entry, not an easy thing to find in the world of health insurance, EMR, and hospital sales cycles. Mental health is one of the most most attractive subsectors in the health space for internet-focused investors:
One quarter of all American who receive mental health care list themselves as the primary payer. There is already a large customer pool operating outside of health insurance, suggesting that lightweight startups have a better chance of capturing market share.
Similarly, 45% of untreated Americans cited high costs as the primary barrier to care. New models that innovate by lowering price points may be able to tap )nto this unserved market.
Proportionally rising allocation towards outpatient care. Outpatient care rose from 24% of mental health expenditure in 1986 to 33% in 2005. While we don’t see exciting investment opportunities in prescription medications or inpatient care, the most interesting mental health startups are innovating in ways that will revolutionize the outpatient experience.
The Affordable Care Act clearly creates more mental health mandates, requiring all insurers to include mental health benefits. Plus, the act is being implemented right after most states slashed mental health budgets in response to the recent recession.
There are three categories of internet-based mental health startups:
Therapy at web scale.
My personal favorite, this is using technology to bring therapy itself online. While long distance therapy has been existence for quite a while (for instance, therapists using Skype for video sessions), we’re seeing a number of internet-based platforms to facilitate this behavior.
I think there is a massive gap between seeing a therapist and “nothing.” It takes both a huge financial and mental commitment to decide to see a professional therapist on a regular basis. Net-native therapy platforms have the potential to help the millions of people caught in the middle, people who probably should see a therapist but can’t bring themselves to make that leap.
TalkSession is a next-gen teletherapy platform. They are starting small and highly curated (there are 26 psychologists/psychiatrists listed so far), but aspire to grow into make teletherapy both universally accessible and affordable. While TalkSession looks like the most polished entrant, there is a tail of similar models. BreakThrough and Blah Therapy are quite similar. Stillpoint Spaces specializes in video therapy sessions with counselors trained in Depth psychology. Online Therapy is an older competitor.
The above platforms have taken teletherapy quite literally, upgrading from phone calls to vidchat over the internet. One of the most interesting things is that there is a huge market for text-based therapy. TalkTala offers both, but has found that most users prefer the asynchronicity and anonymity of texting. TalkTala also has a hybrid model with combines free, public posting (and response from therapists) with paid, private discussions with a therapist.
7 Cups of Tea is a p2p platform to connect with “listeners.” Founded by psychologists, they give listeners brief training on how to be a passive human presence and not an active therapist.While a true marketplace, the majority of listeners elect to offer their services for free. Blah Therapy, mentioned in category 1, also free non-therapist listeners. It’s an interesting idea that this kind of human interaction may be sufficient for non-serious mental illnesses.
Personality Cafe is an example of an older social network focused on mental health. Started as a social forum dedicated to the Myers Briggs type indicator, it’s a place for many people struggling to mental illnesses to connect with and support one another. In this vein, there are two new exciting apps, Secret and Whisper. While neither is directly related to therapy, I think both are relevant. The internet, in large part due to the anonymity it affords, has always been a place to vent and an outlet for personal struggle. I see both apps as progress towards better understanding what form factors can best facilitate these kinds of emotions and desire for human connection.
Find a therapist, i.e. ZocDoc for therapy.
Perhaps unsurprisingly there are few teams working to build an online marketplace for offline therapy. While I think offline therapy should and will continue to be the predominant form of mental health care, the entrepreneurial/investment opportunity is less exciting. By nature, seeing a therapist should be a long-term transaction. Unlike with ZocDoc, for which you may need to find many different types of doctors in a year, you’re probably sticking with a single therapist for quite a while. From a purely economic standpoint, it is much harder for an online marketplace to capture in an initial connection before the users (on both sides) go off-platform. That’s why there are many more teams working on teletherapy. The best directory by far is still Psychology Today’s Find a Therapist.
Many of these companies allow for mental health treatment while remaining anonymous. Of any medical profession, my instinct is that identity and in-person, human interaction is most important for therapy. Yet the market is tending towards the exact opposite: asynchronous messaging. My hope is that this is a good entry for people unfamiliar with or nervous about teletherapy or offline therapy, but that those who need it graduate to more in-depth forms of mental health care. If it becomes an important market, we’ll see more therapists taking advantage of the asynchronicity to use this as a supplemental form of income.
Another question is how far much the market will flatten. It’s possible that the willingness of altruistic non-therapists to act in some mental health care capacity, such as the free listeners on 7 Cups of Tea, will further compress the costs of engaging with a licensed medical professional, whether on- or offline. Is there a significant difference between texting a therapist vs. a “listener?” If there is, which will potential users unfamiliar with mental health care choose? Will there ever be regulation around this issue.
And, finally, an idea I like: technology here can actually lower the switching costs between mental health professionals. Currently, if you decide to leave your therapist and try someone else you must start from scratch. Despite telling your life story to someone, this new therapist knows absolutely nothing about you; it’s like going through a break-up and then starting over with someone else. But most of these online texting platforms save all of your conversations, which can be transferred over to a new therapist if so desired. Rather than go through everything again, a new therapist can read through a new patient’s history be quickly brought up to speed.
There is an overwhelming amount of innovation occurring in the cryptocurrency space. I mean that literally - it’s quite difficult to parse and understand each of the “alternative” projects being built on top of and/or around the Bitcoin blockchain.
To that end, I present a primer on what I believe are the 5 most interesting endeavors:
- Untapped capabilities BTC protocol itself, including smart property, scripting and proposed payments request mechanism
An understanding of these projects allows for a more coherent view on some major questions the cryptocurrency community is addressing. What other problems require a mechanism for distributed consensus? How should identity work in the cryptocurrency world? Should scripting be Turing-complete, or is that overkill? How flawed is BTC’s proof of work, and what other options are there? What does a future of distributed autonomous organizations look like?
I’ll offer my opinions in future posts, but the purpose of this research is to present an unbiased introduction to the technologies and some arguments from people far smarter than I am. To the latter point, the bottom of the document lists some of the best blog posts around these ideas.
In defense of my selection of what to highlight: I am interested in projects that, to an end-user, most differentiate themselves from our current use of Bitcoin as a “simple” currency. So, for example, while LiteCoin and PeerCoin have meaningful technical differences from Bitcoin (mining hardware and proof of stake, respectively), what they enable the end-user to do is not nearly as exciting as the AppCoins of Mastercoin or full Turing-complete scripting capabilities of Ethereum. Similar logic applies in many other cases, but I am happy to persuaded otherwise.
Again, the link is here.
Examining “Software Eating the World”
I recently read a blog post by Ethan Kaplan which argues that
…the world itself is now possible to represent by polymorphic functions with refactorable logic.
This is what we mean by “software eating the world.” Out of personal experience, technologists can often get excited by the application of logic to all of life’s decisions. So, I’d like to consider the two parameters along which this is occurring:
- We are identifying new, previously un-parameterized areas of life to replace with software.
- We are improving on preexisting functions.
The first is intuitive. 10 years ago, only a human could recommend a song to you. Then Pandora began writing algorithms to do it. In the past decade or so we have begun solving this for finding restaurants, movies, taxis, and even love. There are probably still many untouched fields to explore here (if I could think of them I’d build it!). As an investor, this happens to be the most exciting kind of opportunity to come across.
The second parameter begs an interesting question: What are the upper bounds to quantifying every variable? You can often see them directly in the product experience. Searching for a car online (to continue Kaplan’s central example) takes a list of inputs: make, year, price, horsepower, etc. But what if you get inside the car and the driver’s seat doesn’t go back far enough? Or the steering is surprisingly loose for your taste? Or just that, in person, the car doesn’t look quite as sexy as you’d hoped?
I think some of these variables may be inherently unquantifiable. Others disagree. But either way, some of the functions we use may already be approaching the limit^ of an inherent tradeoff given the current computing form factors we use: UX vs. time. Even if it were feasible for Cars.com to ask me all those questions, it’s much more human to just drive the car for 5 min. So, until the next technological breakthrough, how much more effort will we put into improving these functions?
At this point in history “software is eating the world” for only those parts of life that are most easily quantifiable.
:Commodities are particularly interesting, as they remove all human preference and are thus best served by a function.
Disaggregation of a Bank
There are lots of cool charts thrown around the startup/VC world. One of the best is the “Disaggregation of Craiglist,” originally created by Andrew Parker in 2010 and updated by David Haber in 2012 (shown below):
USV has recently invested in a host of internet-enabled financial services, which prompted the question: what would a “Disaggregation of a Bank” look like?
I’ve started putting together a list of startups providing services that would traditionally be offered by a bank (broadly defined). Please contribute if you have any startups or categories that I’m missing!
I took a first stab at visualizing this in the following deck. Past the Craigslist slide, I tried to diagram out the functions of a bank. Most everyone at USV found this a bit confusing, but, on the next slide, it allowed me to show where companies are innovating. I recommend going full screen to take a look (or feel free to download the PDF):
Any thoughts? Ways to improve? My hope is that this leads to larger questions: Which banking functions benefit from economy of scale and remain defensible vs. which areas are ready for disruption? Should startups be focused on consumer, business or both at once?
Best Google Search Ever
This is an anecdote that got me thinking about how the UI/UX of search will continue to evolve.
I recently was in London for Techstars and ended up eating at a small cafe. I had an excellent eggplant/couscous dish (I’m clearly not a chef). A couple weeks later, a few friends were coming over for dinner and I wanted to recreate that dish.
I couldn’t remember the name of the restaurant, anything resembling a street address, or even what the dish would be called.
But I still had a mental image of how I got to the restaurant. So my search was: - Start Google Street View at the St. James Theatre, where Techstars Demo Day was held. - Walk myself through the streets of London using Google Street View towards the restaurant. It’s kind of amazing to explore a city this way. - Found the restaurant! (I was actually disappointed that I couldn’t Street View my way inside). Now I knew the name to search for. - Foursquare brought it up, and I found a link to their menu. Wasn’t the exact recipe, but I was able to make a passable replica knowing the major ingredients.
The point is, we still use search in mostly two capacities: type in a phrase or look at a bird’s eye view map. Yes, Google Street View has been around for a while, but I’d never used it in this way before. But it was perfect for my brain: I don’t remember names, but I have an excellent visual memory. Everyones memory works differently, and I think search will grow to be able to handle this. Someone else might have remembered the decor in the restaurant, or the color of the sign outside. Or the music that was playing. Or the fact that the bathroom was downstairs. I think all these little things, the oddball remnants in our memory, will help us complete searches like this in the future.
Thoughts on Quantified Self
USV has been interested in the quantified self space for over a year now. As investors in “networks enabled by the internet,” we’ve seen the majority of the innovation so far as being outside our wheelhouse: hardware products and apps in specific verticals. And so while we have not made any investments yet, we’ve met with many entrepreneurs and had enough internal discussion to share some of our thoughts.
Quantified self = personal data collection Wearable = device that captures data (except your smartphone) Quantified applications = apps that either collect (with or without a wearable) quantified self data from a user and/or sit on top of the quantified self data from other sources
The form factor of the mobile phone will determine the wearables market
The past two years has seen an explosion in fitness-based quantified self devices such as the Fitbit, UP and Nike Fuelband. This mass adoption is even more remarkable given that these devices all track the same few metrics around activity: steps taken, calories burned, hours and quality of sleep.1
Yet this data can be captured by a mobile phone utilizing the same array of sensors2 found in a wearable device. Moves and Runkeeper are currently leading the pack in app-only products. Battery life and developers’ utilization of that battery life will continue to improve. And Apple’s recent announcement of the M7 chip, a processor dedicated towards making quantified self data available to app developers, shows that mobile phones are making the current class of wearables redundant. Current mainstream devices will have to innovate to remain relevant.
The quantified self device industry must shift towards data that is impossible for a mobile phone to capture. At the bottom of this post is a partial breakdown of the quantified self data that is and will be captured by devices and apps. It’s unlikely that mobile phones will be the form factor to, say, capture brain waves while you sleep, so there is opportunity for wearable products in that specific vertical.
Data tracking will become more medically relevant
While the current mainstream devices are focused on fitness, next-generation quantified self devices will enable medically relevant data. The list of next generation wearables (at the bottom of this post) shows potential for data in bloodwork. And these companies are mostly in the Kickstarter/IndieGoGo phase of development, potentially only a year or two away from being available. Similar advances in mainstream, big-brand wearables won’t be far behind.
Hopefully, this data can be shared with doctors to improve preventative health.3 It’s possible that this will be a lightweight interaction, such as showing your doctor your accumulated physical activity as part of an annual physical. Or, it could become an integral part of the medical ecosystem, our vitals constantly being monitored and our doctors alerted automatically when necessary.
APIs and data aggregation
The biggest winner in this space will be the API-driven platform that creates a stable ecosystem to aggregate and interface between data sources. Developers will have access to larger pools of data. There may also be powerful social networks built on top of this data. Barriers to entry will be particularly high if this platform player figures out how to be HIPAA compliant and become the point of contact between patients and medical professionals, a particularly difficult barrier for fledgling teams. It’s hard to overestimate the value of medically relevant data coming from user engagement in a network lightweight consumer apps.
Signals in the Quantified Self Industry
The original list is here. It includes links to companies and will evolve over time. Feel free to contribute!
Mainstream (i.e. in Fitbit, Fuelband, UP, Moves, Runkeeper, etc.)
- Calories/Distance: tracking how many steps you take and how active you are
- Sleep: tracks amount and quality of sleep, some products also time alarms to your sleep cycle
- “Active” tracking: requires you to manually input data. A suite of apps have been built for fitness regimes and dietary tracking.
Available, but not mainstream
- Heart rate: measuring your pulse
- Withings Activity Tracker is a little clip-on that fulfills that mainstream categories but also has a built in optical pulse tracker. Requires the user to hold their finger up to the device. They also make a blood pressure monitor.
- Azumio has an app for measuring pulse using a phones native camera and flash.
- LUMOBack monitors lower back posture and pelvic alignment. They’ve built a waistband with inertial sensors.
- Asthmapolis has built a bluetooth sensor to monitor the environment, alerting the user of conditions that could trigger an asthma attack
- Spire has a yet-to-launch clip-on device to monitor breathing, showing people their respiratory patterns and helping monitor stress and emotions.
- Tinke tracks and helps with breathing exercises.
- Advanced Fitness
- Nike Hyperdunk is the next generation of in-shoe sensors. Designed specifically to track and analyze your basketball game.
- Active Mind Technology is soon to launch golf specific sensors and analysis after a successful fundraise on AngelList.
- Ovuline uses machine learning to help couples conceive faster. Works with some of the mainstream wearables technology.
- 23andMe is the leader in affordable DNA sequencing and analysis.
- Microbiome: the ecological community of microogranisms that live inside of you
- uBiome provides a $89 kit to “quantify your gut” and compare it to other user data sets. Dozens of health conditions are correlated with the microbiome — from asthma to diabetes, autism to depression, irritable bowel, Crohn’s, chronic sinusitis, heart disease, and more.
- Sano Intelligence is building the “API for the bloodstream,” made possible by their proprietary transdermal blood chemistry sensor patch
- Sproutling is creating a baby monitor for the quantified self age. Will track temperature, heart beat and sleeping position from an ankle band that wirelessly connects to the parents mobile phone.
- Melon is a headband to measure and improve focus. Completed a Kickstarter fundraise for their initial product.
- Muse is another brain sensing headband, available for preorder now. It picks up brainwaves in real-time and sends them to your phone, tracking this data and even turning them into commands!
- Ballistocardiography: measures mechanical forces of the heart, gives data on heartbeat (cardiac contractions), respiration (chest wall movements) and movement
- Beddit is building a sensor that lies on your bed and picks up on these forces while you sleep. Available for preorder.
- In vivo sensors
- Nutritional analysis (without having to manually input everything you eat)
- Mainstream blood glucose monitoring (as is currently the norm for diabetics)
- Integrated into clothing
- And much, much more…
That there is so much consumer interest in a product with so little power is indicative of how exciting the space will become. ↩
An accelerometer, gyroscope and compass are industry standard in smart phones. ↩
My personal realization came when my grandfather was recently admitted to the hospital for the second stroke in two weeks. Doctors were adjusting his blood pressure medication, causing it to fluctuate. But the only data points they get are when it’s too late, once he’s already had the stroke. It’s insane to me that our current medical system is not using a wearable device to monitor his blood pressure in real-time, monitoring the effects of medication outside of the hospital as well. A simple algorithm could easily have provided early warning signs as his condition worsened. ↩
The best ingredients are fresh. So fresh that they’ve just been picked. How do we get to a world in which most of our food can taste this good? There are two obvious possibilities:
- Grow food everywhere. While there are plenty of small scale efforts to grow crops in urban areas, this is inherently unscalable.
- Speed up the supply chain. Most crops are held by wholesale buyers for long periods of time at warehouses, sacrificing quality for market opportunity as prices fluctuate. Even with vast improvements in the system, it’s not physically possible to transport produce from farm to table on the time scale I’m envisioning.
The answer is a combination of the two, the product of thinking so far out of the box that you end up in one: grow food as it moves through the supply chain.
Freight Farms has figured out how to grow crops inside a shipping container in any external environment. Each 40 foot by 8 foot container is packed with produce growing under energy-efficient LEDs regardless of whether the container is held in a lot, is traveling on a flatbed, or has arrived at its ultimate destination. And while their focus is not on consumer yet, it’s possible to imagine a futuristic Whole Foods made up of a series of these containers. Need carrots? Walk into the appropriate container and pick a few out of the hydroponically nourished soil. Suburban homes could have a container in their backyard. Freight Farms could even periodically airdrop containers onto the roofs of urban apartment buildings.1
I’ve spent the past month doing a deep dive into the agricultural supply chain. There are a couple of talented teams creating online marketplaces to disrupt the middleman-heavy status quo. And while Freight Farms is outside of this model, and not a USV company, it has been one of the most fun to think through the implications of the world that they’re envisioning.
The future is full of awesome helicopters/drones making stuff like this a reality. And most urban rooftops are totally wasted space anyway. ↩
Connecting on LinkedIn is pointless gamification
LinkedIn is an network of interconnected professional personas. I’ve already argued that the current connection model makes for a useless professional address book. This post is about why connections themselves are an arbitrary hindrance on the value (to consumers!) of the LinkedIn graph.
Part of the idea behind being on LinkedIn is the possibility that someone interesting will see my profile and reach out to me. It might be someone I met briefly at a conference, someone who just Googled my name, or, hopefully, a recruiter with my potential dream job that I didn’t even know existed.
And my profile is exactly “who” I want them to find: my resume, education, skills, accomplishment and a picture of me wearing a tie.1 This is my professional persona. So the question is: who do I NOT want to be able to access this information?
The answer is no one. The expectation value of benefit to me increases the more open to the world that profile is.2 Yet for anyone to see my profile, they have to be a “second-degree” connection to me. For me, and most other people I’ve talked to, that interaction only occurs with people I’ve met in real life.
On the flip side, there are plenty of people’s resumes I’d be interested in seeing, but who I either am not personally acquainted with or don’t want to bother with a pointless LinkedIn connection. For example, I’d love to see Reid Hoffman’s career track, to understand how he got to where he is today, without bugging him about it.
Why, then, the connection model? It’s a classic case of a network constraining information to earn money. There are no connections on the backend: so long as you pay LinkedIn enough money you get free rein to troll your way through the entire platform. This is only feasible as a business expense, of course, so just recruiters and companies get to see the whole lay-of-the-talent-land.
Constraining information that does not want to be constrained is an inherently unsustainable business model for an internet company.3 The ease of creating a LinkedIn profile used to be a major technical advantage. But it’s increasingly easy to put up your resume online. There are alternative services specializing in specific verticals. Most members of the tech community just throw up their own website. And Google works just as well, possibly even better, than LinkedIn’s internal search.
As an aside, a byproduct of the connection model is a brilliant reinforcement of LinkedIn’s network effects. Connecting keeps us coming back to LinkedIn, remind us of it’s value and reinforcing the idea that LinkedIn is “the” platform for our professional personas. All while providing us with only a tiny fraction of its value of the platform. So the next time you make a connection on LinkedIn, remember why you even had to do it in the first place.
Citi Bike’s Failure of Information
Citi Bike in NYC is great, when it works. Plenty has been written about the programs engineering struggles. But even more astounding, and ultimately more detrimental to the program, is the shoddy quality of information in the program.
Above is a picture from my phone of the Citi Bike app on Saturday night. I had ridden a Citi Bike back to the East Village from Midtown. All of the Citi Bike docks shown are supposed to have many free spots, represented by being only “partially full” in dark blue. All of the ones I’ve circled in red were, in fact, completely full. It ended up taking me longer to ride around searching for a spot in vain than it would have taken for me to have just walked.
Besides being extremely frustrating for a consumer, it’s much more dangerous to Citi Bike itself. For example, the program is spending significant money and effort trying to dynamically balance the allocation of bikes. How can they do this when all their data is plain wrong?
An open letter to a new VC analyst
The following are my thoughts on sourcing deals after having worked at USV for (gasp!) just over a year now. I will focus on run my own process, separate from the day-to-day collaboration with other members of the investment team. While every firm is different, there is hopefully something in here that will be useful. The underlying lesson is to try and identify the weak spots in your firm’s process and then fill them.
There are two types of sourcing: reactive and proactive. Reactive sourcing is fairly self-evident: troll AngelList, TechCrunch, Hacker News, VCDelta, and any other source that fits your sector; go to events, such as demo days, to meet promising companies; take as many (reasonable sounding) pitches as walk in the door; and, of course, VC is a networking business, so meet as many people as possible.1 Most of the value here is adding bandwidth to your firm.
My lesson from this approach is that building companies takes longer than expected. As an analyst, I started off talking to companies that were not mature enough for an investment from us. While these are all teams that would otherwise not have been in contact with someone else on our investment team, it has taken much longer than I expected to be able to bring these companies to the partnership. And, unlike a partner, I will have a much shorter life at the firm, so I unfortunately can’t be as patient.
Proactive sourcing is much more interesting. These are initiatives you can come up with, an intellectual stance for why should something should exist (and it might not yet!). Come up with an idea or a sector and dive in. If that thesis resonates with the firm, go deeper until you find a company that fits. At least at USV, the partners talk about their best deals coming out of such work. As an example, I’ve been fairly deep into Quantified Self for the past month.
Also try to identify a signal that is currently being missed by your firm. After testing out various analytics projects, I couldn’t find it became clear that tracking pageviews, app downloads, etc. at our stage of investing was not meaningful enough. But since Series A is typically the second round of publicly disclosed funding, we’re in the perfect position to use prior VC investment as a signal for good companies. And on top of this I can layer analytics. For example, given the size of the team and the date and amount raised previously it’s quite easy to estimate when they’ll be raising their next round. Once I get a handle on this signal, I should be able to at least get a quick look at every company that otherwise wouldn’t walk in the door.
My 10% project for the past few months has been to build a Django app that integrates with CrunchBase, AngelList and VCDelta. The code for the entire project will remain private, but I’ll start sharing snippets to help others build their own tools. The first is an update of a Python library for the CrunchBase API.
I highly recommend breakfast meetings every day. Let’s you work through lunch and stay focused. ↩