Is This Startup Ready For Investment?

Since 2005 startup accelerators have provided cohorts of startups with mentoring, pitch practice and product focus. However, accelerator Demo Days are a combination of graduation ceremony and pitch contest, with the uncomfortable feel of a swimsuit competition. Other than “I’ll know it when I see it”, there’s no formal way for an investor attending Demo Day to assess project maturity or quantify risks. Other than measuring engineering progress, there’s no standard language to communicate progress.

Corporations running internal incubators face many of the same selection issues as startup investors, plus they must grapple with the issues of integrating new ideas into existing P&L-driven functions or business units.

What’s been missing for everyone is:

  • a common language for investors to communicate objectives to startups
  • a language corporate innovation groups can use to communicate to business units and finance
  • data that investors, accelerators and incubators can use to inform selection

While it doesn’t eliminate great investor judgment, pattern recognition skills and mentoring, we’ve developed an Investment Readiness Level tool that fills in these missing pieces.

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Investment Readiness Level (IRL) for Corporations and Investors
The startups in our Lean LaunchPad classes and the NSF I-Corps incubator use LaunchPad Central to collect a continuous stream of data across all the teams. Over 10 weeks each team gets out of the building talking to 100 customers to test their hypotheses across all 9 boxes in the business model canvas.

We track each team’s progress as they test their business model hypotheses. We collect the complete narrative of what they discovered talking to customers as well as aggregate interviews, hypotheses to test, invalidated hypotheses and mentor and instructor engagements. This data gives innovation managers and investors a feel for the evidence and trajectory of the cohort as a whole and a top-level view of each teams progress. The software rolls all the data into an Investment Readiness Level score.

(Take a quick read of the post on the Investment Readiness Level – it’s short. Or watch the video here.)

The Power of the Investment Readiness Level: Different Metrics for Different Industry Segments
Recently we ran a Lean LaunchPad for Life Sciences class with 26 teams of clinicians and researchers at UCSF.  The teams developed businesses in 4 different areas– therapeutics, diagnostics, medical devices and digital health.  To understand the power of this tool, look at how the VC overseeing each market segment modified the Investment Readiness Level so that it reflected metrics relevant to their particular industry.

Medical Devices
Allan May of Life Science Angels modified the standard Investment Readiness Level to include metrics that were specific for medical device startups. These included; identification of a compelling clinical need, large enough market, intellectual property, regulatory issues, and reimbursement, and whether there was a plausible exit.

In the pictures below, note that all the thermometers are visual proxies for the more detailed evaluation criteria that lie behind them.

Device IRL

Investment Readiness Level for Medical Devices

You can watch the entire presentation here

Therapeutics
Karl Handelsman of CMEA Capital modified the standard Investment Readiness Level (IRL) for teams developing therapeutics to include identifying clinical problems, and agreeing on a timeline to pre-clinical and clinical data, cost and value of data points, what quality data to deliver to a company, and building a Key Opinion Leader (KOL) network. The heart of the therapeutics IRL also required “Proof of relevance” – was there a path to revenues fully articulated, an operational plan defined. Finally, did the team understand the key therapeutic liabilities, have data proving on-target activity and evidence of a therapeutic effect.

Therapeutics IRL

You can see the entire presentation here

Digital Health
For teams developing Digital Health solutions, Abhas Gupta of MDV noted that the Investment Readiness Level was closest to the standard web/mobile/cloud model with the addition of reimbursement and technical validation.

Digital Health

Diagnostics
Todd Morrill wanted teams developing Diagnostics to have a reimbursement strategy fully documented, the necessary IP in place, regulation and technical validation (clinical trial) regime understood and described and the cost structure and financing needs well documented.

Diagnostics IRL

You can see the entire presentation here

For their final presentations, each team explained how they tested and validated their business model (value proposition, customer segment, channel, customer relationships, revenue, costs, activities, resources and partners.) But they also scored themselves using the Investment Readiness Level criteria for their  market. After the teams reported the results of their self-evaluation, the  VC’s then told them how they actually scored.  We were fascinated to see that the team scores and the VC scores were almost the same.

Lessons Learned

  • The Investment Readiness Level provides a “how are we doing” set of metrics
  • It also creates a common language and metrics that investors, corporate innovation groups and entrepreneurs can share
  • It’s flexible enough to be modified for industry-specific business models
  • It’s part of a much larger suite of tools for those who manage corporate innovation, accelerators and incubators

Listen to the blog post here


Download the podcast here

Lessons Learned in Diagnostics

This post is part of our series on the National Science Foundation I-Corps Lean LaunchPad class in Life Science and Health Care at UCSF. Doctors, researchers and Principal Investigators in this class got out of the lab and hospital talked to 2,355 customers, tested 947 hypotheses and invalidated 423 of them. The class had 1,145 engagements with instructors and mentors. (We kept track of all this data by instrumenting the teams with LaunchPad Central software.)

Mira Medicine is one of the 26 teams in the class. The team members are:
  • Pierre-Antoine Gourraud – PhD, MPH UCSF neuroscientist and c0-leader of the MS BioScreen project
  • Jason Crane - PhD UCSF Manager Scientific Software Development
  • Raphaelle Loren - Managing Director – Health Practice at the Innovation Management Institute

Todd Morrill was the diagnostics cohort instructor. Matt Cooper CEO of Carmenta BioSciences was the Mira Medicine team mentor.

Multiple Sclerosis - MS
Multiple Sclerosis – MS – is an immune system disease that attacks the myelin, the fatty sheath that surrounds and protects nerve fibers of the central nervous system (brain, spinal cord, and optic nerve). T-cells, (a type of white blood cell in the immune system,) become sensitized to myelin and cross the blood-brain barrier into the central nervous system (CNS). Once in the CNS, these T-cells injure myelin, and secrete chemicals that damage nerve fibers (axons) and recruit more damaging immune cells to the site of inflammation. multiple sclerosis and therapeutic targets

There are currently ten FDA approved MS medications for use in relapsing forms of MS. None of these drugs is a cure, and no drug is approved to treat the type of MS that shows steady progression at onset. MS disease management decisions are complex and requires a patients neurologist to figure out what drugs to use.

Mira Medicine and Multiple Sclerosis
The team came to class with the thought of commercializing the UCSF Multiple Sclerosis BioScreen Project a Precision Medicine application that integrates a patients medical records with the latest population-based data from hundreds of other Multiple Sclerosis patients, (including their 3D MRI scans,) and using predictive algorithms makes it possible to chart a unique course of treatment for each patient. (Mira Medicine team member Pierre-Antoine Gourraud was the project co-leader.) MS BioScreen

Mira wanted to commercialize the UCSF Multiple Sclerosis Bioscreen project and to add additional neurological diseases which require multiple types of data  (including biomarkers, clinical, and imaging). They wanted to help medical centers and large providers assess disease progression to guide therapeutic decision-making.  Over the course of the class Mira Medicine team spoke to over 80 customers, partners and payers.

Here’s their 2 minute video summary

If you can’t see the video above, click here.

Then reality hit. First, the team found that their Multiple Sclerosis Bioscreen application (which they used as their MVP) was just a “nice-to-have”, not a “must-have”. In fact, the “must have features” were their future predictive algorithms. Next, they found that if their tool can enable a diagnosis, (even without claiming it could) then it was likely that the FDA would require a  510(k) medical device clearance. Then they found to get reimbursed they need a CPT code (and they had to decide whether to code stack - using multiple codes for “one” diagnosis, and thereby getting multiple reimbursements for one test. (The rules have changed so that code stacking is hard or impossible), Or get a new CPT code, or use miscellaneous code.) To get a new CPT and a 510(k) they would have to perform a some sort of clinical study. At a minimum a 1-year prospective study (a study to see if the neurologists using the application had patients with a better outcome then those who didn’t have access to the app). Getting approval to use an existing (aka old) CPT code means showing equivalence to an existing dx process or test, and the requirements are code-specific. Finally, to get access to data sources of other MS patients they would need to have HIPPA Business Associate Agreement.

Watch their Lessons Learned video below and find out how they pivoted and what happened.

If you can’t see the video above click here

Look at their Lesson Learned slides below If you can’t see the presentation above, click here

Lessons Learned

  • Researchers and PI’s come in believing “My science/project/data are so good that people will immediately see the value and be willing to pay for it.  It will “sell itself”.
  • A business is much more than just good science: it is about customers seeing value and being willing to pay and proper validation and reimbursement coding and
  • A successful business is the sum (and integration!) of all the parts of the business model canvas.
    • It includes reimbursement, regulation, IP, validation, channel access, etc.

Listen to the blog post here


Download the podcast here

Lessons Learned in Therapeutics

This post is part of our series on the National Science Foundation I-Corps Lean LaunchPad class in Life Science and Health Care at UCSF. Doctors, researchers and Principal Investigators in this class got out of the lab and hospital talked to 2,355 customers, tested 947 hypotheses and invalidated 423 of them. The class had 1,145 engagements with instructors and mentors. (We kept track of all this data by instrumenting the teams with LaunchPad Central software.)

We are redefining how translational medicine is practiced.

Traditional view of translational medicineWe’ve learned that translational medicine is not just about the science.

More on this in future blog posts.

Lean view of translational medicine

Vitruvian Therapeutics is one of the 26 teams in the class. The team members are:
  • Dr. Hobart Harris  Chief of  General Surgery, Vice-Chair of the Department of Surgery, and a Professor of Surgery at  UCSF.
  • Dr. David Young,  Professor of Plastic Surgery at UCSF. His area of expertise includes wound healing, microsurgery, and reconstruction after burns and trauma. 
  • Cindy Chang is a Enzymologist investigating novel enzymes involved in biofuel and chemical synthesis in microbes at LS9

Karl Handelsman was the therapeutics cohort instructor. Julie Cherrington CEO of Pathway Therapeutics was the team mentor.

Vitruvian Therapeutics is trying to solve the Incisional hernia problem. An incisional hernia happens in open abdominal surgery when the area of the wound doesnt heal properly and bulges outward. This requires a second operation to fix the hernia.Ventral Herniaincisional hernia

Hobart Harris’s insight was what was needed wasn’t one more new surgical technique or device to repair the hernias, but something to prevent the hernia from occurring in the first place. Vitruvian Therapeutics first product, MyoSeal, does just that. It promotes wound repair via biocompatible microparticles plus a fibrin tissue sealant. So far in 300 rats it’s been shown to prevent incisional hernias through enhanced wound healing.

Here’s their 2 minute video summary

If you can’t see the video above, click here.

Two weeks into the class and interviews with 14 of their potential customers (surgeons) reality intruded on their vision of how the world should work. We happened to catch that moment in class in this 90 second clip.

Watch  and find out how talking to just the first 14 customers in the Lean LaunchPad class saved Hobart Harris and the Vitruvian Therapeutics team years.

If you can’t see the clip above click here.

The Vitruvian Therapeutics Lessons Learned Presentation is a real-eyeopener. Given that this product could solve the incisional hernia problem, Hobart and his team naturally assumed that insurance companies would embrace this and their fellow surgeons viewed the problem as they did and would leap at using the product. Boy were they in for a surprise. After talking to 74 surgeons, insurance companies and partners appeared that no one – insurance companies or surgeons – owned the problem. Listen to their conclusions 8-weeks after the first video.

Watch the video and find out how they pivoted and what happened.

Don’t miss Karl Handelsman comments on their Investment Readiness Level at the end. Vitruvian is a good example of a great early stage therapeutics idea with animal data missing and many key components of the business model still needed to verify.

If you can’t see the video above click here

Look at their Lesson Learned slides below

If you can’t see the presentation above, click here

Market Type
During the class the Vitruvian Therapeutics class struggled with the classic question of visionaries: are we creating a New Market (one which doesn’t exist and has no customers)? In Vitruvian’s case preventive measures to stop incisional hernias before they happen.  Or should we position our product as one that’s Resegmenting an Existing Market? i.e. reducing leakage rates.  Or is there a way to get proof that the vision of the New Market is the correct path.

When Hobart Harris of Viturvian asked, “… what if you’re a visionary, and no one but you sees the right solution to a problem” we had a great in-class dialog. Karl Handelsman‘s comments at 3:15 and 4:16 and Allan May at 4:35 were incredibly valuable. See the video below for the dialog.

If you can’t see the video above, click here

Further Reading

Lessons Learned

  • Principal Investigators, scientists and engineers can’t figure out commercialization sitting in their labs
  • You can’t outsource commercialization to a proxy (consultants, market researchers, etc.)
  • Experiential Learning is integral to commercialization
  • You may be the smartest person in your lab, but your are not smarter than the collective intelligence of your potential customers, partners, payers and regulators

Listen to the blog post here


Download the podcast here

Lessons Learned in Medical Devices

This post is part of our series on the National Science Foundation I-Corps Lean LaunchPad class in Life Science and Health Care at UCSF. Doctors, researchers and Principal Investigators in this class got out of the lab and hospital talked to 2,355 customers, tested 947 hypotheses and invalidated 423 of them.  The class had 1,145 engagements with instructors and mentors. (We kept track of all this data by instrumenting the teams with LaunchPad Central software.)

We are redefining how translational medicine is practiced. It’s Lean, it’s fast, it works and it’s unlike anything else ever done.

—–

Sometimes teams win when they fail.

Knox Medical Devices was building a Spacer which contained a remote monitoring device to allow for intervention for children with Asthma . (A Spacer is a tube between a container of Asthma medicine (in an inhaler) and a patient’s mouth.The tube turns the Asthma medicine into an aerosol.)Asthma

Knox’s spacer had sensors for basic spirometry measurements (the amount of air and how fast it’s inhaled and exhaled) to see how well the lung is working. It also had a Nitrous Oxide sensor to provide data on whether the lungs airways are inflamed, an inhaler attachment and a GPS tracking device.

Knox SpacerThe Spacer hardware was paired with data analysis software for tracking multiple facets of asthma patients.

The Knox team members are:

Allan May founder of Life Science Angels was the Medical Device cohort instructor. Alex DiNello CEO at Relievant Medsystems was their mentor.

The Knox team was a great mix of hands-on device engineers and business development. They used agile engineering perfectly to continually test variants of their Minimum Viable Product (MVP’s) in front of customers often and early to get immediate feedback.

Knox was relentless about understanding whether their device was a business or whether it was technology in search of a market. In 10 weeks they had face-to-face meetings with 117 customers, tested 33 hypotheses, invalidated 19 of them and 53 instructor and mentor interactions.

Here’s Knox Medical’s 2 minute video summary

If you can’t see the video above, click here

Knox was a great example of having a technology in search of a customer. The initial hypothesis of who would pay for the device – parents of children with asthma – was wrong and resulted in Knox’s first pivot in week 4. By week 6 they had discovered that; 1) Peak Flow Meters are not as heavily prescribed as they thought, 2) Insurance company reimbursement is necessary for anything upwards of $15, 3) Nitrous Oxide testing isn’t currently used to measure asthma conditions.

After the pivot they the found that the most likely users of their device would be low income Asthma patients who are treated at Asthma clinics funded by federal, state or county dollars. These clinics reduce hospitalization but Insurers weren’t paying to cover clinic costs nor would they cover the use of the Knox device. The irony was that those who most needed the Knox device were those who could least afford it and wouldn’t be able get it.

Watch their Lesson Learned presentation below. Listen to the comments from Allan May the Device instructor at the end.

If you can’t see the video above, click here

In the end Knox, like a lot of startups in Life Science and Health Care, discovered that they had a multi-sided market.  They realized late in the class the patients (and their families) were not their payers - their payers were the insurance companies (and the patients were the users.)  If they didn’t have a compelling value proposition for the insurers (cost savings, increased revenue, etc.) it didn’t matter how great the technology was or how much the patients would benefit.

The Knox Medical Device presentation slides are below. Don’t miss the evolution of their business model canvas in the appendix. It’s a film strip of the entrepreneurial process in action.

If you can’t see the slides above, click here

Knox is a great example of how the Lean LaunchPad allows teams to continually test hypotheses and fail fast and inexpensively. They learned a ton. And saved millions.

Lessons Learned

  • In medical devices, understanding reimbursement, regulation and IP is critical
  • Sometimes teams win when they fail
Listen to the blog post here


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Lessons Learned in Digital Health

This post is part of our series on the National Science Foundation I-Corps Lean LaunchPad class in Life Science and Health Care at UCSF.

Our Lean LaunchPad for Life Science class talked to 2,355 customers, tested 947 hypotheses and invalidated 423 of them.  They had 1,145 engagements with instructors and mentors. (We kept track of all this data by instrumenting the teams with LaunchPad Central software.)

This post is one of a series of the “Lessons Learned” presentations and videos from our class.

Sometimes a startup results from a technical innovation. Or from a change in regulation, declining costs, changes in consumers needs or an insight about customer needs. Resultcare, one of the 26 teams in the class started when a resident in clinical medicine at UCSF watched her mother die of breast cancer and her husband get critically injured.

The team members are:

  • Dr. Mima Geere  Clinical Medicine at UCSF.
  • Dr. Arman Jahangiri HHMI medical fellow at UCSF, Department of Neurological Surgery
  • Dr. Brandi Castro in Neuroscience at UCSF
  • Mitchell Geere product design
  • Kristen Bova MBA, MHS
  • Nima Anari PhD in Data Science

Abhas Gupta was the Digital Health cohort instructor. Richard Caro was their mentor.

ResultCare is a mobile app that helps physicians take the guesswork out of medicine. It enables physicians to practice precision medicine while reducing costs.precision medicine

Here’s Resultcare’s 2 minute video summary

If you can’t see the video above, click here.

Watch their Lesson Learned presentation below. The first few minutes of the talk is quite personal and describes the experiences that motivated Dr. Geere to address this problem.

If you can’t see the video above, click here

The Resultcare presentation slides are below.

If you can’t see the presentation above, click here

Listen to the blog post here


Download the podcast here

We’ve seen the Future of Translational Medicine and it’s Disruptive

A team of 110 researchers and clinicians, in therapeutics, diagnostics, devices and digital health in 25 teams at UCSF, has just shown us the future of translational medicine.  It’s Lean, it’s fast, it works and it’s unlike anything else ever done.

It’s going to get research from the lab to the bedside cheaper and faster.

Welcome to the Lean LaunchPad for Life Sciences and Healthcare (part of the National Science Foundation I-Corps).

This post is part of our series on the Lean Startup in Life Science and Health Care.

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Our class talked to 2,355 customers, tested 947 hypotheses and invalidated 423 of them.  They had 1,145 engagements with instructors and mentors. (We kept track of all this data by instrumenting the teams with LaunchPad Central software.)

In a packed auditorium in Genentech Hall at UCSF, the teams summarized what they learned after 10 weeks of getting out of the building. This was our version of Demo Day – we call it “Lessons Learned” Day. Each team make two presentations:

  • 2 minutes YouTube Video: General story of what they learned from the class
  • 8 minute Lessons Learned Presentation: Very specific story about what they learned in 10 weeks about their business model

In the next few posts I’m going to share a few of the final “Lessons Learned” presentations and videos and then summarize lessons learned from the teaching team.

Magnamosis
Magnamosis is a medical device company that has a new way to create a magnetic compression anastomosis (a surgical connection between two tubular structures like the bowel) with improved outcomes.

anastomosis

Team Members were: Michael Harrison (the father of fetal surgery), Michael Danty, Dillon Kwiat, Elisabeth Leeflang, Matt Clark.  Jay Watkins was the team mentor. Allan May and George Taylor were the medical device cohort instructors.

Their initial idea was that making an anastomosis that’s better, faster and cheaper will have surgeons fighting to the death to get a hold of their device.  magnamosisThey quickly found out that wasn’t the case.  Leak rates turned out to a bigger issue with surgeons and a much larger market.

Here’s their 2 minute video summary

If you can’t see the video above, click here.

Watch their Lessons Learned video below and see how a team of doctors learned about product/market fit, channels and pricing.

If you can’t see the video above, click here

Their slide deck is below. Don’t miss the evolution of their business model in the Appendix.

If you can’t see the presentation above, click here

The best summary of why Scientists, Engineers and Principal Investigators need to get out of the building was summarized by Dr. Harrison below. After working on his product for a decade listen to how 10 weeks of the Lean LaunchPad class radically changed his value proposition and business model.

If you can’t see the video above, click here.

For further reading:

Listen to the blog post here


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How Do You Want to Spend Your Next 4 Years of Your Life?

As our Lean LaunchPad for Life Sciences class winds down, a good number of the 26 teams are trying to figure out whether they should go forward to turn their class project into a business.

Given that we’ve been emphasizing Evidence-based entrepreneurship and the Investment Readiness Level, I guess I shouldn’t have been surprised when someone asked, “After we figure all this data out, should we pursue our idea based on the numbers?”

Ouch.

I pointed out that the “data” you gather in 10 weeks (talking to 100+ customers, partners, payers, etc.,) are not the first thing you should look at. There are three more important things you should worry about.

(see 0:30 in the video below)

turning point

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1. Do you want to spend the next 3 or 4 years of your life doing this?

(See 1:03 in the video below)

Now that you’ve gotten to know your potential channel and customers, regardless of how much money you’re going to make, will you enjoy working with these customers for the next 3 or 4 years?

One of the largest mistakes in my career was getting this wrong. I used to be in startups where I was dealing with engineers designing our microprocessors or selling supercomputers to research scientists solving really interesting technical problems. But in my next to last company, I got into the video game business.

My customers were 14-year old boys. (see 1:30 in the video)  I hated them. It was a lifelong lesson that taught me to never start a business where you hate your customers. It never goes well. You don’t want to talk to them. You don’t want to do Customer Development with them. You just want them to go away.  And in my case they did – they didn’t buy anything.

So you and your team need to feel comfortable being in this business with these customers.

2. Is this a scalable business?  And if not, are you Ok with something small?

(See 2:03 in the video below)

Is it a lifestyle business while you’re keeping your other job?  Is it a small business that hits $4 million in revenue in four years and $8 million in ten years?  Or is it something that can grow to a size that will result in an acquisition or some liquidity event?

You need to decide what your personal goal is and how it matches what you think this business can grow into.  And you and your cofounders need to have that discussion to make sure that all the co-founders’ interests are aligned – before you make any decision to start the company.  If one of you are happy making $500K/year and the other has visions of selling the company to Roche for a billion dollars, you have very different goals. Without clear alignment, one or both of you will be really unhappy later when you try to make decisions.

3. If I Didn’t Make Any Money After 4 Years, Did I Still Have A Great Time?

(See 4:36 in the video below)

If your company fails, would you still say you had one hell of a ride? Founders don’t do startups because they’re searching for a huge financial windfall. They do it because it’s the greatest invention they can imagine. Most of the time you will fail. So if you’re not going to have a great time with your team and learn and build something you are truly excited about – don’t do it.

If you can’t see the video above, click here

Lessons Learned

  • Do you want to spend the next 3 or 4 years of your life doing this business?
  • Is this a scalable business?  And if not, are you Ok with something small?
  • If you didn’t make any money after 4 years, did you have a great time?

Listen to the podcast here


Download the podcast here

When Customers Make You Smarter

We talk a lot about Customer Development, but there’s nothing like seeing it in action to understand its power. Here’s what happened when an extraordinary Digital Health team gained several critical insights about their business model. The first was reducing what they thought was a five-sided market to a simpler two-sided one.

But the big payoff came when their discussions with medical device customers revealed an entirely new way to think about pricing —potentially tripling their revenue.

——

We’re into week 9 of teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) at UCSF teaching with a team of veteran venture capitalists. The class has talked to ~2,200 customers to date. (Our final – not to be missed – Lessons Learned presentations are coming up December 10th.)

Among the 28 startups in the Digital Health cohort is Tidepool. They began the class believing they were selling an open data and software platform for people with Type 1 Diabetes into a multi-sided market comprised of patients, providers, device makers, app builders and researchers.

tidepool website

The Tidepool team members are:

  • Aaron Neinstein MD  Assistant Professor of Clinical Medicine, Endocrinology and Assistant Director of Informatics at UCSF. He’s an expert in the intersection between technological innovations and system improvement in healthcare. His goal is to make health information easier to access and understand.
  • Howard Look, CEO of Tidepool, was VP of Software and User Experience at TiVo. He was also VP of Software at Pixar, developing Pixar’s film-making system, and at Amazon where he ran a cloud services project. At Linden Lab, delivered the open-sourced Second Life Viewer 2.0 project. His teenage daughter has Type 1 diabetes.
  • Brandon Arbiter was a VP at FreshDirect where he built the company’s data management and analytics practices. He was diagnosed at age 27 with Type 1 Diabetes. He developed a new generation diabetes app, “nutshell,” that gives patients the information they need to make the right decisions about their dosing strategies.
  • Kent Quirk was director of engineering at Playdom and director of engineering at Linden Labs.

A Five-sided Market
In Week 1 the Tidepool team diagramed its customer segment relationships like this:

Tidepool ecosystem

Using the business model canvas they started with their value proposition hypotheses, articulating the products and services they offered for each of the five customer segments. Then they summarized what they thought would be the gain creators and pain relievers for each of these segments.

Tide pool value prop week 1

Next, they then did the same for the Customer Segment portion of the canvas. They listed the Customer Jobs to be done and the Pains and Gains they believed their Value Proposition would solve for each of their five customer segments.

Tide pool cust week 1

It’s Much Simpler
Having a multisided market with five segments is a pretty complicated business model. In some industries such as medical devices its just a fact of life. But after talking to dozens of customers by week 3, Tidepool discovered that in fact they had a much simpler business model – it was a two-sided market.

tidepool simplification

They discovered that the only thing that mattered in the first year or two of their business was building the patient-device maker relationship. Everything else was secondary. This dramatically simplified their value proposition and customer segment canvas.

So they came up with a New Week 3 Value Proposition Canvas:

Tide pool value prop week 3

And that simplified their New Week 3 Customer Segment Canvas

Tide pool cust week 3

Cost-based Pricing versus Value-based Pricing
While simplifying their customer segments was a pretty big payoff for 3 weeks into the class, the best was yet to come.

As part of the revenue streams portion of the business model canvas, each team has to diagram the payment flows.

Tide pool market pricing

The Tidepool team originally believed they were going charge their device partners “market prices” for access to their platform. They estimated their Average Revenue per User (ARPU) would be about $36 per year.

Tide pool market pricing ARPU

But by week 6 they had spoken to over 70 patients and device makers. And what they found raised their average revenue per user from $36 to $90.

When talking to device makers they learned how the device makers get, keep and grow their customers.  And they discovered that:

  • device makers were spending $500-$800 in Customer Acquisition Cost (CAC) to acquire a customer
  • device makers own customers would stay their customers for 10 years (i.e. the Customer Life Time (CLT))
  • and the Life Time Value (LTV) of one customer over those 10 years to a device maker is $10,000

Tide pool market pricing device cac

These customer conversations led the Tidepool team to further refine their understanding of the device makers’ economics.  They found out that the device makers sales and marketing teams were both spending money to acquire customers.  ($500 per sales rep per device + $800 marketing discounts offered to competitors’ customers.)

Tide pool device economics

Once they understood their device customers’ economics, they realized they could help these device companies reduce their marketing spend by moving some of those dollars to Tidepool. And they realized that the use of the Tidepool software could reduce the device companies’ customer churn rate by at least 1%.

This meant that Tidepool could price their product based on the $1,800 they were going to save their medical device customers.  Read the previous sentence again. This is a really big idea.

Tide pool value pricing big idea

The Tidepool team went from cost-based pricing to value-based pricing. Raising their average revenue per user from $36 to $90.

Tide pool value pricing $90 ARPU

There is no possible way that any team, regardless of how smart they are could figure this out from inside their building.

If you want to understand how Customer Discovery works and what it can do in the hands of a smart team, watch the video below. The team ruthlessly dissects their learning and builds value-pricing from what they learned.

This short video is a classic in Customer Discovery.

If you can’t see the video click here.

Lessons Learned

  • Most startups begin by pricing their product based on cost or competition
  • Smart startups price their product based on value to the customer
  • You can’t guess how your product is valued by customers
  • Customer Development allows you to discover the economics needed for value pricing your product

Listen to the podcast here


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Lean LaunchPad for Life Sciences – Revenue Streams

We’re teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) at UCSF with a team of veteran venture capitalists. The class has talked to 2,056 customers to date.

This post is an update of what we learned about life science revenue models.

Life Science/Health Care revenue streams differ by Category
For commercialization, the business model (Customers, Channel, Revenue Model, etc.) for therapeutics, diagnostics, devices, bioinformatics and digital health have very little in common.

This weeks topic was revenue streams – how much cash the company can generate from each customer segment. Revenue streams have two parts: the revenue strategy and the pricing tactics.

Figuring out revenue strategy starts by gaining a deep understanding of the target customer(s). Setting a revenue strategy starts with understanding the basics about the customer segments:

  • who’s the user, the recommender, buyer, and payer
  • How the target customer currently purchases goods and services and how much they currently pay for equivalent products
  • Their willingness to pay for value versus lowest cost?
  • How much budget they have for your type of product?

Revenue strategy asks questions like, “Should we offer cost-based or value-based pricing.  How about demand-based pricing? Freemium? Do we price based on hardware sales or do we offer hardware plus consumables (parts that need to be disposed or replaced regularly)? Do we sell a single software package or a subscription?  These strategy hypotheses are tested against the target customer segment(s).

Once you’ve established a revenue strategy the pricing tactics follow. Pricing is simply “how much can I charge for the product using the selected revenue strategy?”  Pricing may be as simple as setting a dollar value for hardware or software, or as complicated as setting a high price and skimming the market or setting a low price as a loss leader.

You can get a feel for how each of the cohorts address the Revenue Streams by looking at the Revenue lectures below – covering the therapeutics, diagnostics, devices and digital health cohorts.

At the end of the lectures you can see a “compare and contrast” video and a summary of the differences in distribution channels.

Diagnostics

Week 5 Todd Morrill Instructor 

If you can’t see the presentation above click here

Digital Health

Week 5 Abhas Gupta Instructor 

If you can’t see the presentation above click here

Devices

Week 5 Allan May Instructor 

If you can’t see the presentation above click here

Therapeutics

Week 5 Karl Handelsman Instructor 

If you can’t see the presentation above click here

Life Science and Health Care Differences in Revenue Streams
”
This weeks lecture and panel was on Revenue; how much cash the company can generate from each customer segment – and the strategy and tactics to do so. Therapeutics, diagnostics, devices and digital health use different Revenue Strategies and Pricing Tactics, in the video and the summary that follows the instructors compare and contrast how they differ.

If you can’t see the video above click here

Therapeutics (Starting at 0:30)

  • Therapeutics revenue is from drug companies not end users
  • 18 months to first revenue from a deal
  • Predicated on delivering quality data to a company
  • Deal can be front-end or back-end loaded
  • Quality of the data has to be extremely high for a deal

Diagnostics (Starting at 4:10)

  • Diagnostic revenue is from end users: a hospital or clinical lab
  • You need to figure out value of your product but…
  • Pricing is capped by your reimbursement (CPT) code limits
  • Reimbursement strategy is paramount, design to good codes avoid bad ones
  • Find a reimbursement code consultant
  • Don’t do cost-based pricing… go for value-based pricing

Medical Devices (Starting at 8:23)

  • There really is no such thing as a perfect First Generation Medical Device
    • So Medical Device companies often start with a Volkswagen product and then build to the Ferrari product
  • Revenue models are typically direct product sales
  • Don’t do cost-based pricing… go for value-based pricing, especially where your device lowers the treatment costs of the patient
  • In most cases, pricing is capped by your reimbursement (CPT) code limits
    • Or pricing can be capped by what competitors offer, unless you can demonstrate superior cost savings
    • In a new market there is no reimbursement code but if you show high cost-savings you can get a high reimbursement rate
  • A risk in device hardware is getting trapped in low-volume manufacturing with low margins and run out of cash

Digital Health (Starting at 10:35)

  • Digital Health revenue models are often subscription models to a company per month across a large number of users
    • Intermediation fees – where you broker a transaction – are another source of revenue (i.e. HealthTap)
    • Advertising is another digital health revenue model, but requires at least 10 million users to have a meaningful model, but can be lower if you have higher value uses like specialist physicians because  you can charge dollars not cents
  • Don’t do cost-based pricing… go for value-based pricing
    • Value-based pricing is based on the needs you’ve learned from the customer segment and the strength of your product/market fit
      • the sum of customer needs + product/market fit = the pricing you can achieve

Lessons Learned

  • Each of these Life Science domains has a unique revenue strategy and pricing tactic
  • In therapeutics revenue comes in lump milestone payments from drug companies based on quality data
  • Diagnostics revenue comes value pricing to hospital or clinical lab
    • capped by reimbursement (CPT) code limits
  • Device pricing starts by offering an initial value-priced base product and then following up with a fully featured product
    • capped by reimbursement (CPT) code limits
  • Digital health products use subscription value pricing. Alternatively may use advertising revenue model

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Well They “Should” be Our Customers

When scientists and engineers who’ve been working in the lab for years try to commercialize their technology they often get trapped by their own beliefs  - including who the customers are, what features are important, pricing etc.

I hear nothing

——–

One the key tenets of the Lean LaunchPad class is that every week each team gets out of the building and talks to 10+ customers/partners to validate a new part of their business model.  Back in class they present their findings to their peers and teaching team in a 10 minute Lessons Learned presentation. One of the benefits of the class is that the teams get immediate unvarnished feedback on their strategy.

For researchers and clinicians who’ve been working on a project in the lab for years, getting out of the building and talking to customers at times creates cognitive dissonance.  While they’ve been in the lab they had a target customer in mind. However when they leave the building and start talking to these  supposed customers there’s almost always a surprise when the customer is not interested in the product.

Often when they consistently hear that their expected customers aren’t interested the first reaction is “the customers just don’t get it yet.”  Rather than testing a new customer segment they keep on calling on the same group – somehow thinking that “we just need to explain it better.”

Some times it takes a nudge from the teaching team to suggest that perhaps looking at another customer segment might be in order.

They Should be Our Customers
The Mira Medicine Team is trying to accelerate the path to the right treatment for each patient in complex Central Nervous System diseases. They spent years building their first tool MS Bioscreen, which was developed for the physicians at the UCSF Dept of Neurology. So they naturally believed that their first customers would be neurologists.

This was a very smart team who ran into the same problem almost every smart researcher attempting to commercialize science faces.  Here’s what happened.

If you can’t see the video click here.

Listen for:

0:35 “Our primary customer we built this app for was neurologists…

1:00 “(but neurologists have told us) your prototype is interesting… and probably some features are nice to haves…

2:26 “What’s special about neurology?  Doesn’t cardiology and oncology have problems like this?

3:00 “Is neurology a key component of what you’re trying to do?

3:15 “I’ve worked on this for two years…”

3:24 “You’ve already done too much prototyping work. You’re hung up on the prototype.”

3:29 “You have a square peg you’er trying to jam in a round hole…”

3:43 “Don’t be afraid to think laterally”

Postscript: 70 customers later they no longer talking to neurologists.

Lessons Learned

  • Don’t get trapped by your own beliefs
  • When reality outside the building doesn’t match your hypotheses – test alternate hypotheses
  • Most of the time your vision is just a hallucination

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Lean LaunchPad for Life Sciences – Distribution Channels

We’re teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) at UCSF with a team of veteran venture capitalists. The class has talked to 1,780 customers to date.

This post is an update of what we learned about life science distribution channels.

Life Science/Health Care distribution channels differ by Category
It turns out that for commercialization, the business model (Customers, Channel, Revenue Model, etc.) for therapeutics, diagnostics, devices, bioinformatics and digital health have very little in common.

This weeks topic was distribution channels; how your product gets from your company to your potential customer segments. You can get a feel for how each of the cohorts address the channel by looking at the distribution channel lectures below – covering the therapeutics, diagnostics, devices and digital health cohorts.

At the end of the lectures you can see a “compare and contrast” video and a summary of the differences in distribution channels.

Diagnostics

Week 3 Todd Morrill Instructor 

If you can’t see the presentation above click here

Digital Health

Week 3 Abhas Gupta Instructor 

If you can’t see the presentation above click here

Devices

Week 3 Allan May Instructor 

If you can’t see the presentation above click here

Therapeutics

Week 3 Karl Handelsman Instructor 

If you can’t see the presentation above click here

Life Science and Health Care Differences in Distribution Channels
This weeks lecture and panel was on distribution channels; how your product gets from your company to your potential customer segments. Therapeutics, diagnostics, devices and digital health use different different channels, in the video and the summary that follows the instructors compare and contrast how they differ.

If you can’t see the video above click here

Medical Devices (Starting at 0:50)

  • Medical Device Distribution Channels in general are a sales team hired directly by the company.
    • A sales team typically includes a sales person and clinical applications specialists.
    •  The specialists help train and educate physician users. They assist with the sale and work with marketing to create demand.
  • Some device industries are controlled by distributors (indirect sales.)
    • Distributors tend to resell commodity products from multiple suppliers.
  • Channel Cost =  $350-400,000 per sales team. On average there’s 1 clinical applications specialist to 2 salespeople.  A lean rollout for a startup would be 4-5 sales people plus 2-3 clinical applications specialists at a cost of ~$2.5 million/year
    • Increasing the number of sales people much past 4-5 for a rollout does not proportionally increase revenue in most cases, because you are on the front end of early adopters and wrestling to overcome and reduce the sales learning curve
    • Travel and Entertainment is a big part of the sales budget since they are all flying weekly to cover accounts
  • 90-180 days for salespeople to become effective
  • Expect little or no revenue for 2- 3 quarters after they start
  • Major reason for failure = hiring sales and marketing staff too quickly
  • Generally an Educational Sale - Hire sales and clinical people first to help early adopters, such as Key Opinion Leaders (KOL’s), master the learning curve with your device so they can write and present papers to influence their peers 

Diagnostics (Starting at 5:16)

  • Diagnostic Channels = Direct sales in the US, with limited Distributor options
    • Many Distributors in Europe and in Asia
    • Sold to hospital laboratories, reference laboratories, or performed in CLIA labs
  • Channel Cost = $350,000+ per supported salesperson
  • Direct to consumer is a (rapidly) growing channel

Digital Health (Starting at 7:25)

  • Digital Health Channels = Direct Sales but you’re selling software to both end users and enterprises
  • Can use existing tech channels and new emerging channels such as Wellness platforms. (Audax Health, Humana Vitality, ShapeUp, Redbrick Health, Limeade)
  • Cloud-based Electronic Medical Records (EMR) are quickly becoming another distribution platform
  • App Stores, and Box are also channels for consumers and enterprise customers, respectively

Therapeutics (Starting at 10:17)

  • Therapeutics Channel = what you’re selling in the early stage is data and Intellectual Property to the pharmaceutical and biotech companies
  • Complicated Sales process – takes 18 months
  • Led by the CEO with a dedicated business development person and your science team
  • You need to define the data they need – this is influenced by how they view their pipeline, and how your technology can fill gaps in their pipeline
  • Pharmaceutical and biotech companies have therapeutics heads, technology scouts and business development people all searching for technology deals to fill their pipeline
  • This is a bound problem – there’s probably 80 people you need to know that make up your channel

Lessons Learned

  • Each of these Life Science domains has a unique distribution channel
  • In Devices innovative products require hiring direct sales people
    • but for commodity device products you may use a distributor
  • Diagnostics requires a direct sales force in the U.S.
    • Distributors in Europe and in Asia
  • In Digital Health direct sales is a possible channel, as are traditional software channels (App Stores, Box, etc.)
    • other DHealth channels such as Wellness Platforms, and cloud-based EMR’s are also emerging
  • In therapeutics it’s a direct sale of data and Intellectual property
    • led by the CEO with a dedicated business development person and your science team

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Lean LaunchPad for Life Sciences – Value Proposition and Customers

We’re deep into teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) at UCSF with a team of veteran venture capitalists. (The class has talked to 1,440 customers to date.)shutterstock_81663952

One of the objectives of the class was to become a Life Science Center of Excellence for the National Science Foundation Innovation Corps. This meant capturing domain specific commercialization expertise for therapeutics, diagnostics, devices and digital health so others can teach this.

Part 1 of this post described the issues in the therapeutics drug discovery pipeline. Part 2 covered medical devices and digital health. Part 3 described what we’re going to do about it.  Part 4 gave a snapshot of what one our teams found the first week outside the building.

This is an update of our progress.

It Takes A Village
We’re teaching 110 students in 28 teams across therapeutics, diagnostics, devices and digital health. Teams are made up of clinicians, researchers, and post docs, (some of the team members include the Chief of  UCSF General Surgery, the inventor of Fetal Surgery, etc.)

Each of the four cohorts is taught by an experienced life science venture capitalist. Alan May for devices, Karl Handelsman for therapeutics Abhas Gupta for digital health and Todd Morrill for diagnostics.

Jerry Engel and Jim Hornthal, both who taught the National Science Foundation I-Corps classes, are the senior instructors. The UCSF Office of Innovation and Technology (Erik Lium and Stephanie Marrus) is the reason the program exists.

Each of the teams is assigned a mentor to match their domainv(Head of device R&D of Phillips, Genetech, Crescendo, CTO of UCSF, venture capitalists from Sofinnova, Burrill, Lightstone, M34, etc.)

Class Organization – Lots of Moving Parts
Our class meets weekly. We first meet as one group and then we break out the therapeutics, diagnostics, devices and digital health into their own cohorts. The teams present what they learned talking to 10-15 customer/week, and get comments, suggestions and critiques from their teaching team.  The instructor then presents  a cohort specific lecture explaining how the business model for their area (therapeutics, diagnostics, devices and digital health) builds on and/or differs from the canonical business model in the online Udacity lectures (which the students had to watch as homework.)

We get back together as one group and the instructors share what they learned as they compare and contrast the differences between therapeutics, diagnostics, devices and digital health.  We’ve recorded these panels for each part of the business model canvas.

The framework of the class looks like this:

Lean LaunchPad for Life Sciences

Life Science/Health Care is not a single Category
One of the reasons I teach is because of how much I learn. One of early surprises of this class for me is finding out that the broad category of “Life Sciences” fails to provide the important nuances of each category to entrepreneurs, investors, educators, policy makers, etc. It turns out that for commercialization, the business model (Customers, Channel, Revenue Model, etc.) for therapeutics, diagnostics, devices, bioinformatics and digital health have very little in common.

You can get a feel for how different by looking at the first two weeks of lectures – covering value proposition and customer segment – from each of the therapeutics, diagnostics, devices and digital health cohorts.

Then at the end of the lectures you can see a “compare and contrast” video and a summary of the differences.

Diagnostics

Week 1 Todd Morrill Instructor 

If you can’t see the presentation above click here

Week 2 Todd Morrill Instructor

If you can’t see the presentation above click here

Digital Health

Week 1 Abhas Gupta Instructor 

If you can’t see the presentation above click here

Week 2 Abhas Gupta Instructor

If you can’t see the presentation above click here

Devices

Week 1 Allan May Instructor 

If you can’t see the presentation above click here

Week 2 Allan May Instructor

If you can’t see the presentation above click here

Therapeutics

Week 1 Karl Handelsman Instructor 

If you can’t see the presentation above click here

Week 2 Karl Handelsman Instructor

If you can’t see the presentation above click here

Life Science and Health Care Differences

Once we realized that the four cohorts of therapeutics, diagnostics, devices and digital health were so different we decided to have the instructors compare and contrast how they’re different for each part of the business model. We’ll be posting these “compare and contrast” videos for every week of the class.

If you can’t see the video above click here

Therapeutics (Starting at 0:30)

  • Therapeutics customer = pharma and biotech companies
  • Therapeutics Pain & gain = be better than what these companies have in their own drug development pipeline
  • Therapeutics Validation =  18 months to a first deal with a potential customer – well before FDA trials, and even before preclinical stage

Digital Health (Starting at 2:40)

  • Digital Health Customer = typically consumer end users
  • Digital Health Pain & gain = product/market fit needs to be a need, and the value proposition must address it.  “Nice to have’s” do not equal a customer need.
  • Digital Health Validation = large scale adoption

Medical Devices (Starting at 6:00)

  • Medical Device customers = short term: physicians in private practice and hospital, long term: medical device companies
  • Medical Device Customer Goal – figure out the Minimal Viable Product.  No such thing as a perfect first generation product that targets a specific physician/customer segment.
  • Medical Device Validation= 95% of device startups are acquired by a medical device company, 5% build a large standalone company

Diagnostics (Starting at 10:45)

  • Diagnostic Customers = short term In vitro diagnostics are sold to a hospital laboratory or standalone lab, long-term you’ll be bought by Abbott, Roche, etc.

Lessons Learned

  • Each of these Life Science domains has a unique business model
  • Commercialization of therapeutics, diagnostics, devices and digital health all require the Principal Investigators / founders outside their building talking to customers, partners, regulators
  • Only the Principal Investigators / founders have the authority and insight to pivot when their hypotheses are incorrect
  • The Lean Startup process and the Lean LaunchPad class can save years in commercialization in these domains
  • This can be taught

Listen to the podcast here


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This Will Save Us Years – Lean LaunchPad for Life Science

We’re deep into week 2 of teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists.

Part 1 of this post described the issues in the drug discovery. Part 2 covered medical devices and digital health. Part 3  described what we’re going to do about it.

This is post is a brief snapshot of our progress.

Vitruvian is one of the 28 teams in the class. The team members are:

  • Dr. Hobart Harris  Chief of  General Surgery, Vice-Chair of the Department of Surgery, and a Professor of Surgery at  UCSF. Dr. Harris is also a Principal Investigator in the UCSF Surgical Research Laboratory at San Francisco General Hospital.
  • Dr. David Young,  Professor of Plastic Surgery at UCSF. His area of expertise includes wound healing, microsurgery, and reconstruction after burns and trauma. His research interests include the molecular mechanisms of wound healing and the epidemiology and treatment of soft tissue infections.
  • Sarah Seegal is at One Medical Sarah is interested in increasing the quality and accessibility of healthcare services. Sarah worked with Breakthrough.com to connect individuals with professional therapists for online sessions.
  • Cindy Chang is a Enzymologist investigating novel enzymes involved in biofuel and chemical synthesis in microbes at LS9

Vitruvian’s first product, MyoSeal, promotes wound repair via biocompatible microparticles plus a fibrin tissue sealant that has been shown to prevent incisional hernias through enhanced wound healing.  The team believed that surgeons would embrace the product and pay thousands to use it.  In week 2 of the class 14 of their potential customers (surgeons) told the team otherwise.

Watch this 90 second clip and find out how the Lean LaunchPad class saved them years.

(If you can’t see the clip above click here.)

Lessons Learned

  • Get out of the building

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Reinventing Life Science Startups – Evidence-based Entrepreneurship

What if we could increase productivity and stave the capital flight by helping Life Sciences startups build their companies more efficiently?

We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists.

Part 1 of this post described the issues in the drug discovery. Part 2 covered medical devices and digital health. This post describes what we’re going to do about it.  And why you ought to take this class.

——

When I wrote Four Steps to the Epiphany and the Startup Owners Manual, I believed that Life Sciences startups didn’t need Customer Discovery. Heck how hard could it be?  You invent a cure for cancer and then figure out where to put the bags of money. (In fact, for oncology, with a successful clinical trial, this is the case.)

Pivots in life sciences companies

But I’ve learned that’s not how it really works. For the last two and a half years, we’ve taught hundreds of teams how to commercialize their science with a version of the Lean LaunchPad class called the National Science Foundation Innovation Corps.  Quite a few of the teams were building biotech, devices or digital health products.  What we found is that during the class almost all of them pivoted - making substantive changes to one or more of their business model canvas components.

In the real world a big pivot in life sciences far down the road of development is a very bad sign due to huge sunk costs.  But pivoting early, before you raise and spend millions or tens of millions means potential disaster avoided.

Some of these pivots included changing their product/service once the team had a better of understanding of customer needs or changing their position in the value chain (became an OEM supplier to hospital suppliers rather than selling to doctors directly.) Other pivots involved moving from a platform technology to become a product supplier, moving from a therapeutic drug to a diagnostic or moving from a device that required a PMA to one that required a 510(k).

Some of these teams made even more radical changes.  For example when one team found the right customer, they changed the core technology (the basis of their original idea!) used to serve those customers. Another team reordered their device’s feature set based on customer needs.

These findings convinced me that the class could transform how we thought about building life science startups.  But there was one more piece of data that blew me away.

Control versus Experiment – 18% versus 60%
For the last two and a half years, the teams that were part of the National Science Foundation Innovation Corps were those who wanted to learn how to commercialize their science, applied to join the program, fought to get in and went through a grueling three month program.  Other scientists attempting to commercialize their science were free to pursue their startups without having to take the class.

Both of these groups, those who took the Innovation Corps class and those who didn’t, applied for government peer-reviewed funding through the SBIR program. The teams that skipped the class and pursued traditional methods of starting a company had an 18% success rate in receiving SBIR Phase I funding.

The teams that took the Lean Launchpad class  – get ready for this – had a 60% success rate. And yes, while funding does not equal a successful company, it does mean these teams knew something about building a business the other teams did not.

The 3-person teams consisted of Principal Investigators (PI’s), mostly tenured professors (average age of 45,) whose NSF research the project was based on. The PI’s in turn selected one of their graduate students (average age of 30,) as the entrepreneurial lead. The PI and Entrepreneurial Lead were supported by a mentor (average age of 50,) with industry/startup experience.

This was most definitely not the hoodie and flip-flop crowd.

Obviously there’s lots of bias built into the data – those who volunteered might be the better teams, the peer reviewers might be selecting for what we taught, funding is no metric for successful science let alone successful companies, etc.  – but the difference in funding success is over 300%.

The funding criteria for these new ventures wasn’t solely whether they had a innovative technology. It was whether the teams understood how to take that idea/invention/patent and transform it into a company. It was whether after meeting with partners and regulators, they had a plan to deal with the intensifying regulatory environment. It was whether after talking to manufacturing partners and clinicians, they understood how they were going to reduce technology risk. And It was after they talked to patients, providers and payers whether they understood the customer segments to reduce market risk by having found product/market fit.

Scientists and researchers have spent their careers testing hypotheses inside their labs. This class teaches them how to test the critical hypotheses that turn their idea into a business as they deal with the real world of regulation, customers and funding.

So after the team at UCSF said they’d like to prototype a class for Life Sciences, I agreed.

Here’s what we’re going to offer.

The Lean LaunchPad Life Sciences and Health Care class

The goal of the Lean LaunchPad Life Sciences class at UCSF is to teach researchers how to move their technology from an academic lab into the commercial world.UCSF Logo

We’re going to help teams:

  • assess regulatory risk before they design and build
  • gather data essential to customer purchases before doing the science
  • define clinical utility now, before spending millions of dollars
  • identify financing vehicles before you need them

We’ve segmented the class into four cohorts: therapeutics, diagnostics, devices and digital health.  And we recruited a team of world class Venture Capitalists and entrepreneurs to teach and mentor the class including Alan May, Karl Handelsman, Abhas Gupta, and Todd Morrill.

The course is free to UCSF, Berkeley, and Stanford students; $100 for pre-revenue startups; and $300 for industry. – See more here

The syllabus is here.

Class starts Oct 1st and runs through Dec 10th.

Download the all three parts of the Life Science series here.

Listen to the podcast here


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Reinventing Life Science Startups – Medical Devices and Digital Health

What if we could increase productivity and stave the capital flight by helping Life Sciences startups build their companies more efficiently?

We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life Sciences and Healthcare (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists.

In this three post series, Part 1 described the challenges Life Science companies face in Therapeutics and Diagnostics. This post describes the issues in Medical Devices and Digital Health.  Part 3 will offer our hypothesis about how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation.  And why you ought to take this class.

——–

Medical devices prevent, treat, mitigate, or cure disease by physical, mechanical, or thermal means (in contrast to drugs, which act on the body through pharmacological, metabolic or immunological means). They span they gamut from tongue depressors and bedpans to complex programmable pacemakers and laser surgical devices. They also diagnostic products, test kits, ultrasound products, x-ray machines and medical lasers.

Incremental advances are driven by the existing medical device companies, while truly innovative devices often come from doctors and academia. One would think that designing a medical device would be a simple engineering problem, and startups would be emerging right and left. The truth is that today it’s tough to get a medical device startup funded.

Life Sciences II – Medical Devices

Regulatory Issues
In the U.S. the FDA Center for Devices and Radiological Health (CDRH) regulates medical devices and puts them into three “classes” based on their risks.

Class I devices are low risk and have the least regulatory controls. For example, dental floss, tongue depressors, arm slings, and hand-held surgical instruments are classified as Class I devices. Most Class I devices are exempt Premarket Notification 510(k) (see below.)

Class II devices are higher risk devices and have more regulations to prove the device’s safety and effectiveness. For example, condoms, x-ray systems, gas analyzers, pumps, and surgical drapes are classified as Class II devices.FDA approvals

Manufacturers introducing Class II medical devices must submit what’s called a 510(k) to the FDA. The 510(k) identifies your medical device and compares it to an existing medical device (which the FDA calls a “predicate” device) to demonstrate that your device is substantially equivalent and at least as safe and effective.

Class III devices are generally the highest risk devices and must be approved by the FDA before they are marketed. For example, implantable devices (devices made to replace/support or enhance part of your body) such as defibrillators, pacemakers, artificial hips, knees, and replacement heart valves are classified as Class III devices. Class III medical devices that are high risk or novel devices for which no “predicate device” exist require clinical trials of the medical device a PMA  (Pre-Market Approval).Life Science Decline

  • The FDA is tougher about approving innovative new medical devices. The number of 510(k)s being required to supply additional information has doubled in the last decade.
  • The number of PMA’s that have received a major deficiency letter has also doubled.
  • An FDA delay or clinical challenge is increasingly fatal to Life Science startups, where investors now choose to walk away rather than escalate the effort required to reach approval.

med device pipeline

Business Model Issues

  • Cost pressures are unrelenting in every sector, with pressure on prices and margins continuing to increase.
  • Devices are a five-sided market: patient, physician, provider, payer and regulator. Startups need to understand all sides of the market long before they ever consider selling a product.
  • In the last decade, most device startups took their devices overseas for clinical trials and first getting EU versus FDA approval
  • Recently, the financing of innovation in medical devices has collapsed even further with most Class III devices simply unfundable.
  • Companies must pay a  medical device excise tax of 2.3% on medical device revenues, regardless of profitability delays or cash-flow breakeven.
  • The U.S. government is the leading payer for most of health care, and under ObamaCare the government’s role in reimbursing for medical technology will increase. Yet two-thirds of all requests for reimbursement are denied today, and what gets reimbursed, for how much, and in what timeframe, are big unknowns for new device companies.

Venture Capital Issues

  • Early stage Venture Capital for medical device startups has dried up. The amount of capital being invested in new device companies is at an 11 year low.
  • Because device IPOs are rare, and M&A is much tougher, liquidity for investors is hard to find.
  • Exits have remained within about the same, while the cost and time to exit have doubled.

Life Sciences III – The Rise of Digital Health
Over the last five years a series of applications that fall under the category of “Digital Health” has emerged. Examples of these applications include: remote patient monitoring, analytics/big data (aggregation and analysis of clinical, administrative or economic data), hospital administration (software tools to run a hospital), electronic health records (clinical data capture), and wellness (improve/monitor health of individuals). A good number of these applications are using Smartphones as their platform.digital health flow

Business Model Issues

  • A good percentage of these startups are founded by teams with strong technical experience but without healthcare experience. Yet healthcare has its own unique regulatory and reimbursement issues and business model issues that must be understood
  • Most of these startups are in a multisided market, and many have the same five-sided complexity as medical devices: patient, physician, provider, payer and regulator.  (Some are even more complex in an outpatient / nurse / physical therapy setting.)
  • Reimbursement for digital health interventions is still a work in progress
  • Some startups in this field are actually beginning with Customer Development while others struggle with the classic execution versus search problem

Regulatory Issues

  • Digital Health covers a broad spectrum of products, unless the founders have domain experience startups in this area usually discover the FDA and the 510(k) process later than they should. 

Venture Capital

  • Seed funding is still scarce for Digital Health, but a number of startups (particularly those making physical personal heath tracking devices) are turning to crowdfunding.
  • Moreover, the absence of recent IPOs and public companies benchmarks creates uncertainty for VCs evaluating later investments too

Try Something New
The fact that the status quo for Life Sciences is not working is not a new revelation. Lots of smart people are running experiments in search of ways to commercialize basic research  more efficiently.

Universities have set up translational R&D centers; (basically university/company partnerships to commercialize research).  The National Institute of Health (NIH) is also setting up translational centers through its NCATS program.  Drug companies have tried to take research directly out of university labs by licensing patents, but once inside Pharma’s research labs, these projects get lost in the bureaucracy.  Realizing that this is not optimal, drug companies are trying to incubate projects directly with universities and the researchers who invented the technology, such as the recent Janssen Labs program.

But while these are all great programs, they are likely to fail to deliver on their promise. The assumption that the pursuit of drugs, diagnostics, devices and digital health is all about the execution of the science is in most cases a mistake.

The gap between the development of intriguing but unproven innovations, and the investment to commercialize those innovations is characterized as “the Valley of Death.”valley of death

We believe we need a new model to attract private investment capital to fuel the commercialization of clinical solutions to todays major healthcare problems that is in many ways technology agnostic. We need a “Needs Driven/Business Model Driven” approach to solving the problems facing all  the stakeholders in the vast healthcare system.

We believe we can reduce the technological, regulatory and market risks for early-stage life science and healthcare ventures, and we can do it by teaching founding teams how to build new ventures with Evidence-Based Entrepreneurship.

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Part 3 in the next post will offer our hypothesis how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation. And why you ought to take this class.

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