Innovation4Alpha Se1 Ep1 — Correlation

Welcome to the all new Innovation4Alpha podcast. The goal of Innovation4Alpha is to provide insight and entertainment to the world of healthcare investing. AngelMD CEO Tobin Arthur and SVP of Clinical Investment Operations Jeff Ross, MD lead the discussion, and welcome your feedback.

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New Startups on AngelMD – April 2019

Each of the following companies joined AngelMD during the month of April, 2019. Make sure to follow the companies that are operating within your areas of interest in order to stay up to date with their progress.

Artificial Intelligence

Bio Science

Medical Device

Diagnostics

Consumer Product

Patient Care

EHR/EMR

Pharmaceutical

Cost Reduction

Telemedicine

Mobile Health

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Turning the Flywheel

Like the late Peter Drucker, I find anything written by Jim Collins compelling. He is a keen observer of human behavior through the lens of companies. One of his more poignant insights was that of the Flywheel concept. He first articulated this concept in his bestseller “Good to Great” and has now expanded on it in a short form book (monograph) appropriately entitled “Turning the Flywheel.”

As Collins writes:

“Once you fully grasp how to create flywheel momentum in your particular circumstance, and apply that understanding with creativity and discipline, you get the power of strategic compounding. Each turn builds upon previous work as you make a series of good decisions, supremely well executed, that compound one upon the other. This is how you build greatness.”

The concept is powerful as a framework for understanding why some businesses build momentum while others idle or die. As an investor I particularly love anything that leverages the concept of compounding.

Collins shares the story of how Amazon embraced and honed their flywheel and went on to become a juggernaut. The flywheel behind Vanguard also presents a solid reference.

But flywheels aren’t easy. If they were, every company would get them right. Rather, they require rigorous thought, experimentation and iteration. If there are five key elements of the flywheel and only three of the five are operating efficiently, then the flywheel doesn’t work.

Each of the core elements has to contribute to the momentum,

I suspect there are a lot of flywheels in training out there that need some consideration and polish, but with effort could transform a mediocre business into a force. Part of that consideration and polish is implementing a core system like OKRs that can help a company keep itself guided toward its true north.

This post is being written as a book recommendation for both startups and investors. Every business owner needs to be considering whether or not there is an opportunity to build a flywheel effect into their business. Similarly, every student of business, otherwise known as an investor, should have a clear grasp of this concept in order to determine if an investment candidate has a flywheel embedded into their business model. 

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Healthcare and the Age of Data

This is the fourth in a series of articles where we examine the macro-level trends that support the thesis of our business.

There are so many incredible ways in which big data, artificial intelligence, and machine learning are used behind the scenes to impact our everyday lives. The technologies are frequently used to inform business decisions and optimize operations.

AI, machine learning, and big data have become ubiquitous. You’ve spent years feeding data into your phone, so now it forecasts your commute time. Amazon uses purchase and demographic data, combined with machine learning, to automatically recommend products you might enjoy.

The following excerpt from a Hacker Noon article helps to further explain the concepts:

Artificial intelligence is the technology that allows computers to do things that were once only the domain of humans. For example, computers have always been able to calculate. With AI, they can learn and draw conclusions.



AI can be roughly divided into two disciplines. These are machine learning and deep learning. Machine learning involves the creation of computers and software that can learn from data, and then apply that knowledge to new data sets. Deep learning creates neural networks, designed to resemble the human brain. Deep learning is used to process data such as sounds and images.



AI doesn’t work without data. It consumes data in order to learn. Big data refers to the massive sets of data that are now available for this purpose. These sets of data can be analyzed by machines. This can reveal patterns and trends and facilitate making future predictions.

For healthcare specifically, Medical Economics identified artificial intelligence as the top trend for 2019 in their March 1 article.

Artificial intelligence (AI) technologies should give us great hope for the near future. The technology will enhance, not replace, human efforts. AI promises to alleviate repetitive burdens and provide more accurate tools, so that the medical community can offer better care. Its explosion onto the funding/startup scene is both indicative of its potential and the reason less tech-savvy people feel overwhelmed. 

There is a large greenfield of healthcare opportunities as AI and machine learning are applied to healthcare. HITECH, the Health Information Technology for Economic and Clinical Health act, mandated the use of datasets, which are now being used to feed machine learning and AI.

As one example, HealthPals is an AngelMD portfolio company that leverages big data to help provide population insights. The company’s use of big data allows it to optimize medical, quality, and cost considerations in ways that we have not seen before.

We see significant portfolio investment opportunities in a wide degree of areas that machine learning and AI can impact. Imaging, population health, and even marketing for large healthcare organizations are some of the more obvious choices. But medical billing, EHRs, and medical devices are all ripe for disruption by data as well.

We are just starting to scratch the opportunities for big data and AI in both healthcare and AngelMD. The AngelMD platform benefits from the application of big data and artificial intelligence principles through our Metis Engine. As participation grows on the network, we are able to identify patterns that can be leveraged to grow the membership, inform portfolio choices, and coach portfolio companies to a better, faster exit.  

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Disruption Takes Time and Money

Throughout history, gatekeepers have erected barriers to keep people from having direct access to markets. Real estate buyers and sellers needed an agent. Travel agents had access to better deals. Brokerage houses maintained critical data for investing. Heck, even the church conducted mass in Latin so that you had to rely on a priest to tell you what was going on. (Perhaps that’s blasphemous to say during Lent, but I digress…)

The Internet has enabled massive, rapid disruption of the gatekeepers. Consumers now have more direct participation than ever before, and the gatekeepers are floundering.

This disruption is not slowing. We will continue to see major impact where it has already began, and we can expect to see untouched markets become targeted. Healthcare is no exception to this rule. The consumerization of healthcare has a host of implications, and doors are opening to a wealth of opportunity for entrepreneurs. (To be fair, self-diagnosing that red spot on your arm via WebMD and Google is not an especially byproduct of this democratization.)

As an investor, you’re aware of the importance of trends. This democratization of information and markets needs to be part of your calculus. It will create headwinds for traditional players such as brokerage houses and payers, while enabling new blood to thrive. The middle man is gasping for breath as more efficient mechanisms of connecting consumers with providers take hold. Technology-enabled connectors and consumers are going to come out as the winners.

But there is a catch. Disruption of inefficient markets doesn’t happen overnight, and it’s not cheap. It takes pools of capital to gain critical mass, and it takes time to move beyond the early adopter. Amazon is a shining example of these facts. While it’s now obvious how much impact the company has had on traditional players, 1998 told a much different story. Then it was novel, but Barnes & Noble, Borders, and others didn’t take the threat seriously. Even if they had, it’s unlikely that these stalwarts could have evolved fast enough to save themselves.

As you are evaluating startup investments, it’s important to look beyond the potential for disruption. Examine the time horizon that is required to gain market penetration, and the amount of capital needed to get there. The company should have research and thoughts on these topics, so the onus does not rest on you alone. If the numbers or the logic don’t add up, then it may be too early to invest. But, if the management seems to have ideas that are well-thought, and contingencies in place for when things go wrong, it may warrant further consideration.

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