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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