Really interesting topic. I’ve used Lemonade and found it easy to use and vastly more transparent then traditional insurance companies. I would be curious of the marginal impact of investing in more data as they should be able to acquire data from the existing customer base as they grow. I would also be curious to see how they stack up against traditional competitors in this space. Glad that you chose to write about Lemonade as I was interested in learning more!
I really enjoyed reading this – super cool/interesting that there are bioprinting companies that are creating real body parts. I’m hopeful that Organovo is able to overcome there cash flow issues as it would be a shame for a company that is working on such an important/vital societal issue to go bankrupt. I guess I am curious what other body parts Organovo has in their pipeline? I am also curious if Organovo believes it will be possible to eventually print a human and in what time frame? I thought this was super intriguing and I look forward to continuing to follow developments in this space!
Awesome essay! It’s reassuring to see that our technology and healthcare corporate communities are working together on one of the more challenging issues that we face as a society – fixing/improving our healthcare system. This morning I found out that an old colleague of mine opted to have a knee replacement surgery done by a robot vs. a traditional knee surgeon, which shocked me as I wasn’t aware of how far along machine learning was among the different verticals in healthcare. I’m curious if Intelligent Scribe played a role in my old colleagues surgery… I would be curious how Intelligent Scribe works / is integrated with the electronic health record systems (EHR) within hospitals. I know these software platforms (epic, all scripts, etc.) are complex and are often hard integrate with. It seems as though Intelligent Scribe would benefit an incredible amount from integrating with these systems. In any regard, great work; I really enjoyed reading this.
Very interesting. I learned how to bill insurance companies, including Cigna, over the course of the past year while working at an ambulance company and it was among the most complex, non-intuitive tasks that I’ve had to learn. I would be curious if the machine learning that Cigna is investing in could be applied to fixing the company’s claim reimbursement process. Aside from the billing / claim reimbursement side of the business it sounds like the machine learning that Cigna is investing in will hopefully benefit the firms customers. Does the increased cost base cause an up-tick in the cost of acquiring insurance for consumers? Interesting and informative essay – really enjoyed reading it.
Interesting article, I’ve heard how these apps work but had never really delved into the mechanics. I’m not shocked that there is such a low hit rate with regard to the matches / phone number exchanges on the app as the interpersonal nature of the product is pretty benign, whereas in person hitting the accept / reject button is a bit more difficult. What I did find interesting was the parallel that was drawn to Amazon and Netflix. I’m curious if the firms were to swap data scientists if the 1/500 phone number hit rate would improve. In any regard, I really enjoyed reading this – great job!
Very interesting topic. I’m certainly glad to hear that Comcast is using machine learning to help better service their customers as I have felt the brunt of their service woes over my lifetime. I am not an expert on this subject but I believe the federal ruling around Open Internet for over the top providers is vital to understanding Comcast’s ability to use it’s current, already established infrastructure to benefit it’s product offering moving forward. If this ruling goes away I think it really increases the earning power of their moving forward, although I think this would be negative for the consumer as it would hinder over the top providers, of which I think are doing a relatively good job. Overall really nice work!