5 ways Amazon can disrupt Healthcare and Pharma in 2019
Goldman Sachs recently produced a 30-page report based on the the work of five research analysts focused on Amazon's likely ambitions to enter the Healthcare and Pharmaceutical industries. Some of the most notable predictions the report makes are ...
1) "Amazon could move into digital health by using the Echo in clinical settings and developing tools for telemedicine and remote patient monitoring. "Imagine seeing a virtual doctor on your Amazon app, having it prescribe you a certain medication, and then tapping a 'buy now' button -- all without leaving your home."
2) "Rather than replacing pharmacies right away, Amazon might start by partnering with a pharmacy benefits manager (PBM), which acts as an intermediary between payers, like health insurers, and the rest of the health system. That would provide "access to patient data and the potential to cross-sell related products."
3) "Amazon could also become an online pharmacy, retail and online pharmacy, integrated PBM and online pharmacy, or handle drug distribution to pharmacies.
Leaving Pharma aside, I agree with Goldman Sachs and believe Amazon can disrupt the Healthcare industry within the next 2 years based on the following 5 steps and by executing their highly successful modus operandi ..
Step 1 : Collect all the Data
Legacy Issues - " Amazon's new team is currently looking at opportunities that involve pushing + pulling data from legacy electronic medical record systems.”
Medical Records - "Amazon is looking for a machine learning director with experience in healthcare IT & analytics and knowledge of electronic medical records”
Hospitals - "Amazon Web Services has hired health experts to beat out Microsoft and Google for contracts with large hospitals & pharmaceutical vendors”
Amazon are clearly tackling one of the biggest issues in healthcare, the fact patient data and medical records are stored on legacy systems.
This is a very smart approach because once Amazon has access to patient data and medical records, it can run machine learning algorithms across these large data sets to identify patterns, behaviours and potentially fraud.
Step 2 : Own the Infrastructure
My prediction is Amazon’s next move will be similar to Google’s acquisition of Apigee, the leading provider of FHIR based API’s.
Amazon will acquire a FHIR based API company with specialist knowledge of healthcare & pharmaceuticals and then integrate it into Amazon Web Services to offer a market leading software as a service model.
Infrastructure in Healthcare is all about scale, and scale is exactly what Amazon has ... Amazon Web Services’ Global Infrastructure operates 44 Availability Zones within 16 geographic Regions around the world, with announced plans for 14 more Availability Zones and five more Regions in China, France, Hong Kong, Sweden, and a second AWS GovCloud Region in the US.
Step 3 : Distribute the Infrastructure
Amazon have a history of building developer networks and actively encouraging 3rd parties to build on top of their infrastructure, healthcare will be no different. In the next 2 years expect to see "Amazon Health Dev Days" and workshops as Amazon invest heavily in distributing their technology across the healthcare market.
Interested in building an Alexa powered voice commerce solution for older people in care homes to order presents for their grand children, no problem ...
Interested to building a mobile application for your cousin with diabetes, no problem..
In May 2016, Amazon started giving away its most sophisticated personalisation technology. DSSTNE (pronounced “destiny”), an open source artificial intelligence framework that the company developed to power its product recommendation system.
Now any company, researcher, or curious tinkerer can use it for their own AI applications.
“We are releasing DSSTNE as open source software so that the promise of deep learning can extend beyond speech and language understanding and object recognition to other areas such as search and recommendations,” the Q&A section of Amazon’s DSSTNE GitHub page reads.
Step 4 : Personalise Online
Amazon has the best recommendation engine in the world, they were very early into massive machine learning projects to offer personalised products to every customer based on many inputs ...
Purchased shopping carts = real money from real people spent on real items = powerful data and a lot of it.
Items added to carts but abandoned.
Pricing experiments online (A/B testing, etc.) where they offer the same products at different prices and see the results
Packaging experiments (A/B testing, etc.) where they offer different products in different "bundles" or discount various pairings of items
Wishlists - what's on them specifically for you - and in aggregate it can be treated similarly to another stream of basket analysis data
Referral sites (identification of where you came in from can hint other items of interest)
Dwell times (how long before you click back and pick a different item)
Ratings by you or those in your social network/buying circles - if you rate things you like you get more of what you like and if you confirm with the "i already own it" button they create a very complete profile of you
Demographic information (your shipping address, etc.) - they know what is popular in your general area for your kids, yourself, your spouse, etc.
user segmentation = did you buy 3 books in separate months for a toddler? likely have a kid or more.. etc.
Direct marketing click through data - did you get an email from them and click through? They know which email it was and what you clicked through on and whether you bought it as a result.
Click paths in session - what did you view regardless of whether it went in your cart
Number of times viewed an item before final purchase
If you're dealing with a brick and mortar store they might have your physical purchase history to go off of as well (i.e. toys r us or something that is online and also a physical store)
Now imagine all these factors and more applied to Healthcare ...
Step 5 : Personalise Offline
The final piece of the jigsaw, integrating your online customer experience with your offline customer experience.
Imagine a world where Amazon recommends and personalises your food shopping based on your health? You have "opted in" to recommendations of what food to buy based on your medical record and medical history.
You "click and collect" your order at the local Amazon Whole Foods and potentially pick up any medication you require. So now I have a "plant based diet" personal to me based on Amazon's machine learning algorithm which calculated the latest scientific research from tens of thousands of medical journals ....
The ultimate online/offline customer experience could be only years away, order via your Amazon mobile app lying in the bath on Friday night, collect your Amazon order on Sunday morning from an Amazon Whole Foods store because you have a few questions to ask or wait for the Amazon delivery driver to knock on your door ...
Let's continue the conservation and debate how quickly Amazon can disrupt Healthcare, tweet @lloydgprice :)