"You Don't Understand Healthcare" A Product Manager's Perspective
It was a typical day of rain mixed with some late snow on a Tuesday morning in April of 2010 in Chicago. Racing towards Kellogg Career Centers, I remember vividly how nervous I was heading into round 2 of my interview with a top Healthcare company. With a background heavy in Finance and Telecom, I had built extensive notes on the industry over weeks of online research and information interviews with b-school colleagues. As I was scrubbing through those notes and trying to get organized, it was time for me to meet the panelists.
We started off with formal introductions and exchanged icebreakers about the weather, but immediately thereafter I was reminded about the fact that the position I had applied for generally requires someone with 8+ years of Patient Marketing experience or 10+ years of experience in Payer-Provider space even though it was not formally stated.
“That’s a valid point but let’s not forget that a company with zero telecom patents and no clue of those 1000s of pages of 3GPP specifications has just built a better smartphone than Motorola, Nokia, Samsung, LG, and HTC,” I said promptly. “Great point Mr. Patel, but Healthcare is complicated,” was their rebuttal with an expression of polite disagreement supported by additional stats about the industry thrown back at me.
“Seriously?” I said to myself. At the expense of those points I used to outbid other interviewees, for all the excitement I had about switching careers and applying those leanings from Marketing and Entrepreneurship courses, all I got was that I wasn’t experienced enough? And this was just the beginning of such reminders in subsequent interviews to come in the following years.
Nevertheless I managed to switch careers in a matter of 3 odd years into Product Strategy and Go-to-Market and my leanings were no different from what they would have been for any outsider who adopted the strategy of Change of Approach vs. Accelerated Learning. It worked because I firmly believe that learning is built into your change in approach to solving a problem.
After Apple disrupted the handset market, came Tesla Model S, which eventually made Tesla the most valuable car company in just 10 years of being in business and beating traditional car companies who had over 1000 years of cumulative industry experience. This was also the age of data monetization where we began to see large companies becoming global players in social media data monetization, risk rata monetization or say Airbnb and Uber who became the largest accommodation and transportation companies without owning any physical assets.
These and many more examples are social proof to fixing a problem by doing things differently than piling up a workforce of experienced professionals or industry graduates.
Reading about the AMZN – BRK – JPMC tie up, I am excited once again that it’s time for those who are not from the Healthcare industry to fix a problem by challenging the status quo. I gathered that a lot of answers rested in transforming the system from a Payer-Provider centric to a solution that revolved around Patient Experience. What if we stopped treating a patient as a billable entity and started to think of them as customer?
Based on my experience in Predictive Analytics on Big Data, I am quoting some interesting analysis by Milliman and here are 3 areas where the Buffet - Bezos – Dimon coalition can help transform healthcare:
Internet of Health (IoH) – Amazon with their history of finding ways to break tangled infrastructure problems down to manageable pieces with clear interfaces to eliminate complexity and make systems talk to each other, can take advantage of today’s massive data collection to assimilate health risk factors and built early warning alerts in real time with Amazon’s Predictive Analytics on Big Data architecture that can take any form of data from any source.
Online Healthcare Marketplace – The success of Affordable Care Act suggests that lack of competition and pricing transparency among healthcare providers contributes significantly to high cost of healthcare. Could the technology powering Amazon’s Marketplace also power a Healthcare Marketplace to create a nationwide transparent cost network of hospitals, physicians, and pharmacies? This will flush out the high cost providers.
Pharma of the future – BRK and JPMC, as financers could develop value-based contracting solutions to provide pharmaceutical manufacturers with a predictable profit stream over the lifecycle of a drug, in change for improved pricing. AMZN’s data management and predictive analytics capabilities could help improve medication adherence, identify personalized medicine opportunities, and monitor medical outcomes all of which tie back to rendering a world class patient experience.
With application of analytics on EMR data, a patient-centric solution can be built where claims are no longer required for processing. Direct access to medical record systems by insurance companies could eliminate the claims process while enhancing the data available for healthcare analytics. This will ensure that a hospital CFO will no longer have to say they have 900 beds and 1300 billing clerks. More over a robust analytics architecture can also produce better outcomes and patient behavior in terms of preemptive care and healthy living.
It's no secret that these outcomes are highly influenced by factors that lie outside of healthcare system. One needs to look no further than 500 plus studies by Robert Wood Johnson Foundation that effectively conclude lifestyle, location and several ancillary attributes that would roughly make up 80% of the social determinants of health outcomes.
80% of Social Determinants of Health Outcomes Lie OUTSIDE of Healthcare
With AI and Big Data, technology and data companies are well positioned to make this category of information digitized, secured and consumable for healthcare to transform. The following diagram by Robert Wood Johnson Foundation helps understand the value of external data sources which answer critical questions around lifestyle as well as physical location & environment specific risk attributes.
Now if you are a Healthcare professional you are probably thinking, “Easier said than done” or “You need experience in healthcare.” So here's a live example for you. 15 years ago no one could have imagined that with lots of data and AI we could build a car that will run faster than a Porsche and require no gas, no engine, no muffler, no transmission and no maintenance. Would you have believed that this car company, with no experience in car building, will exceed in market value over traditional car companies with over 1000 years of cumulative car building experience?
Let's cut through this mindset and start looking at data, shall we?