I was at the SAP Investor Symposium in New York last week, listening to SAP’s put forward their strategy and financial plan for 2017. SAP Board Member Vishal Sikka talked about his mother, who suffers severely from Type 2 diabetes and how he is looking to improve the quality of healthcare in the world by doing genome sequencing using their SAP HANA data platform.
Vishal also discussed briefly how Chairman Hasso Plattner had wanted to re-invent financials for a fourth time in his career. This time, SAP is building financials, tailored for their SAP HANA data platform – with no totals, calculations or aggregations calculated in advance. This means that the Balance Sheet, Cost Center Structure or P&L can be calculated in real-time, as well as real-time profitability analysis or forecasting, and other more sophisticated calculations and algorithms.
But more than that, it means that because information is only stored once, changing organization structures, reallocating resources, or even a full change to the chart of accounts, can be done on the fly. In the midst of this, SAP claim as much as a 50x reduction in space required.
The biggest problem with diabetes, from a global perspective, is the cost of dealing with the consequences and complications of Type 2 diabetes. It is estimated that as much as 25% of critical cardiac wards are filled with diabetes patients, and the complications like nerve damage and cataracts multiply the cost of care.
The details of what causes, and how to reduce the risk of diabetes, are heavily researched but not necessarily well understood. My grandmother had, and Vishal’s mother has diabetes, so we offset the risk of eating a slice of New York pizza against the health benefits of walking, as we headed to the next meeting.
I am being flippant, and this is a serious issue. Insufficient quality information is gathered about non-communicable diseases and they are expected to rise to a 20-year societal cost of $45 trillion. That’s $45,000,000,000,000. Diabetes is the #2 non-communicable disease.
There are several things that we can do about this, and we can start by creating accurate measurements. It is possible to measure blood sugar level (using sensors), activity (using Nike+, FitBit etc.) and various diagnostic metrics, but these sensors are not integrated today into a single view of the patient.
It’s a difficult measure to say to a person that they can cut their risk of diabetes by eating fewer donuts or running more miles, because it’s tough to connect cause and effect, and even tougher to get their attention as they race between meetings, trying to make their way through life.
And so it comes that I met with a health company called Ithaca Health, and we built a SAP HANA based healthcare system. I had heard rumors about what SAP had been doing with their next-generation financials, and we built a similar healthcare system based on the same premise.
What’s more, we built it on SAP’s new River development platform. The purpose of River is to separate intention and optimization, but have both. We can write a complex app like this in a few hours, and flesh it out in a few days. We capture everything from patients, to events, to payers, providers and diagnosis at the lowest level of detail.
We don’t hold any unnecessary information, including ages, or any calculations like “Is Diabetic”. Instead, when you view a patient record, it will calculate the likelihood of diabetes. This is based on a diagnosis, if it exists, but also lifestyle risks and measurements like blood sugar levels.
In existing healthcare solutions, these calculations are produced overnight. So if a new piece of information becomes available, there is a delay before a doctor can use it to diagnose a patient in context of the previous data.
In the United Arab Emirates, Ithaca built a health solution where they screened the whole population for heart disease, and assigned a risk measurement to each person. The prevalence of risk to heart disease was a surprise, but it did allow them to start to proactively try to reduce the long term cost of supporting non-communicable diseases.
Moving onto a research perspective, if we can store sufficient information, we can aggregate it for research purposes. We built this in SAP River, and we don’t have to build a separate data warehouse; we just define the calculations and aggregation levels required, and the data is aggregated in real-time.
This causes an incredible simplicity in design which reflects in the time taken to build the solution. We estimate 20x smaller and 20x faster to build as a result, which is incredible.
And it is just possible that by aggregating lifestyle and diagnostic information about millions of people, we can start to really understand the lifestyle, diagnostic criteria, and medication which make a difference to people’s lives.