America is living in a healthcare paradox. The country has received more Nobel Prizes in Medicine than the rest of the world combined since 1966, yet it was also ranked lowest by the OECD in clinical efficiency.
Think about it, what other country can boast the most advanced hospitals in the world and yet erroneously kill 440,000 of its patients in hospitals through medical errors? That’s comparable to the entire population of Miami being wiped out every year because of medical errors!
These figures are more concerning still when you consider the fact that the US have the highest health expenditures per capita among developed countries. In a year, the government spends on average $8500 on healthcare per American. The second highest spender is almost $3000 behind with $5700 per Canadian spent per year. Yet perhaps the most telling statistic is simply the fact that fax is still the 2nd primary form of peer-to-peer communication in US healthcare.
Transitioning to a Value-Based System
As you probably already know, the United States has recently begun its transition to a Value-Based Care System (VBC). VBC, as you probably already know, is opposed to Cost-Based Care (CBC) and is the principle behind the reimbursement system in the US. It refers to the Value equation: Quality over Cost over Time. So, for patients, it means effective care at low cost. Value Based Payment is slowly displacing CBC. Under this plan, 90% of all traditional CBC Medicare payments should be tied to quality or value and 50% would be tied to alternative payment models by the end of 2018.
Data Analytics key to achieve care coordination.
All these major changes necessitate a greater level of interoperability between payers, providers and fiscal intermediaries, at a national and federal level. The goal of this new interoperability is to create a unified system where data flows from the payer to the provider, allowing for better care and cost reduction.
The core fundamentals of this type of model are the focus on care coordination and collaborative leadership across networks. These use of big data analytics is a major contributor to the success of that accountable care.
And healthcare data is multiple and flowing in from everywhere: social media/web’s data, machine 2 machines’ data, transactional data, biometric data, genomic data, and human-generated data… The HITECH Act of 2009 has sought to promote the adoption and meaningful use of health information technology to put order in that data. Patient data is now required to be electronically available in an effort to establish a healthcare system based on the principles of Value-Based Care (VBC).
A major limit: little (if any) interoperability
However Healthcare in the U.S. is characterized by a high degree of fragmentation. Hospitals have dozens of proprietary tools, siloed systems, and antiquated methods in place that generate severe inefficiencies. Imagine yourself printing datasets out on a sheet of paper and crossing the street to transcribe them on another hospital’s system? That is the kind of thing that happens. Today. In the US.
There is one possible reason for this: as of March 2015, there were 779 health IT vendors. Most of them are selling self-contained systems, intentionally siloed computing systems. That’s why a US hospital can have an average of 10 different Electronic Health Records. This makes data use and sharing far from optimal.
Healthcare Industry lagging behind innovation and money-savings
What is the point in having data if you can’t properly use It? Indeed, many believe this data shortcoming has impeded innovation in healthcare. Skype has been up and running since 2006 but telemedicine is still a far-off prospect. The Internet of Things is a booming market but you still need to go check your sugar rate at the hospital twice a week. We can forecast weather but patient forecasting is still based on historical averages.
Data sharing and use are therefore far from being optimal
Don’t worry, all is not lost. We’ll tell you soon how Dataiku's DSS plans to save yearly the US healthcare system $450B.
How to help the healthcare system to go data-driven?
According to a KPMG Study (April 2015), health system analytics is about to grow exponentially but respondents cite 3 significant barriers:
having unstandardized data in silos (37%)
lack of technology infrastructure (17%)
data & analytics skills gap (15%)
It’s time for a change as DSS is designed to address these 3 challenges. We are indeed convinced that healthcare is an information business where setting up interoperability between data flows is crucial to deliver live analytic learning. That’s why Dataiku has been working with healthcare providers and payers aiming at delivering the best possible treatment at minimal cost.
What we do in healthcare:
At Dataiku, we think healthcare providers and payers should focus on what truly matters: delivering the best possible patient care while minimizing costs.
That’s why DSS can clean, aggregate and transform data from numerous health IT systems. It also frees up your business analyst teams to pursue more development opportunities.
In a healthcare market where Value-Based Care is the dominant paradigm, DSS enables its customers to maximize service value through Fraud Detection, Staffing Optimization, Physician Profiling, Process Improvement, Churn Reduction, Predictive maintenance , No-Show forecasting. Click for more insights on predicting doctor drug prescriptions or developing indicators using healthcare data.
Healthcare providers will be able able to deliver better care, with DSS helping them to optimize disease management, improve patient safety and expand potential for precision medicine (click here to see it can be done with breast cancers).
Finally, Data Science Studio can help any healthcare players to develop Health knowledge and awareness, in strengthening consumer engagement, developing advanced population health management and favoring open transparency.
Healthcare is an information business where the difference between life and death is at stake. The possibilities for predictive analytics are endless and are indicative of the world we live in: where vast quantities of raw data can be accessed, cleansed, collected, parsed, formatted, and elegantly visualized in a meaningful way.
In case of any questions, just ask me!