The healthcare industry is changing – fast. The U.S. Department of Health and Human Services says 30% of Medicare payments need to shift from volume-based to value-based care reimbursement models by 2016, and 50% by 2018. Healthcare providers still have a lot of work to do to successfully make this transition.
The 2015 HIMSS Leadership Survey found that while 41% of healthcare leaders think big data analytics is a number one priority for their organization, a surprising 81% still have basic questions around the quantity and type of data they should be collecting and how to actually turn that data into insight.
That’s not especially good news considering healthcare data continues to grow at 48% per year through 2020 from clinical applications, Internet-enabled medical devices, wearables, and remote patient monitoring. With questions on how to manage all of the data being generated today, how will healthcare providers collect, secure, and share the next big wave of information to come?
What are the next steps? How can healthcare organizations achieve accountable care and effectively support the empowered patient?
Making a Splash with Predictive Analytics
Consider the key role predictive analytics play as hospitals work to reduce their 30-day readmissions rates for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN). To meet these requirements, healthcare providers must speed up the continuous feedback process using analytics across the continuum of care, including inputs from patient monitoring located in the home.
Healthcare providers that have successfully implemented predictive analytics see measurable results – 54% of health IT leaders who spent 1-5% of their operating budget on analytics reported success within financial and clinical management.
Advanced predictive analytics also provide organizations with clear cost reductions – Goldman Sachs predicts the total savings opportunity from digital health initiatives will be $305B. But how do healthcare organizations eliminate redundant infrastructure and copies of data, while improving data governance and compliance?
Eye on the Data Lake Horizon
A data lake provides massive scalability and multi-protocol data-in-place analytics, along with the enterprise data protection and security required by healthcare organizations. It provides a powerful data architecture with a unified location to help reduce silos across the healthcare enterprise. Data can also be connected from trusted outside sources including payers, genomic research centers, public health databases, biobanks, and social media feeds.
Clinical departments, business analysts, and data science teams can conduct effective cross-data analysis as all internal data sources and trusted external sources are incorporated. Healthcare providers can further advance accountable care initiatives, creating a new realm of data science for uncovering trends, patterns, relationships, correlations, and discoveries that can impact integrated patient care.
And, for a best practices example, see how Partners Healthcare, a Boston-based non-profit hospital and physicians network is leveraging a data lake to bring together databases from across disparate systems – speeding the time from research to discovery to clinic to improved patient care. Click here to learn more about their journey.
