The healthcare industry has historically generated large amounts of data driven by record keeping, compliance and regulatory requirements, and patient care. With all of that data, is your organization leveraging analytics to improve patient care? What type of healthcare analytics culture does your organization have?
While most data was previously stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data. Driven by mandatory requirements and the potential to improve the quality of healthcare delivery while reducing the costs, these massive quantities of data (known as ‘big data’) hold the promise of supporting a wide range of medical and healthcare functions including (among others) clinical decision support, disease surveillance, and population health management.
Reports say data from the U.S. healthcare system alone reached 150 exabytes in 2011. At this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (1021 gigabytes) scale and, not long after, the yottabyte (1024 gigabytes). For example, California-based health network Kaiser Permanente, which has more than 9 million members, is believed to have between 26.5 and 44 petabytes of potentially rich data from EHRs, including images and annotations.
There are a few scattered examples of adoption of Predictive Analytics in Health Care organizations, namely the University of Pittsburgh Medical Center (UPMC) and University of Tennessee Medical Center. UPMC started using Predictive Analytics to reduce readmission rate, but didn’t begin making progress until 2013. They eventually realized a 2% reduction thanks to Predictive Analytics.
One reason for slow adoption is that decision makers at hospitals are drowning in reports. Per Pamela Peele, Chief Analytics Officer Insurance Division at UPMC, “We’re awash in data. Every report generates five more report requests. We are raining information down on our decision makers, and when they look befuddled, we create more reports. After a while, you just can’t stand it anymore.”
It doesn’t help that today’s Electronic Health Record (EHR) systems come preloaded with thousands of reports. Too often they are mistaken for a viable Analytics solution. And EHR vendors are not helping by sending mixed messages about their product offerings, perhaps in a bid to corner the market.
Another common reason given: ‘Analytics is expensive…’. It is true that enterprise analytics can be expensive over the long haul. However, when done properly, the Return on Investment can be greater than the initial investment. And you don’t always have to start big. There is now a wide range of tools and technologies available to test analytics at a relatively low cost.
Trust in data, or lack thereof impedes progress as well. Lack of common standards and definitions around key needs derail many data projects before they even get started. Data strategies are not reflective of overall business strategy which leads to lack of trust, rendering any adoption of solutions slow and cumbersome.
However, with the right approach and partnership, targeted analytics applications can be built to be easily expandable to other subject areas or even to the enterprise.
There is never unlimited budget and timeline for a project, however communicating effectively to executives is the key to the success of predictive analytics in healthcare. Leaders may understand that reducing readmissions is important, but they may not get why a certain change in procedure is necessary just by looking over a report or slide deck.
To succeed, organizations need to create a healthcare analytics culture that merges information technology, information management, and information behaviors at every level across the enterprise. Optimum Healthcare IT has a dedicated Healthcare Analytics practice that has the expertise and know-how to help guide you through this journey. Download the brochure here and contact us today.
