Big data is transforming data across industries at an unprecedented rate. Some of the firms at the forefront of data transformation are – Google, Facebook, Amazon, eBay, Wal-Mart, Netflix, Uber and Airbnb. These firms are transforming their businesses at a rapid rate by leveraging analytics and data to gain insights into their customers, rapidly iterating scenarios, transforming their markets and introducing new products and services.
Big data analytics examines large amounts of data to uncover correlations, hidden patterns and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately – an effort that's slower and less efficient with more traditional business intelligence solutions.
Healthcare is hamstrung by its use of legacy systems, inability to integrate, entrenched cultures and outdated skill sets that make the move to the use of big data and analytics a challenge. While skills such as Hadoop, R, Python or statistical modeling can be tough to find in the current market, culture seems to be holding the healthcare market back the most. Cultural change is hard, requiring business process change, communication across the entire organization, and realignment of thought. Healthcare must start by asking the critical questions that will drive change, add value, save lives and control cost. Technology is a critical issue that will usually follow. In my work, we often ask, “What are you trying to solve?” as the starting point of finding solutions. Many healthcare companies already have much of the data needed to develop a big data strategy, but they are struggling with how to integrate all their various systems into big data to begin analysis.
While creating a starting point is important, those healthcare organizations who have been able to change the cultural climate and create a data strategy are on their way to truly innovating in the changing market place. The advancement of rules-based analytic solutions offer an improvement in healthcare but machine learning has the ability to improve the accuracy of the predictions without readjusting the rules. “With Machine Learning, one also has the ability to measure effectiveness and improve it by only changing algorithms or algorithm parameters with science. It's far easier to iterate and get good prediction results than getting it right with a rules based system.” – Karthik Guruswamy Principal Data Scientist & Consultant at Teradata.
What if your organization had the ability to know which patients were likely to readmit or your costliest diagnosis? What if your oncologist could literally diagnose cancer more accurately by looking at 15 indicators as opposed to 20,000? Knowing this type of information in advance, allows healthcare providers to provide treatment earlier resulting in a more positive outcome for patients and reducing cost. These capabilities exist and healthcare is ripe to take advantage of the advances in this space.
While robots and computers will never completely replace doctors and nurses, machine learning and AI are transforming healthcare in positive ways. It all starts with a culture change, a question you want to answer and Big Data. These are no small tasks for any industry, especially healthcare, but the future is now and exciting changes are on the landscape for Big Data and Analytics within healthcare. Is your healthcare organization ready?