Big data is not new. Organizations have been working with large data sets for many years, converting them into actionable information that yields practical insight into their customers’ buying habits, their business strategies, and their achievements. What is unprecedented, however, is the volume and velocity of data now being produced in healthcare due to the digitization of administrative, clinical and financial information in electronic health records (EHRs), billing and other IT systems.

In a matter of years, the healthcare industry has changed from measuring the size of data in terabytes and petabytes to exabytes and even zettabytes—and this volume of data is doubling every year. To put this into perspective, the Institute for Health Technology Transformation reports that the California-based Kaiser Permanente health network is estimated to manage between 26 and 44 petabytes of data from its EHRs alone.  That’s enough to fill 4,400 Libraries of Congress.

While big data has revolutionized other industries such as retail, hospitality and banking, its value has yet to be fully realized in healthcare, where information remains spread across disparate clinical, financial and administrative systems. Varied data sources and formats have made it extremely difficult for healthcare organizations to reliably gather and share information—let alone trust in its quality. At the same time, however, the need for accessible, connected, accurate and actionable data is becoming increasingly important.

Through effective data management, healthcare organizations can begin to harness information to create real-time actionable insights and set their future vision. The McKinsey Global Institute estimates that, if properly leveraged, data can unlock more than $300 billion annually in additional value across the U.S. healthcare segment. That value spans a wide range, from improved revenue cycle support to enhanced patient satisfaction and clinical outcomes.

Best practices in data management

The wealth of healthcare data now available presents both risk and opportunity, as provider organizations are challenged to distinguish essential information from clutter. The following best practices can make a significant difference in successful data management:

1. Implement a data governance strategy early on. Enterprise analytics should start not with technology, but with a master data management strategy. Many healthcare organizations make the mistake of thinking that they need massive amounts of data before they can realize any benefit from it. But more is not necessarily better; after all, in addition to figuring out how to get at it, there is still the question of what to do with it once it is obtained. The key is not to seize every last bit of information, but to capture the right information. Implementing a data governance strategy early on helps healthcare organizations answer two crucial questions: What information is needed; and why is it necessary? A good strategy should begin by defining the issues the data is intended to address. That way, the organization can better focus on the data that needs to be collected and the information processes that need to be managed in order to achieve the desired outcomes.
2. Establish a centralized repository. The fragmentation of data in healthcare remains a significant obstacle to overcome. A hospital or health system needs the ability to get at data from numerous systems and origins—payers, providers, patients—and put it into a usable format. A centralized repository breaks down traditional silos by housing all data in one place, but be wary: simply having a data warehouse is not enough. To be effective, it must be built on a relational database that is capable of pulling information from multiple disparate systems, then quickly and easily integrating, sorting and analyzing the data.
3. Make data accessible. The days of asking a question and waiting for data scientists to provide the answer at a date and in a manner convenient to them are over. Not only is the process time-consuming, but queries seldom are repeatable enough or timely enough to generate truly actionable results. C-suite executives need real-time information that is easily accessible and the ability to retrieve relevant information in their preferred format—whether that happens to be through a high-level dashboard view or via a mobile device, for instance.
4. Close the quality gap. The quality of data drives the quality of predictive results. In the healthcare industry’s rush to adopt EHRs and maintain compliance, however, Chilmark Research reports that little oversight has gone into ensuring that the information entered into those medical records is accurate. Untrustworthy data can undermine an organization’s ability to leverage analytics. To ensure data is clean, it must be interrogated for accuracy, consistency, thoroughness and timeliness. One way to identify bad data is through the use of cleansing tools, such as algorithms that eliminate duplications or flag mismatched procedural and diagnosis codes, for example. In addition to technology solutions, healthcare organizations must also engage providers and staff in quality improvement initiatives designed to enhance the accuracy of data collection efforts on the front end.
5. Implement a business intelligence solution. Variety is yet another obstacle to managing data. Case-in-point: As much as 80 percent of healthcare data is unstructured, whether it’s contained in paper format, medical images or free-form fields that need to be manually abstracted. While this content can be a valuable source of information, not all of the information generated is vital. That is why enterprise analytics capabilities are so crucial. By funneling information into business intelligence solutions that analyze and report on the data, healthcare providers can make better administrative, clinical and financial decisions that help improve patient care, meet Meaningful Use standards, address ICD-10 and other regulatory changes, and create a positive impact on reimbursement.

From insight to action

The true value of effective data management comes from the ability to turn insight into action. A real-time snapshot of financial, operational and clinical performance through access to enterprise-wide information enables healthcare organizations to conduct in-depth root cause analyses and build successful processes around areas of opportunity. Although the rapidly expanding variety, volume and velocity of data can be a challenge, sound data management practices help healthcare organizations make the kind of informed, high-impact business decisions required for survival in an increasingly competitive and complex marketplace.