Game-Changing Dynamic Insight for Informed Decisions
Leverage big data analytics to enhance business outcomes with advanced case management
Content sponsored by IBM Enterprise Content Management
Technology is playing an increasingly dominant role in every aspect of today’s society. The effects of the digital and mobile revolutions are evident everywhere, but there’s one thing that may be an even more impressive game-changer—big data. Of all the data in the world today, 90 percent has been created in the last two to three years.1 This data comes from a range of sources, including sensors that gather climate information; comments made through social media channels; pictures, audio, and videos posted online; transaction records of online purchases; and even cell phone Global Positioning System (GPS) signals. Clearly, big data is transforming the world, and it’s especially changing the way in which organizations conduct business.
By analyzing big data, individuals and organizations can make decisions based on facts rather than intuition or personal experience. When decision makers and knowledge workers have access to increasingly accurate and up-to-date information, along with the analytical tools to make sense of it, their organizations benefit from more informed decisions and more consistent outcomes than ever before. The results of these insightful decisions and outcomes can have a tremendous impact on the way organizations work—and on how knowledge workers do their jobs.
During the last century, IBM has taken a leading-edge role in using relational databases to collect and manage big data, which it also did for documents and enterprise content management (ECM). Now, IBM is combining these technologies with business intelligence (BI) to create a new type of application that enables knowledge workers to research, analyze, and effectively initiate decisions and manage outcomes.
Advanced case management
Big data applications help marketers and other professionals understand customers and other factors to deliver the right message to the right audience at the right time. For example, they can analyze social media data along with customer buying data, and process real-time mobile call data to predict behavior and prevent churn. In another scenario, healthcare professionals can analyze information from electronic health records to expose the early signs of an epidemic.
IBM® Advanced Case Management solutions make it possible to link key information about people, processes, and information into a single view to facilitate decision making. Organizations can optimize outcomes by using analytical tools such as similarity analytics that analyze one individual or circumstance in the context of all the others like them to promote consistent decisions. These options are based on aggregate data points rather than on experience or biases. By augmenting analytics based on structured information—BI—with analysis of unstructured or documentary information—using common language processing—organizations can provide consistent outcomes for a variety of knowledge workers spanning multiple geographies.
Informed decision making in a healthcare setting
Patient management is a prime example of an area in which big data and advanced analytics can empower knowledge workers. A large hospital in the US, for example, has used both predictive and content analytics in concert with IBM Case Manager to help it comply with the Affordable Health Care for America Act, specifically for managing patients readmitted to hospitals for congestive heart failure.
Historically, the hospital used data collected from electronic medical record systems to provide indicators—from a rather unmanageable pool of 113 indicators—for high-risk patients. However, when the hospital included documentary evidence from notes made by doctors, nurses, and other sources such as intake information, a different story emerged. Unstructured information, based on multiple observations, tends to be more accurate than structured information that has only one source—the patient's intake form. Patients tend to be less than truthful, for example, when asked questions about behaviors such as smoking or drug use. Unstructured information, therefore, offers a much higher statistical accuracy rate than the structured information, helping reduce the key variables down to only 18 indicators.
Leveraging this information in a case management infrastructure allowed the hospital to provide this insight directly to medical caseworkers in this scenario. And it enabled those caseworkers to be more proactive in their interactions with patients and help dramatically reduce readmission rates.
IBM expanded this analytical model even further by providing a layer of similarity analytics on top of the predictive analytics (see figure). By looking at a particular patient and making a fact-based comparison of how patients with the same conditions recovered through different medication regimens or treatment procedures, health professionals gained valuable decision-support tools to help them make highly well-informed decisions.
Using similarity analytics to elevate informed decision making
Enhanced insight from big data and analytics
Analytic tools extract valuable information from big data. They help broaden the context and background available for making informed, accurate, and consistent decisions. By merging analytical tools, flexible process management, and access to all required information into a single application, organizations can use big data to help improve the way they do business.
* “Big data, for better or worse: 90% of world’s data generated over last two years,” Science News, ScienceDaily, May 2013.