Advanced Analytics … The Next Big Thing in Healthcare
Updated: Feb 19, 2022
If you are in the healthcare industry, you know you are facing a number of significant challenges. First and foremost, you are being asked to meet rising expectations for higher-quality care, better outcomes and lower costs. But at the same time, you face a critical shortage of resources and an aging population that will require a greater portion of those limited resources every day.
Chronic diseases present some of the toughest challenges. Approximately 45 percent of adults in the United States have at least one chronic illness. Those chronic illnesses not only make life difficult for patients, but they also stretch healthcare resources thin and cost the U.S. economy more than $1 trillion annually.
How does Advanced Analytics work?
Advanced analytics can give you an edge in balancing all of these demands, and in figuring out how to continue the balancing act as the industry evolves. With advanced analytics, you can leverage a broader range of patient information and surface early, targeted intervention opportunities that ultimately help you enhance the quality of care, improve outcomes and reduce costs.
Content Analytics capabilities, such as those offered through IBM Content and Predictive Analytics for Healthcare, can help you analyze a wider range of patient information than you could before. In the past, analytics solutions were frequently limited to structured data—such as the data found in electronic medical record (EMR) and claims systems.
But content analytics lets you incorporate unstructured sources as well, including doctors’ dictated notes, discharge orders, radiology reports, faxes and more. Powerful natural language processing is at work to enable this.
To see how valuable unstructured information can be in uncovering insights, read my previous blog post, “Playing the Healthcare Analytics Shell Game.”
Predictive analysis capabilities can help you identify patients at risk for developing additional illnesses or requiring further interventions. You can use predictive modeling, trending and scoring to anticipate patient outcomes and evaluate the potential effects of new interventions.
Using patient similarity analytics capabilities, such as those developed by IBM Research, a provider could examine thousands of patient attributes at once. That includes not only clinical attributes but also demographic, social and financial ones. By assessing similarities of attributes in a broad patient population, providers can better anticipate disease onset, compare treatment effectiveness and develop more targeted healthcare plans.
Surface New Intervention Opportunities
The insights you gain from these analytics capabilities are the keys to discovering opportunities for new, individualized and highly targeted patient interventions—interventions that can reduce expensive hospital readmissions for chronic patients, avoid the onset of other illnesses, prevent postoperative infections, slow the deterioration of conditions and more.
That all adds up to better care and better outcomes at a lower cost.
In future posts, I’ll present a more in-depth discussion of patient similarity analytics and examine how advanced analytics can be integrated with care management.
In the meantime, I’d be eager to read your comments and questions.
 S.Y. Wu, A. Green, “Projection of chronic illness prevalence and cost inflation,” RAND Health, 2000.
 Milken Institute, “An Unhealthy America: The Economic Burden of Chronic Disease Charting a New Course to Save Lives and Increase Productivity and Economic Growth,” October 2007, http://www.milkeninstitute.org/healthreform/pdf/AnUnhealthyAmericaExecSumm.pdf.