Advanced Analytics … The Next Big Thing in Healthcare

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.[1] Those chronic illnesses not only make life difficult for patients, they also stretch healthcare resources thin and cost the U.S. economy more than $1 trillion annually.[2]

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

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 that unstructured information can be in uncovering insights, read my previous blog post, “Playing the Healthcare Analytics Shell Game.”

Predictive Analytics

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. 

Similarity Analytics

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 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.  In the mean time, check out some of the analytics research currently underway at IBM Research,

[1] S.Y. Wu, A. Green, “Projection of chronic illness prevalence and cost inflation,” RAND Health, 2000.

[2] 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,

6 thoughts on “Advanced Analytics … The Next Big Thing in Healthcare

  1. May years ago another ECM company promoted a visual analytcs system for healthcare. It was based on something the called KEGS or Knowledge Enhanced Graphics. It was all very flashy and interesting but flawed. I will be at IOD next week, I look forward to catching up with you, it’s been three years and seeing if you guys caught the flaws.

  2. Pingback: Advanced Analytics, the Next Big Thing in Healthcare | i3 Business Solutions

  3. Nice post, Craig. There’s also a lot of analytics that can be applied to healthcare operations. For example, estimating surgical throughput and operating room utilization, based on the surgeon, mix of procedures, and patient characteristics. This easily beats the current practice built into most surgical systems, which usually is just an average of the last few cases. There’s lots of opportunity out there!

    Jason Goto

  4. As the data maturity model purposefully migrates toward meaningful analytics, three distinct requirements exist: Population management, utilization analysis, and care coordination — correlated to the administrative, care management/quality, and direct treatment strata within healthcare organizations. What cannot be lost is the need for cross-community analytics that provide trending beyond primary care. The best management of resources and care delivery will require amalgamation and analysis combing predictive models from non-traditional healthcare verticals, such as behavioral health, dental, substance use disorder treatment, public health, and non-traditional medicine.

  5. As a Pediatric Dentist we face some unique challenges different than the ones you outlined, it is interesting that the solutions are the same. The better we get at integrating Analytics information in the treatment of our patients the better we will become at anticipating problems rather than reacting to them.

  6. Pingback: Healthcare Data is the New Oil: Delivering Smarter Care with Advanced Analytics | Craig Rhinehart's ECM Insights

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