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Fixing readmissions with analytics

Carolinas Healthcare System reduced the COPD readmission rate from 21 to 14 percent

The challenge for the Carolinas Healthcare System was to reduce the readmission rate for patients with chronic obstructive pulmonary disease. The solution: predictive analytics.

One of the biggest problems, according to Jean Wright, chief innovation officer at CHS, was in identifying patients at risk of readmitting before they leave the hospital and enable care providers to intervene.

Faced with CMS readmission penalties the healthcare system had to take swift action.

[See also: Readmissions solutions sprouting up.]

"We knew at that point in time that COPD was not one of the conditions that was currently being measured and penalized under the CMS system but we knew it was on the agenda for 2013 and 2014," said Wright.

CHS selected San Juan Capistrano, Calif.-based Predixion Software. Predixion utilizes Machine Learning Semantic Model that enables predictive applications to be quickly implemented into production environments, making the predictive process, according to the company, easily adaptable and repeatable.

Nish Hartman, director of healthcare at Predixion Software said that preventable hospital readmission costs $25 billion in wasteful spending.

"What makes prediction impactful, said Hartman, "is what we call the last mile. This is serving up the risk scores in interventions in a very clear and simple-to-use interface. This is where the true value of your data is realized at the point of care with actionable insights delivered in real time that can be acted upon immediately."

An action plan was implemented and CHS rolled out a model in 2013.

 

 

 

Wright told participants at a webinar titled: Bringing Predictive Analytics to the Point of Care, on Sept. 10, that initial implementation was completed in six months and included seven hospitals. The solution is now fully deployed at 13 of CHS's metro-based hospitals where 100,000 plus patients have been managed with clinicians knowing their 30-day risk for readmission.

[See also: Readmissions penalties may be unfair.]

Wright said that in a year and a half to a two-year period CHS was able to drop the readmission rate from 21 percent to 14 percent.
 CHS took a multidisciplinary approach in attacking the problem of readmissions and a key part of the project team included nurses. "We knew nursing was a critical element. I know how important nursing is particularly when it comes to transition of care, discharge planning and case management," said Wright.

Wright explained that as they built the model they decided to segment patients into four groups: very high, high, medium and low risk.

The Predixion tool allows case managers to review patient lists that contain increased risk indicators and decreased risk indicators that allows them to see specific variables that caused someone's risk to either up or down.

For example, what caused a patient to have left hemispheric stroke? "If one of these variables had to do with her anemia or her electrolytes if I kept her another day or so and improved that clinical condition, I could in that moment of time, measure her risk of readmission and see if it would impact change. So this is real decision making using live clinical information at the point of care," said Wright.

Wright said that by utilizing predictive analytics tools, you could make a decision and use an intervention that the team chose based on their knowledge of the evidence and then create a permanent trail as to which intervention you have applied to a specific patient.

"About 80 percent of the time when we work off the model, we are right smack on target. If we say that someone has a high risk of readmission the patient is probably going to be readmitted and if we say it's very unlikely, then they're probably not going to be readmitted," said Wright.

Among the benefits of using predictive modeling:

  • Improved productivity of nurses and case managers
  • A reliable method of working the list of patients, instead of basing rounds on room number, time of discharge or other information
  • Case managers can now be deployed based upon the complexity of the patients and their likelihood of readmission
  • The ability to better manage work loads across floors and units
  • Identifying the interventions also provides the back up information needed for transition of-care documentation

Why predictive analytics?

Predictive analytics has been gaining ground over the past several years. Simply put, predictive analytics uses a variety of statistical techniques including: modeling, machine learning, and data mining, that analyzes current and historical facts in order to make predictions about the future.

In the healthcare setting predictive analytics is effective in addressing issues such as: length of stay management, preventable readmissions, and hospital acquired infections.

According to the Gartner Group, organizations that use predictive business metrics will increase their profitability by 20 percent by 2017. Nevertheless, predictive analytics can also be challenging. The Aberdeen Group reports that predictive analytics can be difficult to operationalize because it can be complex and time consuming.

In addition, there is a shortage of data scientists. "Business can't see past these challenges to how predictive analytics will improve daily outcomes," the report stated. The Aberdeen Group also cites barriers to adoption of predictive analytics such as: lack of understanding of the benefits, lack of appropriate skills, and difficulty in quantifying ROI.

However, some industry observers believe that predictive analytics will continue to play a major role in the healthcare sector.

Peter Horner, the editor of Analytics Magazine, said in a 2012 article: "Skyrocketing costs have rendered the current U.S. healthcare system 'unsustainable,' market forces are calling for a performance-based system, analytics are crucial to this paradigm shift from 'volume' to 'value', and the transformation is inevitable."

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