Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
SHARE THIS:

9 years ago
Predictive Analytics Can Help Solve Hospital Problems

 

This excerpt is from Healthcare Finance News. To view the whole article click here.

Video: See Katrina Belt’s session from Predictive Analytics World Health Healthcare in Boston from Oct. 2014. (free registration required!).

Experts say analytics gives them the data to reshape their healthcare environments in the transition from fee-for-service to value-based care.

Health systems entering the world of predictive analytics should start small before leaning so heavily on big data, said Katrina Belt, chief financial officer of Baptist Health in Alabama.

“For us, we needed to ease in,” Belt said. They started with looking at claims, she said. After that, they started analyzing clinical data to help them reduce catheter-associated infections.

“We use analytics to tell us where to begin,” Belt said. “It’s gives us an opportunity for us to address risk.”

Belt and Linda Burt, chief financial officer of the Nebraska Methodist Health System, shared their perspectives during a Healthcare Finance webinar on predictive analytics Monday.

Burt said a program started at Nebraska Methodist Health System in 2010 helps them to be data-centric in per unit cost before going down a path of cost reductions.

“It allows us to be more confident that we’re looking at things properly,” she said.

Both CFOs said analytics gives them the data to reshape their healthcare environments in the transition from fee-for-service to value-based care reimbursement.

Belt said Baptist Health started by taking a look at the self-pay population. They learned emergency room visits were decreasing but the number of patients coming back with acute care conditions was increasing in intensive care.

From demographic information, they learned the self-pay population was split equally among men and women with their ages falling mostly between 18 and 26 years old.

“They were going to continue to be our patients,” Belt said. “They went away with doctors’ orders they threw away and prescriptions they couldn’t afford to fill.”

Baptist Health addressed the issue starting with incentives to reduce bad debt and putting a program in place in which the patients agreed to be compliant with their care if the provider helped them pay the cost of prescriptions.

“We began to understand who our patients were,” Belt said.

Burt said at Nebraska Methodist Health System, data opened their eyes to the discrepancy in post-acute care in their three-hospital system. One was a heavy discharger to skilled facilities and the other two were not, she said.

Through application to one of the Medicare bundled payment models, they also learned the amount of skilled care at the one facility was much higher than other systems in the region.


This excerpt is from Healthcare Finance News. To view the whole article click here.

See Katrina Belt’s session from Predictive Analytics World Health Healthcare in Boston (Oct. 2014.). Check out PAW Healthcare 2015 to see more case studies and sessions on analytics and healthcare.

Leave a Reply