Irreverent Revenue Cycle No. 4: Natural language advantages in revenue cycle management

Podcast

Irreverent Revenue Cycle No. 4: Natural language advantages in revenue cycle management

Jun 22, 2021·Authored by Michael L. Duke

Using automated intelligence to improve performance

Welcome to Irreverent Revenue Cycle, a Healthy Outcomes podcast series from Baker Tilly, where we discuss hot topics around revenue cycle performance in the healthcare industry, innovative ways to solve issues for your organization, and practical tips to take with you as you listen.

On this episode of Irreverent Revenue Cycle, we discuss natural language generation (NLG) software and how utilizing tools such as Automated Insights by Wordsmith can help healthcare provider organizations make actionable decisions to improve their revenue cycle performance. Topics addressed in the episode include:

Automated Insights (NLG) software incorporated with Tableau Software to drive revenue cycle analysis
Utilizing analytics to make actionable insights to address negative performance
How to leverage data to create efficiencies

Connect with us to hear more about our revenue cycle optimization team.

Irreverent Revenue Cycle Host

Michael L. Duke
Michael has diverse experience and a record of accomplishment in leading people, programs, improving operational outcomes and developing best management practices within the healthcare industry. He has led several successful engagements that demonstrated an ability to develop and implement executive level strategies. Recognized for professionalism, commitment to excellence, and ability to communicate and work with senior management, associates, and customers. He has proven expertise in the areas of: operations management, corporate performance management and revenue cycle management.

Our guests

Kenton Perez, Account Executive, Automated Insights

Nick Nelli, Senior Solutions Architect, Automated Insights

Automated Insights is a technology company that specializes in natural language generation (NLG) software that transforms big data into a readable, action-oriented format.