How Cleveland Clinic Transformed Revenue Cycle Analytics and Saved Millions November 14, 2018 Bob Gross, MBA, CPHIMS Director, Revenue Cycle Analytics

Agenda What is the “Revenue Cycle” Revenue Cycle as an analytical use case Buy Versus Build Analytics as a core competency The importance of cohesive teams

What is the “Revenue Cycle” “All administrative and clinical functions that contribute to the capture, management, and collection of patient service revenue.” Healthcare Financial Management Association (HFMA) In other words, the Revenue Cycle reflects the entire life of a patient account from creation to payment. “What is the Revenue Cycle”: http://www.ohsu.edu/xd/about/services/patient-business-services/revenue-cycle/ Revenue Cycle Diagram: http://www.marianowo.org/revenue-cycle-management-process-ppt-12-things-you-didnt-know-about-revenue-cycle-management-process-ppt-4197

What is the “Revenue Cycle” Characteristics of Revenue Cycle Data Voluminous - Each step in the Revenue Cycle creates data “exhaust” that can be harvested and converted into actionable information Complex - Trends and root cause analysis can be elusive due to the nuanced nature of the data Structured – Fortunately, much of the data in the Revenue Cycle has been codified and structured “What is the Revenue Cycle”: http://www.ohsu.edu/xd/about/services/patient-business-services/revenue-cycle/ Revenue Cycle Diagram: http://www.marianowo.org/revenue-cycle-management-process-ppt-12-things-you-didnt-know-about-revenue-cycle-management-process-ppt-4197

Revenue Cycle as an Analytics Use Case Cleveland Clinic began to experience challenging conditions in the Revenue Cycle in 2014, including Insurance Denials Increasing Patient Responsibility Resource constraints Advanced analytical capabilities were seen as a way to understand and manage these challenges A vision of a Revenue Cycle driven by advanced analytics was embraced.

Revenue Cycle as an Analytics Use Case Gartner Analytics Maturity Model In 2014, Revenue Cycle Analytics at Cleveland Clinic were highly descriptive, often focusing on report generation Our new vision would drive us higher on the analytical maturity scale Shift focus towards root cause analysis to understand the “why" Predictive analytics could be developed to Prevent insurance denials Improve patient collections Prioritize our workforce on most valuable patient accounts 2014 Gartner Analytics Maturity Model: https://www.zdnet.com/article/data-to-analytics-to-ai-from-descriptive-to-predictive-analytics/

Buy Versus Build A Software as a Service provider was selected in mid 2014 to implement an analytics platform capable of achieving the vision Software as a Service (SaaS) Experience Some of the quick wins with the SaaS analytics provider were Ingesting and visualizing patient accounting data Developing analytical views on insurance claims data However, it became clear that in order to develop advanced analytical capabilities, the SaaS arrangement would require much more investment and customization As more investment in time and capital was made, we began to ask the question “Should we build this ourselves?”

Revenue Cycle as an Analytics Use Case Gartner Analytics Maturity Model In 2016, Revenue Cycle Analytics at Cleveland Clinic were revealing root cause trends in denials and payment delays Although detailed analysis was now possible, engraining those learnings in our operations required manual intervention Predictive analytics would require large investments with the SaaS vendor 2016 2014 Gartner Analytics Maturity Model: https://www.zdnet.com/article/data-to-analytics-to-ai-from-descriptive-to-predictive-analytics/

Buy Versus Build In 2016, Cleveland Clinic made a substantial investment in technologies that could enable the development of an in-house Revenue Cycle Analytics Platform. Making the “pitch” to leadership An in-depth assessment of resource requirements and timing was conducted Feasibility studies were also performed – “Can we actually accomplish this?” We proposed creating a partnership between our Enterprise Analytics and Revenue Cycle Management teams to build a replacement to the SaaS platform over 18 months. An investment in software developers, quantitative analysts, and business analysts was made to support the project. Net of the personnel investment, the project would achieve a $1.8M annual ROI upon completion.

Buy Versus Build We successfully completed the implementation of our Revenue Cycle Analytics platform in June, 2018, enabling us to eliminate the need for SaaS based analytics. Analytical Capabilities Achieved With the implementation of the in-house developed solution, the following capabilities now exist Data Modeling – New data sources can be ingested using an enterprise standard “data vault” methodology Visualization –Dashboards and drill down analytics can be developed and published enterprise wide Machine Learning – Predictive models are being developed to predict denials prior to billing claims, and scoring patients for propensity to pay. All capabilities are unified with consistent tools and connectivity

Revenue Cycle as an Analytics Use Case Gartner Analytics Maturity Model Predictive and Prescriptive Analytics are now capabilities of Revenue Cycle at Cleveland Clinic Models are being deployed to predict denials and calculate patient propensity to pay Our predictive models are connected to our patient billing system (Epic) to drive workflows and actions, completing the analytics cycle 2016 2014 2018 Gartner Analytics Maturity Model: https://www.zdnet.com/article/data-to-analytics-to-ai-from-descriptive-to-predictive-analytics/

Analytics as a Core Competency Advanced Analytics are now viewed as a core competency within Revenue Cycle Management at Cleveland Clinic Building our platform in-house enabled us to transform our Revenue Cycle by creating and deploying predictive and prescriptive analytics Advanced analytical capabilities give us a competitive advantage in managing insurance denials and facilitating patient payments Using analytics is good, but competing on analytics is better - Thomas Davenport

The importance of Cohesive Teams Building a cross-functional and matrixed team is challenging A foundation of vulnerable trust enables teams to embrace constructive conflict, commit to decisions, hold each other accountable, and focus on results

Summary The Revenue Cycle covers the entire life of a patient account, providing rich data “exhaust” to harvest for analytics Advanced analytics can play a major role in solving today’s Revenue Cycle challenges Trade-offs exist between buying and building analytical solutions Advanced analytics can provide a competitive advantage Cohesive teams enable organizations to achieve results

Contact Information Bob Gross, MBA, CPHIMS Director, Revenue Cycle Analytics Cleveland Clinic https://www.linkedin.com/in/bob-gross-46809486/ grossb@ccf.org