Data Visualization & Analytics. Implementing effective operational analytics throughout revenue cycle operations

Transcription

1 Data Visualization & Analytics Implementing effective operational analytics throughout revenue cycle operations 1

2 Agenda Learning Objectives Introduction to Process Analytics Compare Process Analytics to other techniques Discussion of process level data sources Example use cases for Process Analytics Summary Q&A 2

3 Introductions Michael Duke is a Principal in Baker Tilly s Healthcare Consulting Group. He has over 25 years of healthcare consulting and management experience. Mike has consulted on numerous engagements for academic medical centers, large medical centers, community hospitals and large physician groups for clients in 22 states. His areas of expertise include end-to-end revenue cycle management and operational performance improvement, health care information technology management and business intelligence implementation. 3

4 Learning Objectives The Key points from today s presentation will cover: 1 Understand the basics of Process Analytics visualization techniques 2 Learn how Process Analytics are different than other approaches 3 Learn how Process Analytics can be used to improve revenue cycle performance 4

5 Introduction to Process Analytics 5

6 Process Analytics Defined Process Analytics is the use of data visualizations, packaged in specific ways, to review, evaluate and control essential workflows, activities and performance for the purpose of making timely and effective management decisions. Examples include: 1 Track insurance eligibility errors by user 3 Review trends with claim translation errors by source FTE (e.g. registration clerk) 2 Evaluate authorization denials by location, physician and staff 4 Monitor staff efficiencies as well as effectiveness at the individual level 6

7 So What Are We Really Talking About? Process Analytics Splunk.com defines it as: enabling organizations to gain real-time, end-toend visibility into complex business processes The Automation Association describes it as: Analysis of a process to develop a statistical based understanding, leading to process improvements and/or optimization Not Process Analytics Executive dashboard representing static Key Performance Indicators (KPIs) A set of standard or adhoc reports developed in any of the various host / ancillary systems A set of Excel spreadsheets that get updated and ed around to various users HFMA MAP Keys for comparative monitoring 7

8 Other Important Definitions Process Analytic Data Visualization: The graphical representation of data that typically leads to faster decision making and trend identification Guided Process Discovery: Analytics that guide the end-user through prepackaged visuals that link together to measure specific outcomes. This enables all users (irrespective of operational background) to leverage experienced based logic, real-time information, and leading practices in their decision making. Unguided Process Discovery: The ability to access data about individual instances of a metric and use that data to monitor, analyze, and improve an organization s critical operational processes 8

9 Comparing Process Analytics to Other Techniques 9

10 Traditional Analytic Concepts Scorecards, Dashboards and Reports Scorecards Primarily used to help align operational execution with business strategy Dashboards Less focused on a strategic objective and more tied to specific operational goals By the nature of their design, these traditional approaches are very static in nature Reports Reports are best used when the user needs to look at raw data in an easy to read format 10

11 Traditional Analytic Strengths and Weaknesses Strengths Allows for quicker decisions than data in a table format Utilizes basic visual presentation of data Typically automatically updated with no need to request data Weaknesses Very little drill down capability to explore granular issues Generally not updated in near time Static in nature No real capability to explore the data 11

12 Traditional Analytic Examples 12

13 Process Analytic Concepts Fast Analysis Analytic tools store data in an in-memory database or cache to avoid the performance issues Not Reporting Analytic tools are designed for visual data exploration, analysis and lightweight data mining Connectivity Supports connections between all/most available data sources 13

14 Process Analytic Strengths and Weaknesses Strengths Allows for drill down capabilities Standard metrics with high visual recognition features Linked to transactional data from various sources Able to pinpoint location/user associated with performance Weaknesses Usually developed by IT resources mirroring industry KPIs Continues to rely on users experience to understand outcomes Usually doesn t contain the right data as the data is designed without the end in mind 14

15 Process Analytic Examples 15

16 Guided Discovery Concepts An important component to process analysis There is a philosophy in teaching, particularly younger students, called Guided Discovery Learning. The teacher makes sure that the students are guided to their discoveries by using leading questions and probes. There is an approach to data analysis that is called Guided Analytics. This approach is built on the premise that analytics should do more than reporting. It should guide the end-user through analyses to the right action. This enables all users (irrespective of analytical capabilities) to leverage experienced based logic, realtime information and best practices in their decisions. Where we want to go: the tools are currently available in the marketplace and we want to embed our expertise into the tools to fully exploit the market opportunity A fundamental component of management consulting / advisory services relates applying past experiences and leveraging those to improve the client s conditions.. 16

17 Discussion of data sources and insights 17

18 How Do We Generate New Insights? Internal sources A source is internal when the data is collected from systems within the organization: Electronic documents: Standard system reports from host revenue cycle system Ancillary system reports from bolt-on applications (e.g. Claims Scrubber) 837 claims submission Transactional data: Payment and adjustment transaction data Charge detail data System audit logs Workflow system transaction data External sources A source of data is external when the data is collected from a system outside the organization: Payer information: 835 payment information Payer web portal information Other external sources: Claim status website data Self pay credit scoring systems Aggregate sources such as payer claims data, etc. 18

19 Revenue Cycle Analytics A Process Centric Approach Revenue Cycle analytics approaches have promised a lot over the years, but has typically failed when it came time to deliver. Yes, analytics has improved, but we are really not much farther along than we were years ago with Key Performance Indicator spreadsheets. Here is what is need to finally realize true actionable information: Data Modeling Data modeling is critical to tell the story of process performance: Start with the end in mind Must be structured in such a way that allows for the understanding of process breakdowns Takes advantage of episodic data to appropriately link data Data Visualization Data visualization is the leading method for information recognition and actionable problem solving: Various alert level capabilities for lights out monitoring and early prevention Guided discovery concepts for efficient root cause analysis On the fly filtering / data view customization Embedded problem solving to improve user decisions

20 Example uses for Process Analytics 20

21 Information Available to Some Organizations Ok, so we know the payer issuing the denial And we can look at the Reason Grouping And we can even know the Remark Code sent by the payer Remark Code 158: Service/procedure was provided outside of the United States 21

22 More Information Available to Some Organizations But we can do better Here we have groupings based on Remark Category And now we are grouping by time period so we can at least compare trends Time period summaries by Remark Category are helpful to understand impact 22

23 Denial Analytics System Summary by Month 23

24 Denial Analytics Top Payer Drill Down 24

25 Denial Analytics Appeal Productivity vs. Effectiveness 25

26 Denial Analytics Appeal Productivity vs. Effectiveness 26

27 Denial Analytics Appeal Productivity vs. Effectiveness 27

28 Summary 28

29 Benefits of Process Analytics Improve staff productivity through understanding performance outcomes for similar job assignments Higher staff effectiveness through targeted education Provide meaningful management insight and control Granular, one version of the truth operations Ability to understand root cause and take appropriate actions Improve overall revenue cycle performance and financial outcomes 29

30 Q & A 30

31 Thank You! Contact information Michael Duke