In This Issue: Auto-Adjudication and Process Change

Issue summary and other notes...

Article 1 Process Change. Why are claims process improvements so difficult to make and the strategies to help make them.

Article 2 Auto-Adjudication (AA) Definition. Why are claims process improvements so difficult to make and what strategies are there to help make the process easier to manage?

CH Tip Sorts and Priorities. Sort your spreadsheet, it helps your audience better see what's important.

Notes

Welcome aboard. We’re proud to introduce our new Process Improvement for Healthcare Claims Operations Newsletter. There are hundreds of newsletters and information sources in the world of healthcare. None that we have found focus narrowly on claims operations process improvement. We aim to fill that gap. The purpose of this free quarterly newsletter is to provide you, the claims manager, with information about the decision support tools, industry best practices and case studies you need to support your process improvement efforts. We welcome your feedback. Either reply with an email (raykobs@datamethod.com) or a voicemail (781) 402-0006

80/20.80% of the decision support insight will come from 20% of the data. The question to answer is how little data can we get by with to support the five most important decisions we make - not what data do we need.

The Cathedral and the Bazaar In his article -- The Cathedral and the Bazaar -- Eric Raymond oulines the development of the operating system Linux. What makes Linux interesting (to me anyway), is not its technical attributes but they way this piece of software was designed and built. When I think of software development I think of subdividing larger tasks into smaller, more specific tasks, of organizing and carefully delegating those specific tasks. The Cathedral approach. Linux was built in a bazaar. Participants split out tasks as they felt appropriate. Anyone could participate. On any task. And how they approached that task was completely up to them. What emerged was an operating system which rivals (and is regarded by many to be superior to) Windows NT in terms of its robust features and performance. Maybe the 'bazaar' approach is is the future of process improvement?

Why Is Claims Process Improvement So Difficult?

The pitfalls and best practices associated with improving claims turnaround.

Claims processes are highly complex. Given the volume of information, the number of information sources and the number of steps involved, it’s a wonder claims are paid at all much less correctly. Health care organizations, from small groups of providers to national HMOs, are searching for ways to make improvements to their claims processes. These improvement efforts are focused on at least one of three things:

1. Reducing the cost to process a claim.
2. Reducing the time to process a claim.
3. Reducing other customer service issues such as claim errors.

Attempting to achieve one or more of these objectives requires the following:

1. A robust claims process decision support tool which minimizes IS resource requirements (to build or support), is easy for a business analyst to use (less than a week of training), and contains a manageable sample of relevant claims and process activity data.Reducing the cost to process a claim.
2. A process improvement approach driven by economics which focuses on removing work from a claims process (not on doing it faster), guides analysis and change efforts to parts of the claims process representing the greatest work (i.e. economic opportunity) and hence return, and provides leading indicators of future problem areas Reducing the time to process a claim.
3. A focussed long-term commitment process improvement which understands that process improvement is a marathon not a sprint, allocates appropriate business and IS resources to improvement efforts, and involves senior management in the prioritization and tracking of improvement initiatives.

It is no small challenge to get these three components in place. The limits of technology and other internal priorities often require tradeoffs. Using case studies, we will examine how payors have met these tradeoffs for each of the three components, beginning next quarter with ‘A robust claims process decision support tool’.

A Working Definition of Auto-Adjudication Rate (AA)

How best to define your AA Rate measurement to make sure it ties to efficiency but can be readily tracked and managed...

The percent of claims that process (pay or deny) automatically, commonly called the auto-adjudication rate (AA rate), is a key measure for any claims operation. In a nutshell, the AA rate is measured as follows: Total AA Claims / Total Claims. The trick is in how a payor defines both Total AA Claims and Total Claims. In order to arrive at a specific definition, a few process measurement requirements apply:

1. Auto-adjudication is an efficiency measure. It should be defined in such a manner as to focus attention on the level of efficiency (i.e. the amount of manual work) associated with manually processing claims.
2. The auto-adjudication rate should be easy to measure using existing systems.
3. Other parts of the claims process, such as data entry, should be measured separately.

A definition which meets these three requirements is as follows:

Notes and caveats associated with this definition include:

Total Finalized Claims are the claims that were completed (paid or denied) for that period. Total Finalized Claims is more representative as a denominator than Total Received Claims. Car manufacturers measure automobile production based on cars coming off the assembly line, not parts received. Measuring the AA rate using Total Received Claims introduces ambiguity around claims that are sent back to EDI clearinghouses or providers because they are incomplete. These data quality issues are more appropriately tracked as part of whatever measures support claims data capture, not auto-adjudication.

1. Measure auto-adjudication every week. Daily measurement is a level of precision not required. Monthly measurement is too long a period to pick up secondary trends. Claims operations run in weekly cycles. Measuring AA across a week provides a large enough window to minimize daily or hourly bumps and variations caused by system or other process problems, but provides a small enough window to highlight key trends and changes.
2. Total Finalized Claims are the claims that were completed (paid or denied) for that period. Total Finalized Claims is more representative as a denominator than Total Received Claims. Car manufacturers measure automobile production based on cars coming off the assembly line, not parts received. Measuring the AA rate using Total Received Claims introduces ambiguity around claims that are sent back to EDI clearinghouses or providers because they are incomplete. These data quality issues are more appropriately tracked as part of whatever measures support claims data capture, not auto-adjudication.
3. Total Paid or Denied Claims Processed Automatically (a mouthful) are auto-adjudicated claims that, once entered (electronic or manually-keyed) into the system, pay or deny without being pended, blocked, or manually edited. An auto-adjudicated claim is a claim which adjudicates without being touched by an examiner (other than data entry).
4. Claims adjustments (i.e. claims which are paid, either manually or auto-adjudicated, and then later adjusted because a provider objects) do not impact the AA Rate. Adjustments are a separate problem. Measure them separately.

How were the case study payers using these tools to improve AA or reduce TAT? One of them was literally printing huge stacks of green bi-fold output and manually tabulating in order to perform root cause analysis. Another created a few new custom reports, which largely highlighted the need for more information. Rather than endlessly create new reports, management elected to focus on claims process improvement projects other than AA and TAT. The final case study payer had determined that a new database was needed but was mired in the process of developing an agreed upon specification for the database (no one could agree how much data was needed). It is also illuminating to review how each of the three had approached changing referral rules as a means to increase AA. Two of the three had abandoned hope of understanding the nature of their referral process. One had actually gone as far as to create a new report listing the claim numbers of all claims pended for the lack of a required referral for a particular week. This report was then run and printed. An analyst then went online and looked up each claim to determine how many were later paid.

CH Tip: Sorts

Sorts bring the relevant to the top. Using sorts helps make your point...

How many times have you reviewed a spreadsheet where the relevant column or row was somewhere in the middle. Put your critical columns at the left and rows and the top. Spreadsheets make it easy to gather numbers, sorting them appropriately makes it easier for us to see understand them.