D'Arcy Gue

ICD-10 Troubles? It’s Quality Improvement Time

October 8, 2015

ICD-10 5 Minute Read

As your hospital already may have discovered, October 1 was not the end of its ICD-10 conversion.

For many of you, the transition thus far is going relatively well. According to the Advisory Board, post-October 1 has been uneventful, because the hospitals it has worked with “have done so much preparation that they’re eager.” Their clients, some of the largest hospitals and health systems in the country, have spent millions preparing for as long as four years. But, actual claims payment trends won’t start being available for weeks, so no one should be sighing with relief quite yet.

In the meantime, as of Day 8,  we at Phoenix (and colleagues from other firms) are beginning to see smaller hospitals and healthcare providers that are in trouble.  For them,  it’s already time for the next phase of ICD-10 — quality improvement.

Thousands of smaller organizations have lagged in implementing systems upgrades and process changes, and had difficulty in training physicians.  In the last few days, we have learned of at least four hospitals, each with over 200 beds, that have conducted no physician training in ICD-10 oriented documentation, and other hospitals whose physicians still believe they can continue documenting as they have in the past. Lack of physician training or commitment is an automatic crisis creator, and these hospitals should go into an immediate catch-up mode.

We also have seen several organizations that already are experiencing errors, and related indications of likely revenue delays. ICD-10 operational processes, like all new processes, will have higher error rates until your teams become familiar with the ICD-10 data elements and what they mean.   These error rates will be most pronounced in the next few months. In order to drive quality improvement,  it will be critical to your organization’s work (and revenue) flow to provide frequent and robust data feedback to your affected departmental teams.

A primary tool for managing operational processes and quality improvement going forward will be actionable data.  The availability of this data will depend on how quickly first claims have been coded and readied for billing. There are three major types of data that will be especially significant in your quality improvement effort: inventory data, grouping data, and denial data.

Inventory Data

  • DNFC
  • DNFB (which usually includes DNFC that can be netted out)
  • Charts returned to the provider for clarification
  • Bills placed on hold
  • Claims held by the claim editor
  • Claims out to payer with no response
  • Unworked front end rejections inventory
  • ICD-10 code related rejections received
  • ICD-10 code related rejection inventory

Grouping Data

We’ve discussed the relationship of DRG issues and potential impacts to revenue extensively.   By examining the grouping of your submitted claims, you can identify areas of DRG shift, and then investigate them to determine the causes of the shift.   Don’t forget that the total number of claims may drop if there are coding backlogs, so it’s probably more useful to look at percentages.

It may be helpful to examine this claims grouping both as an overall percentage of DRGs, and also as a percentage of particular DRG triads and diads, since those are likely to be easy places to spot trends where documentation and or coding might present financial issues.

Example: The Major Chest Procedure triad

DRG % of Cases
ICD-9 ICD-10
163 -Major Chest Procedure with MCC 10 12
164 -Major Chest Procedure with CC 60 50
165 -Major Chest Procedure without MCC/CC 30 38

Remittance Data

One obvious way to identify ICD-10 related process failures and coding issues will be by analyzing rejections issued by your payers. Unfortunately, simply looking at the number of rejections presents a number of problems:

  • Rejections for multiple causes will be mixed together with the same rejection reason code. Rejections do not indicate root cause.
  • Patterns may exist at the payer level or across payers.
  • ICD-9 and ICD-10 payments and rejections will overlap in the billing system, obscuring patterns.

Despite these issues, such an analysis has value; just make sure you do not rely on rejections as your sole source of feedback.  Apparent patterns will need to be investigated, probably by a multi-disciplinary team such as the one we proposed in our recent post on setting up an ICD-10 ICD-10 War Room. 

Many providers with reasonably modern billing systems already will have access to a capable suite of rejection analysis and reporting tools.   Some providers’ older systems lack such tools, but those providers may have access to similar tools from a clearinghouse.   Another approach to your analysis would be to load all your claims and remittances into a stand-alone database, which can be used as a source for data and pattern analysis.

Whichever way you choose to access the data, the entire set of claims and rejections received at your facility is almost certainly too large to permit you to look at every item individually.   To make the most of your limited analysis time, consider the following:

  • Focus only on denials specifically related to ICD-10 diagnosis and procedure codes, e.g. “Procedure not appropriate for diagnosis.”   Demographic denials are not likely to hide significant ICD-10 problems.
  • Look at patterns of denials across payers (because documentation and coding should be fairly payer-independent). Look for the same within single payers as rejections may relate to a single payer’s business rules or mapping, and including multiple payers in your query may hide these single payer issues.
  • As above, a percentage of claims denied will be more useful in most cases than raw numbers, because the total number of claims may vary.
  • Don’t forget to provide a clear way for your insurance collectors to refer specific items to you for further research. One example may represent a single claim, or it might be the tip of a much larger iceberg.

Finally, make sure that your organization has adequate dedicated resources to manage this analytical work. Identifying patterns and correcting them early is the most cost effective approach to speeding your return to normal revenue patterns.

Phoenix consultants have extensive experience managing and analyzing revenue cycle data, in association with billing system installs and optimizations — augmented by our ICD-10 expertise.   If you’d to discuss how we might assist you with this critical ICD-10 related revenue cycle analysis, contact us!

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