How analytics can offset a possible ICD-10 revenue decline
Healthcare providers have been warned for months that failure to prepare for the October 1 deadline to implement ICD-10 will wreak havoc on both their processes of care and revenue cycle.
Some of the predictions* by the Centers for Medicare & Medicaid Services (CMS) are sobering indeed:
· Denial rates will increase by 100% to 200%
· Accounts receivables will swell by 20% to 40%
· Claim error rates will rise to 6% to 10%
Perhaps most sobering of all, CMS says healthcare providers could see payments decline for up to two years* after the new medical and diagnostic coding system is enacted. And all of this is going on just as healthcare providers are struggling with a transition from fee-for-service to value-based reimbursement models.
Despite this, many healthcare providers are woefully unprepared for ICD-10. What’s the hold-up? According to a survey last year by the Workgroup for Electronic Data Interchange (WEDI)**, providers cited “other priorities” as their top obstacle to ICD-10 readiness. There are certainly competing priorities, but none more important than averting a potential revenue disaster.
The good news for providers scrambling belatedly to get ready for October 1 and beyond is that they can use data analytics both to revisit priorities before the deadline and make necessary adjustments after ICD-10 is live.
By analyzing the right data, providers can gain valuable clarity into their revenue cycle and related processes, thus enabling them to identify high-volume and high-value treatments and procedures. These are the ones which deserve the most attention in terms of the ICD-10 codes that clinicians and support staff must learn – particularly if the translation of these high-value and/or high-volume treatments to the new code set is particularly complex. After all, the last thing providers need is for coding errors to result in delays and denials for their most common or profitable procedures and services.
After October 1, providers no longer will have to speculate about the impact of ICD-10 on the revenue cycle because they’ll be experiencing it. That’s the perfect time to begin collecting and analyzing live data in order to evaluate how well the transition is going, whether any adjustments should be made, and how their organization is performing post-October 1 relative to other similar providers.
In other words, data properly analyzed can give providers a reality check and a roadmap for improvement. Given CMS’s prediction that ICD-10 could decrease revenue for up to two years, that roadmap may be very valuable. Does a particular diagnosis-related group have a high claims denial rate? Why? Are there documentation problems? Can denials be traced to a particular coder or clinician?
Clarity paves the way for healthcare providers to make corrections and improvements before and after October 1. Data analytics can provide that clarity.
*HIMSS G7 Advisory Report, ICD-10 Transformation: Five Critical Risk-Mitigation Strategies, 2011.
McMillan, M. (October 1, 2012). Practice Makes Perfect When It Comes to ICD-10. Journal of AHIMA,
**Source: WEDI, ICD-10 Industry Readiness Survey, August 2014
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