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The momentum is back for ICD-10 risk assessments, following the Department of Health and Human Services’ (DHHS) announcement of a one-year delay in implementation date. Although we are still in the 30-day comment period, the industry is ready to begin and/or continue its assessment efforts. That’s my one big “take away” from the April 2012 AHIMA ICD-10 Summit.

Thank goodness for the pre-summit announcement, as the April event was well received with great attendance! I had the distinct pleasure and honor to speak at the ICD-10 Summit with Dr. Louis Rossiter on the subject of  “Predicting Payment Impact of ICD-10 through Analytics.” Of the roomful of more than 200 attendees, more than half had begun their risk assessment, and the others were taking it all in so that they could have the tools to begin.

We understand that ICD-10 will have a major impact on the documentation expected from clinicians, and an even bigger effect on the coding staff within your organization; but because Centers for Medicare and Medicaid Services (CMS) has also reassigned combination of codes in ICD-10 to define diagnostic related groups (DRGs) for payment, a change in Medicare and commercial reimbursements is inevitable. As part of the risk assessment process, it will be vital for hospitals to get a handle on the financial impacts of ICD-10.

Our presentation discussed the methodology behind informatics and the opportunity to address the impact of ICD-10 through analytics. We believe the key for information management specialists is to monetize the impact of ICD-10 through modeling tools. These tools can help you better communicate and get the attention of your CEO and CFO to take action, properly resource your efforts, and stimulate change for all who need to be involved in the transition. By modeling tools, we recommend using your own hospital data to simulate payments under ICD-9 compared to payments under ICD-10. Please note that they will be different. You can trend this information over time and find variances between before and after transition by service line, DRG, and major diagnostic category (MDC).

Naturally, your hospital’s recent claims data is a rich source of information. It is possible to translate your ICD-9 claims files for a recent period of time into ICD-10. Then use your hospital’s current Medicare payments by DRG to simulate the reimbursement impact for all payers in order to get a pure ICD-10 impact. Summarize the payments by various factors such as DRG, MDC, and service line so that you can see which areas of the hospital will be affected the most, and the least, or not at all.

We utilized the terminology “Medicare Estimated Payment” (MEP) throughout our presentation to indicate your hospital’s current payment rate by DRG under Medicare, which can be used to simulate payments for all payers. In the traditional approach, we have natively coded patient discharge records that can be assigned a DRG and an MEP to give you an estimate of what the hospital will be paid. It is possible to take the same records and the information they already contain to assign the ICD-10 codes. You can then translate those to DRGs and again assign the MEP just as before.



Two important observations to note about this process:

1. The ICD-9 code(s) was assigned by a coder thinking and using ICD-9 (we call this native coding). The ICD-10 is simulated based upon this natively coded ICD-9. The simulation was not natively coded in ICD-10. In our experience, this does not seem to make a difference, specifically since CMS does not know the impact of natively coding ICD-10.

2. The reason the payments will be different is the way CMS has used the ICDs differently in 9 versus 10 to assign the DRG. For example, many cardiovascular and orthopedics services have been grouped to a lower DRG payment, but in other instances, some have a higher DRG payment.

When the MEP has been assigned to each claim, it is possible to look at each claim and understand what DRG will be assigned under ICD-9 or ICD-10 and what the resultant payment is likely to be. They can be further analyzed by average or aggregate payments. We recommend that when you do your analyses, you consider calculating payments specific to your institution (your own wage index, unique DSH payment, and unique IME/GME). You can work with your CFO to obtain this information.

An ICD-10 Payment Impact Analysis solution should translate ICD-9 hospital claims data into ICD-10 and use Medicare expected payment so that hospitals can simulate the reimbursement impact by MS-DRG for a recent period of time. Hospitals should be able to drill down into the data and assess financial impact by MDC, MS-DRG, and major service lines. The bottom line is that you must be able to view hospital claims with the ICD-9, the translated ICD-10, and the Medicare payment at a glance. The goal is to leverage the financial impact analysis data to galvanize change across your organization.

It is critical for every organization to understand how they do business under ICD-9 as well as ICD-10. Predictive analytics is the gateway to analyzing large amounts of data in a clear and concise manner. A payment impact analysis pinpoints the winning and losing service lines so that an organization can plan and implement strategies for fiscal success. Even with a one-year delay, it will be imperative for all organizations to have the best analytic tools at their disposal.

About the Author

Maria T. Bounos, RN, MPM, CPC-H, is the Business Development Manager for Regulatory and Reimbursement software solutions for Wolters Kluwer.  Maria began her career at Wolters Kluwer as a product manager, responsible for product development, maintenance, enhancements and business development and now solely focuses on business development.  She has more than twenty years of experience in healthcare including nursing, coding, healthcare consulting, and software solutions.

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