March 19, 2013

Code Power! Amplify Your ICD-10 Efforts with Data Analytics for HIM

By

The transition to ICD-10 continues to be in the forefront of changes in healthcare, but most of the focus remains on education and documentation. The prospects of expected financial impact and clinical outcome enhancement among commercial and government payors continue to be unclear, and this prompts health information management (HIM) leaders to ask: Knowing that these changes will be significant, how can I wrap my arms around them and assist my facility and clinicians in preparing?

HIM: Do not Fear the Financial Aspect

Using data analytics to quantify reimbursement impacts can provide HIM leaders with a reliable perspective on how claims might be processed by payors. This information is necessary for all parties present at discussions and negotiations with payors and qualified healthcare providers (QHP). Best practices suggest beginning with a hospital’s full year’s worth of historical ICD-9 data and applying the CMS “forward” approach to General Equivalence Mappings (GEMs), which then can provide a translation source that can be used to go from the ICD-9 data to ICD-10 CM/PCS code options. Defining financial impact is possible by also adding the CMS reimbursement maps.

While many payers intend to use the CMS reimbursement maps to process ICD-10 claims, it is important that the tool used for this analysis allows the hospital staff to select the most appropriate ICD-10-CM/PCS codes for their service lines. This custom selection option is vital to meaningful application of the reimbursement maps, which convert ICD-10 codes to the most frequently recognized ICD-9 codes for reimbursement.

Native Coding is the Fun Part

Begin with enlisting the HIM professional’s extensive knowledge to perform native coding. Native coding involves using a mixed strategy combining data analytics with GEMs applied to historical claims, moving to actual ICD-10 code applications with documentation. Using data analytics provides a handy approach by offering identification of selected accounts and potential financial impacts (i.e. DRG-negative or -positive) by physician(s). A final step for inpatient claims is to apply the appropriate DRG grouping logic in order to visualize where unanticipated DRG changes are possible, based on how payors will adjudicate the ICD-10 claims.

Dual Coding: How Analytics can Help

HIM staff can “supercharge” their data analytics with dual-coding data (ICD-9 and ICD-10) used to drill down and fine-tune projected financial impacts. Most hospitals do not have the ICD-10-trained coder capacity to dual code all accounts. An obvious place to dual code is the most frequent DRGs and ICD-9 codes; however, this is not the most effective way to determine the subset of accounts most critical to continued financial continuity. A more effective approach is to target those codes that drive DRG changes and reduced adjusted mortality rates. It is imperative that limited ICD-10 trained coder resources be deployed where there is the most to gain from their dual-coding efforts.

Initially, there will be significant variation among ICD-10-trained coders in choosing ICD-10 codes, even on the very same accounts and even when using their encoders and other reference tools. Hospitals need an analytic tool to highlight variations and similarities in order to accelerate learning and ensure coding consistency. Achieving financial and compliance objectives is a process that demands coding consistency and continuity. Use of data analytics is vital toward achieving effective allocation of coding resources in preparing for the transition, financial impacts and payor negotiations. What’s more, it will be tremendously important beyond Oct. 1, 2014, to explore opportunities for improvement of ICD-10 data (and, in turn, financial stability).

Code Power: Unleash It

Harnessing data analytics for your facility’s ICD-10 initiatives is easier than you think, and every facility has a hidden jackpot within its own coded data. But we still need to think more globally in terms of clinical outcomes, readmissions, patient profiles, and ultimately, cross continuum data tracking. Remember, using data analytics does not cause more work – it forces you to work smarter instead of harder. So, let’s summarize:

  • Debunking the reactive approach with a proactive solution

    • Using data analytics allows HIM to move forward with a productive and efficient workflow
    • Historic claims data provides a decisional roadmap for navigating ICD-10 documentation and training requirements

  • There are two types of General Equivalent Mappings (GEMs)
    • Translation GEMs “forward” map ICD-9 to ICD-10
    • Reimbursement GEMs “backwards” map ICD-10 to ICD-9 (in order to calculate reimbursement on the two-year, revenue-neutral requirement)
    • Facilities can create customized selections of default appropriate ICD-10 codes (when there are multiple codes to choose from) based on documentation and patient populations to model potential impacts
  • Risk mitigation with data analytics:
    • Identifies revenue that could be at risk
    • Allows unlimited drilling down (by department, physician, etc.) to identify and verify financial impacts and clinical documentation improvement (CDI) roadmaps
    • Pinpoints the top 25 DRG “winners and losers” with a preparatory method for native coding efforts
    • Strategizes the addressing of documentation issues universally and by physician
    • Pinpoints accounts that can be identified easily
    • Alleviates negative reimbursement while quantifying positive reimbursement
  • Amplification for HIM coding staff ICD-10 education and dual-coding responsibilities
    • Real examples will improve accuracy and productivity due to increased comfort level and confidence building

About the Author

Andrea Clark is a prominent healthcare industry expert who founded HRAA in 2001. Ms. Clark has more than 30 years of experience working with healthcare professionals, information systems, hospital coding and operational and compliance training.

Contact the Author

To comment on this article please go to

Disclaimer: Every reasonable effort was made to ensure the accuracy of this information at the time it was published. However, due to the nature of industry changes over time we cannot guarantee its validity after the year it was published.