The transition to ICD-10 is a momentous change in the healthcare industry that will affect healthcare organizations' entire revenue cycle. However, most of the focus remains only on education and documentation issues. Expected financial impact and clinical outcome enhancement across commercial and government payers continue to be unclear and prompts hospitals to ask the question: How is ICD-10 significant to these issues? How can business intelligence solutions help?
Using data analytics to quantify impacts on reimbursement can provide a reliable perspective reflecting how claims could and should be processed by respective payors. This information is necessary to equip all parties present at discussions and negotiations with payors and qualified healthcare providers. Best practices suggest beginning with a hospital's full year's worth of historical ICD-9 data and applying the content management system "forward" to general equivalence mappings, which provides a translation source system from the ICD-9 data to ICD-10 CM/PCS code options.
Visualizing a financial impact is also possible by adding CMS reimbursement maps. Many payors intend to use the CMS reimbursement maps to process ICD-10 submitted 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 the ICD-10 codes to the most frequently recognized ICD-9 codes for reimbursement. A final step for inpatient claims is to apply the appropriate diagnosis related group grouping logic to visualize where DRG changes are possible based on how payors will adjudicate the ICD-10 claims.
Hospitals can further "supercharge" their data analytics with dual coding data (ICD-9 and ICD-10) to drill down and fine-tune the financial impacts. Most hospitals do not have the ICD-10 trained coder capacity to dual code all accounts. While an obvious place to dual code is the most frequent DRGs and ICD-9 codes, this is a 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 the limited ICD-10 trained coder resources be deployed where there is most to gain from their dual coding efforts.
"ICD codes create cross-continuum views of patient treatments. The quality of ICD codes reflects the quality of tracking, trending and predictions. Your analytics must be accurate, specific and complete to find the patterns of usage, and treatment that are affecting your healthcare budget," says Mike Gallagher, MD, MBA, MPH, Chief Medical Information Officer, at Health Revenue Assurance Associates.
Plainly, what is fuzzy and ambiguous can come into crystal clear focus with the use of big data for ICD-10 execution efforts, whether predictive financial impacts or quality clinical outcomes.
Andrea Clark, RHIA, CCS, CPC-H, is chairman, CEO and founder of Health Revenue Assurance Associates. HRAA is a provider of revenue\integrity technology and services for healthcare organizations across the U.S.
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The financial aspect
Using data analytics to quantify impacts on reimbursement can provide a reliable perspective reflecting how claims could and should be processed by respective payors. This information is necessary to equip all parties present at discussions and negotiations with payors and qualified healthcare providers. Best practices suggest beginning with a hospital's full year's worth of historical ICD-9 data and applying the content management system "forward" to general equivalence mappings, which provides a translation source system from the ICD-9 data to ICD-10 CM/PCS code options. Visualizing a financial impact is also possible by adding CMS reimbursement maps. Many payors intend to use the CMS reimbursement maps to process ICD-10 submitted 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 the ICD-10 codes to the most frequently recognized ICD-9 codes for reimbursement. A final step for inpatient claims is to apply the appropriate diagnosis related group grouping logic to visualize where DRG changes are possible based on how payors will adjudicate the ICD-10 claims.
Hospitals can further "supercharge" their data analytics with dual coding data (ICD-9 and ICD-10) to drill down and fine-tune the financial impacts. Most hospitals do not have the ICD-10 trained coder capacity to dual code all accounts. While an obvious place to dual code is the most frequent DRGs and ICD-9 codes, this is a 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 the limited ICD-10 trained coder resources be deployed where there is most to gain from their dual coding efforts.
The clinical outcomes aspect
Accurate, specific and complete ICD codes for every patient record are the ultimate cross-continuum data to track, trend and predict value. In the past, understanding utilization patterns was key, but with healthcare reform, the goal is now improved population health outcomes and value (quality divided by cost). Without accurate and complete ICD-coded data, outcomes and therefore value cannot be understood and more concerning, may be undervalued or misconstrued."ICD codes create cross-continuum views of patient treatments. The quality of ICD codes reflects the quality of tracking, trending and predictions. Your analytics must be accurate, specific and complete to find the patterns of usage, and treatment that are affecting your healthcare budget," says Mike Gallagher, MD, MBA, MPH, Chief Medical Information Officer, at Health Revenue Assurance Associates.
Plainly, what is fuzzy and ambiguous can come into crystal clear focus with the use of big data for ICD-10 execution efforts, whether predictive financial impacts or quality clinical outcomes.
Andrea Clark, RHIA, CCS, CPC-H, is chairman, CEO and founder of Health Revenue Assurance Associates. HRAA is a provider of revenue\integrity technology and services for healthcare organizations across the U.S.
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