4101 — Switching to ICD-10: Lessons learned from 202 Conditions Mapped by the Women's Health Evaluation Initiative
Lead/Presenter: Jonathan Shaw,
COIN - Palo Alto
All Authors: Shaw JG (Center for Innovation to Implementation, VA Palo Alto; Stanford University, Division of Primary Care & Population Health), Veldanda S (Center for Innovation to Implementation, VA Palo Alto), Saechao F (Center for Innovation to Implementation, VA Palo Alto) Romodan Y (Center for Innovation to Implementation, VA Palo Alto) Berg E (Center for Innovation to Implementation, VA Palo Alto) Phibbs CS (Center for Innovation to Implementation and Health Economics Resource Center, VA Palo Alto; Stanford University, Department of Pediatrics) Frayne SM (Center for Innovation to Implementation, VA Palo Alto; Stanford University, Division of Primary Care & Population Health)
Women's Health Evaluation Initiative (WHEI) aggregates ICD-9-CMs into 202 "conditions," to characterize women Veterans' health profiles. We developed/tested an algorithm to address the 2016 transition to ICD-10, mapping all 70,000+ ICD-10s.
"Backward" ICD-10 to ICD-9 mapping guided assignment of every ICD-10 to the original 202 conditions. CMS's General Equivalency Mappings (GEMs) was our definitive cross-walk. For one-to-many translations, if the translation alternatives (multiple ICD-9s) mapped to discrepant WHEI conditions, we algorithmically mapped the ICD-10 concordant with the ICD-9 with highest frequency among FY2015 women. Two physicians manually reviewed algorithm results for ICD-10s with frequency > = 100 among FY2016 women to resolve any inconsistencies with original WHEI mappings. To test the algorithm, we compared condition frequencies in adjacent years/coding systems (FY2015/FY2016). Although temporal change is expected, prior work showed most condition frequencies did not change by > 5% across a longer (5-year) period; thus we examined any > 5% absolute change to identify conditions substantially impacted by ICD-9/10 translation.
Of 72,157 ICD-10s backwards mapped to ICD-9s, 7,961 (11%) had one-to-many GEMs mapping; of those, 3,583 had ambiguous WHEI condition mapping and were re-mapped per the algorithm. Manual review of algorithmically-mapped ICD-10s with frequency > 100 (n = 74) resulted in re-mapping of 28. Another 735 without GEMs ICD-9 equivalents were manually mapped. Comparing across the ICD-9/10 (FY2015/FY2016) divide, only 5 of 202 WHEI conditions frequencies changed > 5%. Review of these 5 showed, for two related conditions, ICD-10's greater specificity led to fewer cases assigned to the non-specific condition (substitution effect). It also revealed limitations of GEMs backwards mapping, with the most notable example being ICD-10 Z72.0 (tobacco use) mapping one-to-one to ICD-9 V69.8 (lifestyle problems) resulting in initial assignment to "Residual Codes" rather than appropriately to "Tobacco use." Such review prompted reassignment of another 102 codes.
In VA research and surveillance, existing general equivalency mapping resources can facilitate identifying ICD-10 groupings to replace prior ICD-9 categories, but such efforts must be complemented by careful consideration of the mapping effort's purpose and selective manual review.
Our successful adaptation of 202 WHEI conditions to ICD-10 will allow meaningful comparison of healthcare profiles and temporal changes across the ICD-9/10 eras.