title: Preparing for the ICD-10-CM Transition: Automated Methods for Translating ICD Codes in Clinical Phenotype Definitions.
abstract: BACKGROUND
  The national mandate for health systems to transition from ICD-9-CM to ICD-10-CM in October 2015 has an impact on research activities. Clinical phenotypes defined by ICD-9-CM codes need to be converted to ICD-10-CM, which has nearly four times more codes and a very different structure than ICD-9-CM.
  
  
  METHODS
  We used the Centers for Medicare & Medicaid Services (CMS) General Equivalent Maps (GEMs) to translate, using four different methods, condition-specific ICD-9-CM code sets used for pragmatic trials (n=32) into ICD-10-CM. We calculated the recall, precision, and F score of each method. We also used the ICD-9-CM and ICD-10-CM value sets defined for electronic quality measure as an additional evaluation of the mapping methods.
  
  
  RESULTS
  The forward-backward mapping (FBM) method had higher precision, recall and F-score metrics than simple forward mapping (SFM). The more aggressive secondary (SM) and tertiary mapping (TM) methods resulted in higher recall but lower precision. For clinical phenotype definition, FBM was the best (F=0.67), but was close to SM (F=0.62) and TM (F=0.60), judging on the F-scores alone. The overall difference between the four methods was statistically significant (one-way ANOVA, F=5.749, p=0.001). However, pairwise comparisons between FBM, SM, and TM did not reach statistical significance. A similar trend was found for the quality measure value sets.
  
  
  CONCLUSIONS
  The optimal method for using the GEMs depends on the relative importance of recall versus precision for a given use case. It appears that for clinically distinct and homogenous conditions, the recall of FBM is sufficient. The performance of all mapping methods was lower for heterogeneous conditions. Since code sets used for phenotype definition and quality measurement can be very similar, there is a possibility of cross-fertilization between the two activities.
  
  
  CONCLUSIONS
  Different mapping approaches yield different collections of ICD-10-CM codes. All methods require some level of human validation.
authors.affiliation: National Library of Medicine.
authors.forename: Kin Wah
authors.initials: KW
authors.lastname: Fung
authors.affiliation: Duke University School of Nursing.
authors.forename: Rachel
authors.initials: R
authors.lastname: Richesson
authors.affiliation: Duke Clinical Research Institute.
authors.forename: Michelle
authors.initials: M
authors.lastname: Smerek
authors.affiliation: Duke University School of Nursing.
authors.forename: Katherine C
authors.initials: KC
authors.lastname: Pereira
authors.affiliation: Group Health Research Institute.
authors.forename: Beverly B
authors.initials: BB
authors.lastname: Green
authors.affiliation: Duke Clinical Research Institute; Duke University School of Medicine.
authors.forename: Ashwin
authors.initials: A
authors.lastname: Patkar
authors.affiliation: Duke University School of Medicine.
authors.forename: Megan
authors.initials: M
authors.lastname: Clowse
authors.affiliation: Center for Health Research, Kaiser Permanente Northwest.
authors.forename: Alan
authors.initials: A
authors.lastname: Bauck
authors.affiliation: National Library of Medicine.
authors.forename: Olivier
authors.initials: O
authors.lastname: Bodenreider
keywords: Cohort Identification; Electronic Health Record (EHR); Health Information Technology; Standardized Data Collection