1. ICD9 vs ICD10 A comparison using Veterans Aging Cohort Study Data
Farah K-Khan, Melissa Skanderson, Cynthia Brandt, Janet Tate, Amy Justice
VACS-COMpAAAS Scientific Meeting
December 13, 2016

2. ICD9 to ICD10 Impact on Research Data
No Laterality (left or right side)
Laterality added to increase specificity M – Unequal limb length, Left Humerus
Non-specific codes
Specificity increases depth of data
Limited combination codes
Extensive combination codes
14,000 codes
69,000 codes
Length is 3 to 5
Length is 3 to 7
ICD10 detailed and specific, used globally
Potential for researchers to use more targeted queries and get more specific outcomes.
Researchers need to be aware of implications of mapping relationships where data overlaps ICD transition

3. Mapping Tool and Strategies
Used the AHRQ MapIT 2015 tool
General Equivalence Mapping(GEM)
Batch processing of ICD9 using backward and forward mapping
Created a Crosswalk
Validation queries using Structured Query Language(SQL)
Mapping tool and crosswalk
Local validation and data quality assessment after mapping

4. ICD CODES COMPARISION Unique Patients in VACS
1 Year Comparison October 2014 to October 2015 vs. October 2015 to October 2016
Sample restricted to people who were alive and seen in both time intervals
Inpatient
Outpatient
Percentage of unique people in VACS with alcohol related conditions, Diabetes,
Hypertension, and Cocaine use

5. Alcohol CODES COMPARISION Unique Patients with any alcohol Diagnosis
1 Year Comparison - October 2014 to October 2015 vs. October 2015 to October 2016
Sample restricted to people who were alive and seen in both time intervals

6. Goals Evaluate data trend across time and code sets
Evaluate original estimates compared to newer estimates for more conditions
Make determination of programming strategy based on estimates
Promote collaborative definition of conditions in ICD10 across cohorts
With time we may have less missing data
We may have to maintain ICD9 for older data
Compare ICD9 CM estimates with ICD10CM for more conditions