1. Laboratory data as a service
R. George Hauser, MD

2. Problems with cdw laboratory data
Non-standard laboratory test identifiers
Even common tests (e.g., troponin) have site-specific, non-standardized names
Commonly employed workarounds include LOINC codes and searches for tests by name
LOINC codes are inconsistent, erroneous, and missing
Searches for tests by name require time-intensive manual curation
Non-standard laboratory results
Categorical results do not have standardized values (e.g., positive, pos, P, positive for X)
Numeric results with mixed character values (e.g., <100 mg) require modification
The data requires a large amount of effort to standardize!

3. solution
Standardization of laboratory identifiers
Manual laboratory identifier assignment by experts aided by summary metrics
Quality control of laboratory identifiers to ensure accuracy in test identifier assignment, consistency of tests between sites and across time, and coverage of the most commonly performed tests
Standardization of laboratory results
Numerical result standardization
Categorical result standardization
Deployed on CDW
>95% coverage of laboratory identifiers for each site
>99.5% coverage of test results at each site
Extensive metrics on laboratory data (e.g., longitudinal volume, result percentiles)

4. Comparison to vacs data
Test Name
Name Match
Result Match
HCV antibody
99.5%
95.6%
HCV RIBA
100%
HCV Genotype
98.6%
99%
HCV Viral Load
99.6%
94%
Glucose
AST
Comparisons performed with Melissa Skanderson

5. Benefits to vacs
Elimination of individual efforts to standardize laboratory data
Time savings
Cost savings
Quality improvement
Standardized laboratory data curated by experts
One coherent solution to inconsistent laboratory data

6. Questions? R. George Hauser Ronald.Hauser@yale.edu

7. Prospective standardization - report