Developing optimized automated rule sets for reporting hemolysis, icterus and lipemia based on a priori outcomes analysis

•A process to develop and test automated serum indices decision rules is outlined.•This process was modeled in 2 laboratories serving 3 patient populations.•Serum indices decision algorithms need to be tailored to laband patient population.

BackgroundThere is limited information about the effects of instituting CLSI Document C56A recommended workflows for the automated detection of hemolysis, lipemia and icterus (HIL) in different clinical laboratories and patient populations. We describe a process to develop and tailor automated reporting rules that are appropriate for the local laboratory population.MethodsAutomated decision algorithms were generated and applied to 2 high volume labs serving community and hospital populations. Proposed rules were applied to the datasets offline to predict the outcomes, and then were further optimized prior to implementation.ResultsIntroduction of automated serum indices decreased HIL flagging compared to manual flagging. Hemolysis flagging was the greatest in all 3 patient populations, and was successfully reduced for LD, CK and AST by optimized rules that incorporated both the H-index result and the analyte result. Changes in flagging rates were also patient population specific, particularly for icterus which was a problem in hospitalized populations but not in the community. Overall, concordance between manual and automated flagging methods was very low in both laboratories.ConclusionsWe demonstrate that flagging algorithms may not be universally transferable due to lab specific and population specific factors and demonstrate the benefits of local, a priori testing of algorithms prior to implementation.