Detection of preanalytic laboratory testing errors using a statistically guided protocol.
Preanalytic laboratory testing errors are often difficult to identify. We demonstrate how laboratories can integrate statistical models with clinical judgment to develop protocols for preanalytic error detection. Specifically, we developed a protocol to identify spuriously elevated glucose values resulting from improper "line draws" or related phlebotomy errors. Using a decision tree-generating algorithm and an annotated set of training data, we generated decision trees to classify critically elevated glucose results as "real" or "spurious" based on available laboratory parameters. Decision trees revealed that a 30-day patient-specific average glucose concentration lower than 186.3 mg/dL (10.3 mmol/L), a current glucose concentration higher than 663 mg/dL (37 mmol/L), and an anion gap lower than 16.5 mEq/L (16.5 mmol/L) suggested a spurious result. We then used the results from the decision tree analysis to inform the implementation of a clinical protocol that significantly improved the laboratory's identification of spurious results. Similar approaches may be useful in developing protocols to identify other errors or to assist in clinical interpretation of results.
Department of Pathology, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, USA.
Jason M
JM
Baron
Craig H
CH
Mermel
Kent B
KB
Lewandrowski
Anand S
AS
Dighe
D000465:Algorithms; D001185:Artificial Intelligence; D001786:Blood Glucose / Q000032:analysis; D002983:Clinical Competence; D003663:Decision Trees; D006801:Humans; D007753:Laboratories; D019300:Medical Errors; D015233:Models, Statistical
D001786:Blood Glucose