title: Temporal trends of hemoglobin A1c testing.
abstract: OBJECTIVE
  The study of utilization patterns can quantify potential overuse of laboratory tests and find new ways to reduce healthcare costs. We demonstrate the use of distributional analytics for comparing electronic health record (EHR) laboratory test orders across time to diagnose and quantify overutilization.
  
  
  METHODS
  We looked at hemoglobin A1c (HbA1c) testing across 119,000 patients and 15 years of hospital records. We examined the patterns of HbA1c ordering before and after the publication of the 2002 American Diabetes Association guidelines for HbA1c testing. We conducted analyses to answer three questions. What are the patterns of HbA1c ordering? Do HbA1c orders follow the guidelines with respect to frequency of measurement? If not, how and why do they depart from the guidelines?
  
  
  RESULTS
  The raw number of HbA1c orderings has steadily increased over time, with a specific increase in low-measurement orderings (<6.5%). There is a change in ordering pattern following the 2002 guideline (p<0.001). However, by comparing ordering distributions, we found that the changes do not reflect the guidelines and rather exhibit a new practice of rapid-repeat testing. The rapid-retesting phenomenon does not follow the 2009 guidelines for diabetes diagnosis either, illustrated by a stratified HbA1c value analysis.
  
  
  CONCLUSIONS
  Results suggest HbA1c test overutilization, and contributing factors include lack of care coordination, unexpected values prompting retesting, and point-of-care tests followed by confirmatory laboratory tests.
  
  
  CONCLUSIONS
  We present a method of comparing ordering distributions in an EHR across time as a useful diagnostic approach for identifying and assessing the trend of inappropriate use over time.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, USA.
authors.forename: Rimma
authors.initials: R
authors.lastname: Pivovarov
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, USA.
authors.forename: David J
authors.initials: DJ
authors.lastname: Albers
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, USA.
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.affiliation: Department of Pathology & Cell Biology, Columbia University, New York, USA.
authors.forename: Jorge L
authors.initials: JL
authors.lastname: Sepulveda
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, USA.
authors.forename: Noémie
authors.initials: N
authors.lastname: Elhadad
mesh_terms: D000046:Academic Medical Centers; D001774:Blood Chemical Analysis / Q000706:statistics & numerical data* / Q000639:trends; D003920:Diabetes Mellitus / Q000097:blood*; D057286:Electronic Health Records; D006442:Glycated Hemoglobin / Q000032:analysis*; D019983:Guideline Adherence / Q000639:trends*; D006801:Humans; D008137:Longitudinal Studies; D008485:Medical Audit; D015233:Models, Statistical; D009519:New York City; D019982:Organizational Case Studies; D010346:Patient Care Management / Q000458:organization & administration; D017410:Practice Guidelines as Topic; D018709:Statistics, Nonparametric; D019564:Unnecessary Procedures / Q000706:statistics & numerical data* / Q000639:trends
chemical_list: D006442:Glycated Hemoglobin
keywords: Electronic Health Records; Guideline Adherence; Laboratory Test Overutilization; Temporal Trends