Cited by 41Healthcare cost, quality, practicesClinical Laboratory Practices and Quality ControlElectronic Health Records Systems

Temporal trends of hemoglobin A1c testing

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Pivovarov, Rimma, Albers, David J, Hripcsak, George, Sepulveda, Jorge L, Elhadad, Noémie·2014·Journal of the American Medical Informatics Association

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. MATERIALS AND 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. DISCUSSION: 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.

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  • CONCERN EWS's methodological choices, including robust modeling of temporal data patterns and use of nursing assessment and observational data, allowed it to have accurate and robust predictive power and sufficient lead time to alter a patient's clinical trajectory.

    “Several methodological choices allowed CONCERN EWS to have accurate and robust predictive power and sufficient lead time to alter a patient’s clinical trajectory as we observed in this trial, including: (1) robust modeling of temporal data patterns40–42 (for example, time of day, day of week and patient hospital day) for health care processes (for example, nurses’ surveillance and medication”

    From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [43]

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Research works printed but not linked: 22

  1. [1]Smith M, Saunders R, Stuckhardt L, et al. Committee on Learning the Health Care System in America. Best care at lower cost: the path to continuously learning health care in America, Washington, DC: Institute of Medicine; 2012. [PubMed] [Google Scholar]
  2. [2]Sacks DB, Bruns DE, Goldstein DE, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem 2002;48:436–72 [PubMed] [Google Scholar]
  3. [3]Selvin E, Steffes MW, Zhu H, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med 2010;362:800–11 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. [5]Committee IE. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 2009;32:1327–34 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. [6]American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2010;33(Suppl 1):S62–S69 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. [7]Association AD. Standards of medical care in diabetes–2013. Diabetes Care 2013;36(Suppl 1):S11–66 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. [8]Laxmisan A, Vaughan-Sarrazin M, Cram P. Repeated Hemoglobin A1C ordering in the VA health system. Am J Med 2011;124:342–9 [DOI] [PubMed] [Google Scholar]
  8. [9]Driskell OJ, Holland D, Hanna FW, et al. Inappropriate requesting of glycated hemoglobin (Hb A1c) is widespread: assessment of prevalence, impact of national guidance, and practice-to-practice variability. Clin Chem 2012;58:906–15 [DOI] [PubMed] [Google Scholar]
  9. [10]Akan P, Cimrin D, Ormen M, et al. The inappropriate use of HbA1c testing to monitor glycemia: is there evidence in laboratory data? J Eval Clin Pract 2007;13:21–4 [DOI] [PubMed] [Google Scholar]
  10. [11]Van Walraven C. Population-based study of repeat laboratory testing. Clin Chem 2003;49:1997–2005 [DOI] [PubMed] [Google Scholar]
  11. [12]Salvagno GL, Lippi G, Targher G, et al. Monitoring glycaemic control: is there evidence for appropriate use of routine measurement of glycated haemoglobin? Clin Chem Lab Med 2007;45:1065–7 [DOI] [PubMed] [Google Scholar]
  12. [13]Massey FJ, Jr. The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc 1951;46:68–78 [Google Scholar]
  13. [14]Goldstein DE, Little RR, Lorenz RA, et al. Tests of glycemia in diabetes. Diabetes Care 2004;27:1761–73 [DOI] [PubMed] [Google Scholar]
  14. [15]Singer DE, Coley CM, Samet JH, et al. Tests of glycemia in diabetes mellitus. Their use in establishing a diagnosis and in treatment. Ann Intern Med 1989;110:125–37 [DOI] [PubMed] [Google Scholar]
  15. [16]Peters AL, Davidson MB, Schriger DL, et al. A clinical approach for the diagnosis of diabetes mellitus: an analysis using glycosylated hemoglobin levels. Meta-analysis Research Group on the Diagnosis of Diabetes Using Glycated Hemoglobin Levels. JAMA J Am Med Assoc 1996;276:1246–52 [PubMed] [Google Scholar]
  16. [17]Lyon AW, Higgins T, Wesenberg JC, et al. Variation in the frequency of hemoglobin A1c (HbA1c) testing: population studies used to assess compliance with clinical practice guidelines and use of HbA1c to screen for diabetes. J Diabetes Sci Technol 2009;3:411–17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. [18]Stewart BA, Fernandes S, Rodriguez-Huertas E, et al. A preliminary look at duplicate testing associated with lack of electronic health record interoperability for transferred patients. J Am Med Inform Assoc JAMIA 2010; 17:341–4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. [19]Baron JM, Dighe AS. The role of informatics and decision support in utilization management. Clin Chim Acta Int J Clin Chem 2014;427:196–201 [DOI] [PubMed] [Google Scholar]
  19. [21]Miyakis S, Karamanof G, Liontos M, et al. Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy. Postgrad Med J 2006;82:823–9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. [22]Huang ES, Basu A, O'Grady M, et al. Projecting the future diabetes population size and related costs for the U.S. Diabetes Care 2009;32:2225–9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. [23]Zhi M, Ding EL, Theisen-Toupal J, et al. The landscape of inappropriate laboratory testing: a 15-year meta-analysis. PLoS ONE 2013;8:e78962. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. [24]Bates DW, Boyle DL, Rittenberg E, et al. What proportion of common diagnostic tests appear redundant? Am J Med 1998;104:361–8 [DOI] [PubMed] [Google Scholar]

Cited by

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Authors
Pivovarov, R., Albers, D. J., Hripcsak, G., Sepulveda, J. L., & Elhadad, N.
Affiliations
Columbia University (US)
Venue
Journal of the American Medical Informatics Association
Published
2014-11
Volume
21
Issue
6
Pages
1038-1044
ISSN
  • 1067-5027 (ISSN-L)
  • 1527-974X
OpenAlex
W2160555924
Cited by
41
References
2
Citations by year
2026: 1 · 2025: 5 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 3 · 2020: 5 · 2019: 2 · 2018: 8 · 2017: 5 · 2016: 1 · 2015: 4 · 2014: 1
FWCI
6.64
Type
journal article
Language
en
Open access
Locations
  • Journal of the American Medical Informatics Association (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (1144854)
  • U.S. National Library of Medicine (R01:LM06910, LM06910, R01LM006910, R01LM010027)
Topics
  • Health Sciences › Health Professions › General Health Professions › Healthcare cost, quality, practices
  • Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
hemoglobin A1c testing, temporal trends, guideline adherence, diabetes diagnosis, electronic health records, point-of-care testing, care coordination, health care costs
MeSH terms
Academic Medical Centers, Blood Chemical Analysis, Diabetes Mellitus, Glycated Hemoglobin, Humans, Longitudinal Studies, Medical Audit, New York City, Patient Care Management, Models, Statistical, Practice Guidelines as Topic, Statistics, Nonparametric, Unnecessary Procedures, Organizational Case Studies, Guideline Adherence, Electronic Health Records

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