Literature DB >> 31228416

Evaluating practice variance among family physicians to identify targets for laboratory utilization management.

Leonard T Nguyen1, Maggie Guo1, Brenda Hemmelgarn2, Hude Quan2, Fiona Clement2, Tolulope Sajobi2, Roger Thomas3, Tanvir C Turin3, Christopher Naugler4.   

Abstract

BACKGROUND: There is widespread variation in testing practice among practitioners, however there has been no objective way to pinpoint target tests for utilization management. We propose to take advantage of inter-physician variance in clinical practice as a quantitative measure to generate lists of potentially misutilized tests.
METHODS: Testing frequencies from a database of clinical testing volumes for outpatients in Calgary, Canada, were obtained for the study period of 2016. For each chemistry, microbiology or hematology test, an arithmetic coefficient of variation (CV) was calculated from family physicians' ordering frequencies.
RESULTS: The mean CV for all 358 tests considered was 219% (95% CI 206-231%) with a range of 52-729%. The highest variance was observed for human T-lymphotropic virus antibody testing and several tests for heavy metal levels (mercury, copper, zinc and chromium). Among the 100 most commonly run tests, high variance was found for several endocrinology tests including cortisol.
CONCLUSIONS: The utility of ranking clinical tests by ordering variance presents a practical approach to evaluate relative variation in physician practice strategy and to identify potential areas of misutilization.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Administrative medicine; Community medicine; Family medicine; Laboratory medicine; Public health; Utilization

Mesh:

Substances:

Year:  2019        PMID: 31228416     DOI: 10.1016/j.cca.2019.06.017

Source DB:  PubMed          Journal:  Clin Chim Acta        ISSN: 0009-8981            Impact factor:   3.786


  3 in total

1.  Association Between Provider-Sponsored Health Plan Ownership and Health Insurance Marketplace Plan Quality.

Authors:  Sih-Ting Cai; David Anderson; Coleman Drake; Jean M Abraham
Journal:  J Gen Intern Med       Date:  2022-02-17       Impact factor: 6.473

2.  Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.

Authors:  Jason M Baron; Richard Huang; Dustin McEvoy; Anand S Dighe
Journal:  JAMIA Open       Date:  2021-03-01

3.  Dataset of clinical laboratory tests according to ordering variance among family physicians in Calgary, Alberta, Canada.

Authors:  Leonard T Nguyen; Maggie Guo; Brenda Hemmelgarn; Hude Quan; Fiona Clement; Tolulope Sajobi; Roger Thomas; Tanvir C Turin; Christopher Naugler
Journal:  Data Brief       Date:  2019-08-12
  3 in total

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