Literature DB >> 24006028

Measuring Diabetes Care Performance Using Electronic Health Record Data: The Impact of Diabetes Definitions on Performance Measure Outcomes.

Annemarie Gregory Hirsch1, Ann Scheck McAlearney2.   

Abstract

The objective was to examine the use of electronic health record (EHR) data for diabetes performance measurement. Data were extracted from the EHR of a health system to identify patients with diabetes using 8 different EHR data-based methods of identification. These EHR-based methods were compared to the gold standard of a manual medical record review. The study team then assessed whether the method of identifying patients with diabetes could affect performance measurement scores. The sensitivity of the 8 EHR-based methods of identifying patients with diabetes ranged from moderate to high. The use of certain data elements in the EHR to identify patients with diabetes selectively identified those who had better performance measures. Diabetes performance measures are influenced by the data elements used to identify patients. As EHR data are used increasingly to measure performance, continuing to improve our understanding of how EHR data are collected and used will be critical.
© 2013 by the American College of Medical Quality.

Entities:  

Keywords:  diabetes; health information technology; performance measurement

Mesh:

Year:  2013        PMID: 24006028     DOI: 10.1177/1062860613500808

Source DB:  PubMed          Journal:  Am J Med Qual        ISSN: 1062-8606            Impact factor:   1.852


  6 in total

1.  "EMERGing" Electronic Health Record Data Metrics: Insights and Implications for Assessing Residents' Clinical Performance in Emergency Medicine.

Authors:  Stefanie S Sebok-Syer; Lisa Shepherd; Allison McConnell; Adam M Dukelow; Robert Sedran; Lorelei Lingard
Journal:  AEM Educ Train       Date:  2020-08-09

2.  Measuring pain care quality in the Veterans Health Administration primary care setting.

Authors:  Stephen L Luther; Dezon K Finch; Lina Bouayad; James McCart; Ling Han; Steven K Dobscha; Melissa Skanderson; Samah J Fodeh; Bridget Hahm; Allison Lee; Joseph L Goulet; Cynthia A Brandt; Robert D Kerns
Journal:  Pain       Date:  2021-09-15       Impact factor: 7.926

3.  Factors associated with daily consumption of sugar-sweetened beverages among adult patients at four federally qualified health centers, Bronx, New York, 2013.

Authors:  Ross B Kristal; Arthur E Blank; Judith Wylie-Rosett; Peter A Selwyn
Journal:  Prev Chronic Dis       Date:  2015-01-08       Impact factor: 2.830

4.  Monitoring compliance with standards of care for chronic diseases using healthcare administrative databases in Italy: Strengths and limitations.

Authors:  Rosa Gini; Martijn J Schuemie; Alessandro Pasqua; Emanuele Carlini; Francesco Profili; Iacopo Cricelli; Patrizio Dazzi; Valentina Barletta; Paolo Francesconi; Francesco Lapi; Andrea Donatini; Giulia Dal Co; Modesta Visca; Mariadonata Bellentani; Miriam Sturkenboom; Niek Klazinga
Journal:  PLoS One       Date:  2017-12-12       Impact factor: 3.240

5.  Medicaid coverage accuracy in electronic health records.

Authors:  Miguel Marino; Heather Angier; Steele Valenzuela; Megan Hoopes; Marie Killerby; Brenna Blackburn; Nathalie Huguet; John Heintzman; Brigit Hatch; Jean P O'Malley; Jennifer E DeVoe
Journal:  Prev Med Rep       Date:  2018-07-27

6.  The Diabetes Location, Environmental Attributes, and Disparities Network: Protocol for Nested Case Control and Cohort Studies, Rationale, and Baseline Characteristics.

Authors:  Annemarie G Hirsch; April P Carson; Nora L Lee; Tara McAlexander; Carla Mercado; Karen Siegel; Nyesha C Black; Brian Elbel; D Leann Long; Priscilla Lopez; Leslie A McClure; Melissa N Poulsen; Brian S Schwartz; Lorna E Thorpe
Journal:  JMIR Res Protoc       Date:  2020-10-19
  6 in total

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