Literature DB >> 31335195

Challenges with Patient Adoption of Automated Integration of Blood Glucose Meter Data in the Electronic Health Record.

Jake Weatherly1, Saniya Kishnani1, Tandy Aye1.   

Abstract

Providers often encourage patients with type 1 diabetes (T1D) to contact them with blood glucose (BG) values between visits. However, patients and families find it cumbersome to share their BG values with clinical providers, creating a barrier to communication. Although many phone applications exist to help patients track BG values, most do not integrate with the electronic health record (EHR). Recent advances in technology can integrate the glucose meter (GM) data into the EHR. This pilot and feasibility study aimed to understand how an automated integration system of GM data into the EHR and remote monitoring by health care providers would impact patient-provider communication. Patients or parents of patients with T1D (n = 32, average hemoglobin A1c [HgbA1c]: 8.5%, SD: 1.7, average age: 13.9 years, SD: 3.8) who owned an Apple iPod® or iPhone® (5s or higher) participated, and their number of contacts through telephone calls or MyChart™ messages between clinic visits was recorded during each of the three phases: run-in, intervention, and learned. Twenty-eight families completed all phases, and despite guided review of BG trends and automated integration of BG values, the number of patient-initiated calls (P = 0.23) and HgbA1c values (P = 0.08) did not improve, nor was there a clinically significant change in the number of BG checks per day. Barriers to adoption and effectiveness of this technology exist, and patient motivation is still needed.

Entities:  

Keywords:  Electronic health record; Glucose meter; Pediatric; Self-monitoring of blood glucose; Type 1 diabetes

Mesh:

Substances:

Year:  2019        PMID: 31335195      PMCID: PMC6812727          DOI: 10.1089/dia.2019.0178

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  19 in total

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Review 2.  12. Children and Adolescents.

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Authors:  Donna S Eng; Joyce M Lee
Journal:  Pediatr Diabetes       Date:  2013-04-30       Impact factor: 4.866

4.  Use of technology with health care providers: perspectives from urban youth.

Authors:  Sarah Lindstrom Johnson; S Darius Tandon; Maria Trent; Vanya Jones; Tina L Cheng
Journal:  J Pediatr       Date:  2012-01-13       Impact factor: 4.406

5.  A Pilot Study of Use of a Software Platform for the Collection, Integration, and Visualization of Diabetes Device Data by Health Care Providers in a Multidisciplinary Pediatric Setting.

Authors:  Jenise C Wong; Zara Izadi; Shannon Schroeder; Marie Nader; Jennifer Min; Aaron B Neinstein; Saleh Adi
Journal:  Diabetes Technol Ther       Date:  2018-11-21       Impact factor: 6.118

Review 6.  Technology to optimize pediatric diabetes management and outcomes.

Authors:  Jessica T Markowitz; Kara R Harrington; Lori M B Laffel
Journal:  Curr Diab Rep       Date:  2013-12       Impact factor: 4.810

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Journal:  BMJ       Date:  2004-05-15

Review 8.  Predictors of self-management in pediatric type 1 diabetes: individual, family, systemic, and technologic influences.

Authors:  Diana Naranjo; Shelagh Mulvaney; Maureen McGrath; Theresa Garnero; Korey Hood
Journal:  Curr Diab Rep       Date:  2014       Impact factor: 4.810

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Authors:  R Jeffery; E Iserman; R B Haynes
Journal:  Diabet Med       Date:  2013-02-28       Impact factor: 4.359

10.  Automated integration of continuous glucose monitor data in the electronic health record using consumer technology.

Authors:  Rajiv B Kumar; Nira D Goren; David E Stark; Dennis P Wall; Christopher A Longhurst
Journal:  J Am Med Inform Assoc       Date:  2016-03-27       Impact factor: 4.497

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  1 in total

1.  Pharmacoadherence: An Opportunity for Digital Health to Inform the Third Dimension of Pharmacotherapy for Diabetes.

Authors:  David C Klonoff; Jennifer Y Zhang; Trisha Shang; Chhavi Mehta; David Kerr
Journal:  J Diabetes Sci Technol       Date:  2020-12-08
  1 in total

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