Adoption of Digital Health Technologies in the Practice of Behavioral Health: Qualitative Case Study of Glucose Monitoring Technology.
blood glucose self-monitoring
chronic disease
diabetes self-management
digital technology
mental illness
mobile phone
real-time systems
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
03 02 2021
03 02 2021
Historique:
received:
04
02
2020
accepted:
23
11
2020
revised:
05
08
2020
entrez:
3
2
2021
pubmed:
4
2
2021
medline:
18
5
2021
Statut:
epublish
Résumé
Evaluation of patients with serious mental illness (SMI) relies largely on patient or caregiver self-reported symptoms. New digital technologies are being developed to better quantify the longitudinal symptomology of patients with SMI and facilitate disease management. However, as these new technologies become more widely available, psychiatrists may be uncertain about how to integrate them into daily practice. To better understand how digital tools might be integrated into the treatment of patients with SMI, this study examines a case study of a successful technology adoption by physicians: endocrinologists' adoption of digital glucometers. This study aims to understand the key facilitators of and barriers to clinician and patient adoption of digital glucose monitoring technologies to identify lessons that may be applicable across other chronic diseases, including SMIs. We conducted focus groups with practicing endocrinologists from 2 large metropolitan areas using a semistructured discussion guide designed to elicit perspectives of and experiences with technology adoption. The thematic analysis identified barriers to and facilitators of integrating digital glucometers into clinical practice. Participants also provided recommendations for integrating digital health technologies into clinical practice more broadly. A total of 10 endocrinologists were enrolled: 60% (6/10) male; a mean of 18.4 years in practice (SD 5.6); and 80% (8/10) working in a group practice setting. Participants stated that digital glucometers represented a significant change in the treatment paradigm for diabetes care and facilitated more effective care delivery and patient engagement. Barriers to the adoption of digital glucometers included lack of coverage, provider reimbursement, and data management support, as well as patient heterogeneity. Participant recommendations to increase the use of digital health technologies included expanding reimbursement for clinician time, streamlining data management processes, and customizing the technologies to patient needs. Digital glucose monitoring technologies have facilitated more effective, individualized care delivery and have improved patient engagement and health outcomes. However, key challenges faced by the endocrinologists included lack of reimbursement for clinician time and nonstandardized data management across devices. Key recommendations that may be relevant for other diseases include improved data analytics to quickly and accurately synthesize data for patient care management, streamlined software, and standardized metrics.
Sections du résumé
BACKGROUND
Evaluation of patients with serious mental illness (SMI) relies largely on patient or caregiver self-reported symptoms. New digital technologies are being developed to better quantify the longitudinal symptomology of patients with SMI and facilitate disease management. However, as these new technologies become more widely available, psychiatrists may be uncertain about how to integrate them into daily practice. To better understand how digital tools might be integrated into the treatment of patients with SMI, this study examines a case study of a successful technology adoption by physicians: endocrinologists' adoption of digital glucometers.
OBJECTIVE
This study aims to understand the key facilitators of and barriers to clinician and patient adoption of digital glucose monitoring technologies to identify lessons that may be applicable across other chronic diseases, including SMIs.
METHODS
We conducted focus groups with practicing endocrinologists from 2 large metropolitan areas using a semistructured discussion guide designed to elicit perspectives of and experiences with technology adoption. The thematic analysis identified barriers to and facilitators of integrating digital glucometers into clinical practice. Participants also provided recommendations for integrating digital health technologies into clinical practice more broadly.
RESULTS
A total of 10 endocrinologists were enrolled: 60% (6/10) male; a mean of 18.4 years in practice (SD 5.6); and 80% (8/10) working in a group practice setting. Participants stated that digital glucometers represented a significant change in the treatment paradigm for diabetes care and facilitated more effective care delivery and patient engagement. Barriers to the adoption of digital glucometers included lack of coverage, provider reimbursement, and data management support, as well as patient heterogeneity. Participant recommendations to increase the use of digital health technologies included expanding reimbursement for clinician time, streamlining data management processes, and customizing the technologies to patient needs.
CONCLUSIONS
Digital glucose monitoring technologies have facilitated more effective, individualized care delivery and have improved patient engagement and health outcomes. However, key challenges faced by the endocrinologists included lack of reimbursement for clinician time and nonstandardized data management across devices. Key recommendations that may be relevant for other diseases include improved data analytics to quickly and accurately synthesize data for patient care management, streamlined software, and standardized metrics.
Identifiants
pubmed: 33533725
pii: v23i2e18119
doi: 10.2196/18119
pmc: PMC7889421
doi:
Substances chimiques
Blood Glucose
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e18119Informations de copyright
©Suepattra G May, Caroline Huber, Meaghan Roach, Jason Shafrin, Wade Aubry, Darius Lakdawalla, John M Kane, Felicia Forma. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.02.2021.
Références
J Psychiatr Res. 2019 Feb;109:126-132
pubmed: 30530207
Psychiatr Rehabil J. 2017 Sep;40(3):339-341
pubmed: 28891661
Rheumatol Ther. 2018 Jun;5(1):185-201
pubmed: 29470832
Med Devices (Auckl). 2017 Oct 04;10:237-251
pubmed: 29042823
Diabetes Care. 2015 Apr;38(4):544-50
pubmed: 25552422
NPJ Digit Med. 2019 Mar 22;2:18
pubmed: 31304366
Diabetes Technol Ther. 2005 Oct;7(5):770-5
pubmed: 16241880
Clin Psychol Psychother. 2014 Sep-Oct;21(5):427-36
pubmed: 23918764
JMIR Diabetes. 2017 Aug 16;2(2):e20
pubmed: 30291093
Diagnostics (Basel). 2013 Oct 29;3(4):385-412
pubmed: 26824930
Lancet Psychiatry. 2016 Jan;3(1):40-48
pubmed: 26620388
CNS Spectr. 2017 Apr;22(2):203-219
pubmed: 28421980
Nature. 2016 Apr 7;532(7597):20-3
pubmed: 27078548
JMIR Hum Factors. 2017 Dec 07;4(4):e31
pubmed: 29217504
Schizophr Res Treatment. 2012;2012:245103
pubmed: 23213525
Evid Based Ment Health. 2019 Feb;22(1):17-22
pubmed: 30559332
Endocr Pract. 2016 Aug;22(8):1008-21
pubmed: 27214060
PLoS Med. 2005 May;2(5):e151; quiz e175
pubmed: 15916460
BMC Endocr Disord. 2018 Feb 20;18(1):12
pubmed: 29458348
Neuropsychiatr Dis Treat. 2016 Oct 11;12:2587-2594
pubmed: 27785036
Eur Psychiatry. 2005 Dec;20(8):529-39
pubmed: 16171984
J Diabetes Sci Technol. 2017 May;11(3):484-492
pubmed: 28745093
J Diabetes Sci Technol. 2018 Sep;12(5):1090-1091
pubmed: 29808719
Diabetes Care. 2017 Feb;40(2):181-187
pubmed: 27899489
Patient Prefer Adherence. 2014 Feb 15;8:237-46
pubmed: 24627628
Br J Biomed Sci. 2012;69(2):83-93
pubmed: 22872934
JMIR Cardio. 2019 Mar 26;3(1):e11951
pubmed: 31758771
Diabet Med. 2011 Sep;28(9):1118-22
pubmed: 21692844
J Diabetes Sci Technol. 2016 Jun 28;10(4):852-8
pubmed: 27234809
Value Health. 2018 Sep;21(9):1069-1076
pubmed: 30224111
BMC Med Inform Decis Mak. 2016 Sep 15;16(1):120
pubmed: 27630020
Am J Manag Care. 2017 May 1;23(5):e156-e163
pubmed: 28810130
Patient Prefer Adherence. 2017 Jun 27;11:1071-1081
pubmed: 28721020