Patient-Driven Diabetes Care of the Future in the Technology Era
Open accessAbstract
The growing burden of diabetes is fueled by obesity-inducing lifestyle behaviors including high-calorie diets and lack of physical activity. Challenges in access to diabetes specialists and educators, low adherence to medications, and inadequate motivational support for proper disease self-management contribute to poor glycemic control in patients with diabetes. Simultaneously, high patient volumes and low reimbursement rates limit physicians' time spent on lifestyle behavior counseling. These barriers to efficient diabetes care lead to high rates of diabetes-related complications, driving healthcare costs up and reducing the quality of patients' lives. Considering recent advancements in healthcare delivery technologies such as smartphone applications, telemedicine, m-health, device connectivity, machine-learning technology, and artificial intelligence, there is significant opportunity to achieve better efficiency in diabetes care and increase patient involvement in diabetes self-management, which ultimately may put an end to soaring diabetes-related healthcare expenditures. This review explores the patient-driven diabetes care of the future in the technology era.
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The emergence of DHTs, especially AI, may help address obstacles and alleviate disease burden of diabetes in the future because AI-based DHTs could help implement better prevention strategies, manage patients unable to attend appointments, deliver real-time health information, promote self-management, and save time and money.
“The emergence of digital health technologies (DHTs), especially artificial intelligence (AI), may help address these obstacles and alleviate the disease burden of diabetes in the future, because AI-based DHTs in diabetes care could help implement better prevention strategies for high-risk populations, manage diabetic patients who are unable to attend physician appointments in person, deliver real-”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [8]
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References 96 · Cited by 111
- Authors
- Ashrafzadeh, S., & Hamdy, O.
- Affiliations
- Joslin Diabetes Center (US)
- Corresponding author
- Hamdy, Osama
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 29
- Issue
- 3
- Pages
- 564-575
- ISSN
- 1550-4131 (ISSN-L)
- OpenAlex
- W2893594082
- Cited by
- 111
- References
- 96
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 23 · 2023: 17 · 2022: 23 · 2021: 11 · 2020: 6 · 2019: 3
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Novo Nordisk
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- diabetes management, diabetes self-management, patient engagement, digital health technologies, telemedicine, mobile application, mobile health, artificial intelligence, machine learning, device connectivity, glycemic control, medication adherence, diabetes complications, health care costs
- MeSH terms
- Self-Management, Cell Phone Use, Diabetes Mellitus, Humans, Health Knowledge, Attitudes, Practice, Life Style, Obesity, Blood Glucose Self-Monitoring, Exercise, Remote Consultation, Cell Phone, Mobile Applications
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