Transition of Care for Patients with Diabetes
Abstract
Background Diabetes is a common chronic condition among adults that can complicate the transition from the hospital to the community. Hospital readmission is an important contributor to total medical expenditures and is an emerging indicator of quality of care. Failure to acknowledge diabetes transition of care is associated with increased emergency department visits and 30-day readmissions. Methods Literature review of transition of care models, sample tools and processes are presented. Updated guidelines and recommendations aiming to identify and address risk factors for readmission of patients with diabetes are provided. Results Increased attention has been given to different aspects of diabetes care in regards to discharge planning. This includes early initiation of a discharge plan identifying readmission risk factors at time of admission. In addition, involvement of patients, families, care givers, health care providers and institutions to establish transitional care. Utilization of hospital resources includes medication reconciliation, diabetes education, care coordination, discharge planning, follow up appointments and post discharge care. Conclusion Addressing transition of care is not a choice but an important quality of care marker. The transition of care determines where patients with diabetes will follow up and how payers will remunerate hospitals for management of diabetes during hospitalization, discharge planning process and readmission rates. Different transi…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The post-hospitalization period is critical for diabetes care: gaps in follow-up, medication mismanagement, or worsening conditions can precipitate acute complications and costly ED visits.
“The post-hospitalization period is critical for diabetes care: gaps in follow-up, medication mismanagement, or worsening conditions can precipitate acute complications and costly ED visits [7,10–12].”
From Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health (2026) · cites this paper as [11]
Cited by
Sorted by citations
Lee, S.-Y., Saleem, M., Land, A. M., DeLaney, E. W., Garretson, A. R., Patel, M., Hall, A. G., Westrick, S., Ovalle, F., Cherrington, A. L., et al. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106604
- Authors
- Garnica, P.
- Affiliations
- North Shore University Hospital (US)
- Corresponding author
- Garnica, Patricia
- Venue
- Current Diabetes Reviews
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2017-05-16
- Volume
- 13
- Issue
- 3
- Pages
- 263-279
- ISSN
- 1573-3998 (ISSN-L)
- OpenAlex
- W2584673777
- Cited by
- 18
- References
- 0
- Citations by year
- 2026: 2 · 2025: 3 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 1
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Diabetes Reviews (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
- Keywords
- diabetes mellitus, care transitions, hospital readmission, discharge planning, Transitional Care Model, risk factors for readmission, medication reconciliation, diabetes education, care coordination, patient engagement, quality of care
- MeSH terms
- Aftercare, Diabetes Mellitus, Humans, Patient Discharge, Patient Education as Topic, Patient Transfer, Medication Reconciliation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review