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Cited by 44Diabetes Management and ResearchDiabetes Management and EducationChronic Disease Management Strategies

Predicting and preventing acute care re-utilization by patients with diabetes

Open access
Rubin, Daniel J., Shah, Arnav A.·2021·Current Diabetes Reports
DOI 10.1007/s11892-021-01402-7PMC PMC8418292
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Abstract

Purpose of review Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization. Recent findings Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.

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  • Past ED visit frequency emerged as the strongest predictor, consistent with literature on recurrent acute care utilization and likely reflecting underlying health instability, insufficient outpatient follow-up, and barriers to primary care access.

    “Past ED visit frequency emerged as the strongest predictor, consistent with literature on recurrent acute care utilization and likely reflecting underlying health instability, insufficient outpatient follow-up, and barriers to primary care access [2,9].”

    From Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health (2026) · cites this paper as [9]

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Research works printed but not linked: 3

  1. [16]Ostling S Wyckoff J Ciarkowski SL The relationship between diabetes mellitus and 30-day readmission rates Clin Diab Endocrinol 2017 3 1 1 8 PMC5472001 28702257
  2. [21]Sarthak SS, Tripathi SP EmbPred30: Assessing 30-days readmission for diabetic patients using categorical embeddings. arXiv. 2020;2002.11215v1
  3. [67]Magny-Normilus C, Nolido NV, Borges JC, et al. Effects of an intensive discharge intervention on medication adherence, glycemic control, and readmission rates in patients with type 2 diabetes. J Patient Saf. 2021;17(2):73-80. PMC7647006 31009408

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Authors
Rubin, D. J., & Shah, A. A.
Affiliations
Temple University (US)
Corresponding author
Rubin, Daniel J.
ORCID
Venue
Current Diabetes Reports
Publisher
Springer Science and Business Media LLC
Published
2021-09
Volume
21
Issue
9
Pages
34-34
ISSN
  • 1534-4827 (ISSN-L)
  • 1539-0829
DOI
10.1007/s11892-021-01402-7
PMC
PMC8418292
OpenAlex
W3198048275
Cited by
44
References
64
Citations by year
2026: 11 · 2025: 8 · 2024: 8 · 2023: 6 · 2022: 10 · 2021: 1
FWCI
0.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Current Diabetes Reports (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK122073)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
Keywords
diabetes mellitus, hospital readmission, emergency department utilization, readmission prediction, risk factors, care transitions, post-discharge follow-up, telephone follow-up, COVID-19 pandemic, machine learning, electronic health records, health care costs, quality of care
MeSH terms
COVID-19, SARS-CoV-2, Diabetes Mellitus, Humans, Length of Stay, Patient Discharge, Patient Readmission, Retrospective Studies

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