Decision-Supported Collaborative Care for Gestational Diabetes: A Narrative Review of Development Pathways and Evaluation in Routine Clinical Practice.
- 1Department of Endocrinology, The Second Hospital of Jilin University, Changchun, 130000, People's Republic of China.
Abstract
Gestational diabetes mellitus (GDM) requires frequent glucose monitoring, rapid behavior adjustment, and timely escalation within a short pregnancy window, yet routine care is often episodic and fragmented across teams. Digital tools have improved data capture, but many systems still stop at logging and messaging and do not consistently translate patient-generated data into clinical action. This review frames decision-supported collaborative care for GDM as a workflow-centered care model that links glucose and clinical context to interpretable decision logic and role-based team processes for triage, escalation, and closed-loop feedback. We conducted a structured narrative review of English-language studies indexed in PubMed/MEDLINE and Web of Science from database inception to 2026, supplemented by manual screening of relevant reference lists. Owing to substantial heterogeneity in intervention design, clinical settings, and reported outcomes, the evidence was synthesized narratively. We summarize key system components, outline a practical development pathway from defining actionable use cases to embedding safety guardrails and workflow integration, and propose a multidimensional evaluation approach that combines clinical outcomes with process, implementation, and system-level measures. We also discuss major barriers to scale, including alert burden, role accountability, data governance and privacy, and digital equity. This framework may support clinicians, healthcare organizations, and policymakers in designing, implementing, and evaluating safe, equitable, and workflow-compatible GDM services.
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BibTeX
@article{yan2026decisionsupported,
title = {{Decision-Supported Collaborative Care for Gestational Diabetes: A Narrative Review of Development Pathways and Evaluation in Routine Clinical Practice.}},
author = {Yan Yan and Maoguang Yang and Lijuan Wang and Nan Cui and Chongli Zhong},
journal = {Diabetes, metabolic syndrome and obesity : targets and therapy},
year = {2026},
month = oct,
doi = {10.2147/dmso.s621355},
url = {https://europepmc.org/article/MED/42953578},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Decision-Supported Collaborative Care for Gestational Diabetes: A Narrative Review of Development Pathways and Evaluation in Routine Clinical Practice.
AU - Yan Yan
AU - Maoguang Yang
AU - Lijuan Wang
AU - Nan Cui
AU - Chongli Zhong
PY - 2026
DA - 2026/10/23
JO - Diabetes, metabolic syndrome and obesity : targets and therapy
DO - 10.2147/dmso.s621355
UR - https://europepmc.org/article/MED/42953578
AB - Gestational diabetes mellitus (GDM) requires frequent glucose monitoring, rapid behavior adjustment, and timely escalation within a short pregnancy window, yet routine care is often episodic and fragmented across teams. Digital tools have improved data capture, but many systems still stop at logging and messaging and do not consistently translate patient-generated data into clinical action. This review frames decision-supported collaborative care for GDM as a workflow-centered care model that links glucose and clinical context to interpretable decision logic and role-based team processes for triage, escalation, and closed-loop feedback. We conducted a structured narrative review of English-language studies indexed in PubMed/MEDLINE and Web of Science from database inception to 2026, supplemented by manual screening of relevant reference lists. Owing to substantial heterogeneity in intervention design, clinical settings, and reported outcomes, the evidence was synthesized narratively. We summarize key system components, outline a practical development pathway from defining actionable use cases to embedding safety guardrails and workflow integration, and propose a multidimensional evaluation approach that combines clinical outcomes with process, implementation, and system-level measures. We also discuss major barriers to scale, including alert burden, role accountability, data governance and privacy, and digital equity. This framework may support clinicians, healthcare organizations, and policymakers in designing, implementing, and evaluating safe, equitable, and workflow-compatible GDM services.
SN - 1178-7007
ER -