A scoping review of digital solutions in diabetes outpatient care: Functionalities and outcomes
Bibliographic record
Abstract
BACKGROUND: Digital interventions are increasingly used in outpatient diabetes care to address growing healthcare demands and workforce limitations. This study investigates the functionalities of digital solutions and their impact on Quadruple Aim outcomes: enhancing population health, improving patient experience, supporting clinician well-being, and reducing healthcare costs. METHODS: We followed Joanna Briggs Institute guidelines, searching PubMed, Embase, Cochrane, Scopus, and Web of Science (January 2019-February 2024). Included studies reported digital diabetes interventions with outcomes directly relevant to the Quadruple Aim. Each intervention was mapped to a digital solution horizon: Horizon 1 involves foundational digital workflows; Horizon 2 leverages real-time data to create analytics; Horizon 3 encompasses transformative uses, such as predictive analytics. RESULTS: We identified 4,397 articles with 56 meeting the inclusion criteria. Interventions included telehealth (n = 15), mobile health (mHealth) (n = 20), combined telehealth and mHealth (n = 14), robotics (n = 1), electronic medical records (n = 1), and artificial intelligence (n = 5). Most interventions (n = 51) were categorised as Horizon 1, with 10 adopting Horizon 2, 5 using Horizon 3, and 10 spanning multiple horizons. Regarding Quadruple Aim outcomes, 44 studies addressed population health (41 positive), 31 targeted patient experience (29 positive), 4 focused on clinician well-being (3 positive), and 6 on cost reduction (4 positive). CONCLUSION: Digital solutions have demonstrated measurable benefits, particularly in population health and patient experience. Most interventions remain at Horizon 1. Advancing these digital solutions to Horizon 2 and 3 is essential for system-wide transformation. Future research should include cost efficiency and clinician experience alongside evaluations of population health and patient experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.031 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".