Digital interventions to improve safety and quality of inpatient diabetes management: a systematic review
Abstract
Importance Diabetes is common amongst hospitalised patients and contributes to increased length of stay and poorer outcomes. Digital transformation, particularly the implementation of electronic medical records (EMRs), is rapidly occurring across the healthcare sector and provides an opportunity to improve the safety and quality of inpatient diabetes care. Alongside this revolution has been a considerable and ongoing evolution of digital interventions to optimise care of inpatients with diabetes including optimisation of EMRs, digital clinical decision support systems (CDSS) and solutions utilising data visibility to allow targeted patient review. Objective To systematically appraise the recent literature to determine which digitally-enabled interventions including EMR, CDSS and data visibility solutions improve the safety and quality of non-critical care inpatient diabetes management. Methods Pubmed, Embase and Cochrane databases were searched for suitable articles. Selected articles underwent quality assessment and analysis with results grouped by intervention type. Results 1202 articles were identified with 42 meeting inclusion criteria. Four key interventions were identified; computerised physician order entry (n = 4), clinician decision support systems (n = 21), EMR driven active case finding (data visibility solutions) and targeted patient review (n = 10) and multicomponent system interventions (n = 7). Studies reported on glucometric outcomes, evidence-based medication…
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?
Systematic reviews of randomized controlled trials using computerized assistance to improve glycemic outcomes in the hospital found significant improvement in the percentage of time individuals spent in the glycemic goal range, lower mean blood glucose levels, and no increase in hypoglycemia.
“Systematic reviews of randomized controlled trials using computerized assistance to improve glycemic outcomes in the hospital found significant improvement in the percentage of time individuals spent in the glycemic goal range, lower mean blood glucose levels, and no increase in hypoglycemia (11).”
From 16. Diabetes Care in the Hospital: Standards of Care in Diabetes—2026 (2026) · cites this paper as [11]
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 95 · Cited by 36
- Authors
- Sly, B., Russell, A. W., & Sullivan, C.
- Affiliations
- The University of Queensland (AU)
- Corresponding author
- Sly, Benjamin
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 157
- Pages
- 104596
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W3203684078
- Cited by
- 36
- References
- 95
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 8 · 2023: 7 · 2022: 3
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Metro South Hospital and Health Service
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- inpatient diabetes management, digital interventions, electronic health records, clinical decision support systems, computerized physician order entry, data visibility, active case finding, medication errors, insulin prescribing, glycemic control, patient safety, quality of care, length of hospital stay
- MeSH terms
- Diabetes Mellitus, Humans, Inpatients, Decision Support Systems, Clinical, Medical Order Entry Systems, Electronic Health Records
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