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Record W4415354408 · doi:10.2196/73485

Revolutionizing Hypoglycemia Management in Long-Term Care: Lessons Learned From a Pilot Quality Improvement Initiative Using Continuous Glucose Monitoring

2025· article· en· W4415354408 on OpenAlexaffvenue
Denis O’Donnell, Lisa Quesnelle, Benjamin Yuen

Bibliographic record

VenueJMIR Diabetes · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsExtendicare (Canada)Integrity Testing Laboratory (Canada)St Joseph's Health CareCARE Canada
Fundersnot available
KeywordsFingerstickHypoglycemiaWorkloadContinuous glucose monitoringQuality managementGlycemicBlood Glucose Self-MonitoringBlood glucose monitoringDiabetes management

Abstract

fetched live from OpenAlex

Unlabelled: Despite efforts to raise glycemic targets and reduce modifiable risk factors, hypoglycemia continues to impact a large number of long-term care (LTC) residents living with diabetes mellitus and remains one of the leading causes of hospitalization in this cohort. Effective, sustainable practice strategies to monitor and maintain glycemic control in LTC are lacking. We describe the stepwise approach used by 2 LTC homes that switched from traditional fingerstick testing to a continuous glucose monitoring (CGM) system as part of a quality improvement initiative to reduce nursing workload and address hypoglycemia. This was an exploratory pilot project. A working group was established at each of the 2 participating LTC homes, including representatives from management and direct care staff. Kickoff meetings were held with key direct care staff to discuss the limitations of current monitoring practices and potential solutions. The following interventions were agreed upon and implemented by the working groups: (1) the initiation of structured glucose monitoring for residents using CGM (FreeStyle Libre 2), requiring scanning of sensors 4 times per day; (2) provision of staff education and training on CGM by a diabetes expert; and (3) scheduling of interdisciplinary rounds as needed to optimize diabetes management. System changes were gradually introduced in a stepwise manner over a 3-month period (intervention phase), during which the LTC homes progressed from traditional fingerstick testing to point-of-care sensor readings and then to full use of the CGM software platform. Hypoglycemia was defined as a glucose reading of ≤4 mmol/L. Glucose readings were collected from 38 residents living with diabetes mellitus and receiving insulin in the 6 months before the start of the intervention phase (baseline evaluation) and in the 6 months after the end of the intervention phase (post-launch evaluation). All hypoglycemic readings detected by a sensor at a point-of-care test were validated using a fingerstick test. Nursing workload associated with glucose testing was assessed in an anonymous survey of nursing staff at baseline and after the launch. The approach resulted in a 40% reduction in nursing time required to complete a glucose reading (from 5.1 min per test at baseline to 3.1 min per test at the post-launch evaluation). The frequency of glucose monitoring increased from a total of 19,438 glucose readings in the baseline evaluation to 35,971 point-of-care sensor scans in the post-launch evaluation. The number of detected hypoglycemic events increased 12-fold, from 88 in the baseline evaluation to 1049 in the post-launch evaluation. Hypoglycemic events continue to impact a large number of LTC residents living with diabetes mellitus. CGM can improve the detection of hypoglycemic events while decreasing nursing workload. A gradual transition to CGM can help overcome underlying barriers and concerns and ensure a sustainable approach.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.400
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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