Improving the efficiency of virtual insulin teaching for patients admitted to hospital through the COVID-19 pandemic: a quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Throughout the COVID-19 pandemic, many areas of medicine transitioned to virtual care. For patients with diabetes admitted to hospital, this included diabetes education and insulin teaching. Shifting to a virtual model of insulin teaching created new challenges for inpatient certified diabetes educators (CDE). OBJECTIVE: We advanced a quality improvement project to improve the efficiency of safe and effective virtual insulin teaching throughout the COVID-19 pandemic. Our primary aim was to reduce the mean time between CDE referral to successful inpatient insulin teach by 0.5 days. DESIGN, SETTING, PARTICIPANTS: We conducted this initiative at two large academic hospitals between April 2020 and September 2021. We included all admitted patients with diabetes who were referred to our CDE for inpatient insulin teaching and education. INTERVENTION: Alongside a multidisciplinary team of project stakeholders, we created and studied a CDE-led, virtual (video conference or telephone) insulin teaching programme. As tests of change, we added a streamlined method to deliver insulin pens to the ward for patient teaching, created a new electronic order set and included patient-care facilitators in the scheduling process. MAIN OUTCOME AND MEASURES: Our main outcome measure was the mean time between CDE referral and successful insulin teach-back. Our process measure was the percentage of successful insulin pen deliveries to the ward for teaching. As balance measures, we captured the percentage of patients with a successful insulin teach, the time between insulin teach and hospital discharge, and readmissions to hospital for diabetes-related complications. RESULTS: Our tests of change improved the efficiency of safe and effective virtual insulin teaching by 0.27 days. The virtual model appeared less efficient than usual in-person care. CONCLUSIONS: In our centre, virtual insulin teaching supported patients admitted to hospital through the pandemic. Improving the administrative efficiency of virtual models and leveraging key stakeholders remain important for long-term sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".