19 Impact of early insulin starts on length of stay for hospitalized patients with diabetes
Bibliographic record
Abstract
Background Surrey Memorial Hospital (SMH) serves one of the fastest growing cities in Canada. 12% of Surrey’s population has diabetes compared to 8% in all of British Columbia with 3,455 new cases diagnosed each year. 67% of patients discharged from SMH have diabetes and have a longer length of stay (LOS). The lack of timely, effective insulin teaching and discharge planning was found to be one of the barriers to timely discharges. Objectives 1. To optimize the process and overcome barriers in teaching insulin and co-creating safe discharge. 2. To evaluate the impact of the new insulin teaching pathway on the length of stay with the aim to reduce the LOS from 12 to 11 days for inpatients newly started on insulin at SMH by September 30, 2020. Methods In 2018, a multidisciplinary group including nursing educators, pharmacist, patient partner, and an endocrinologist, collaborated with other stakeholders using Model for Improvement. We developed insulin teaching toolkit and the Insulin Teaching Patient Pathway, a streamlined, pre-printed order set that ensures staff have the tools needed for insulin teaching and follow-up at the diabetes center. Results LOS decreased from 12.4 to 8.7 days and showed sustainment through the COVID19 pandemic (figure 1). Conclusions The initial project goal to decrease LOS has been sustained at SMH. Additional outcomes have been spread to other hospitals, executive leadership recognition and support, and the hiring of a full-time inpatient educator. Most importantly, the empowerment of patients and staff regarding diabetes education and insulin administration. Executive leadership buy-in and the frontline team’s ‘pull’ system were key to success.
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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".