Implementing a Diabetic Algorithm for Ophthalmology Surgery Patients: A Quality Improvement Initiative
Bibliographic record
Abstract
Introduction: The objective of this quality improvement, interventional study regarding patients with diabetes undergoing diabetic ophthalmology outpatient surgery aimed to develop, implement, and evaluate a new diabetic algorithm to improve safety, operating room efficiency, and decrease supply cost. Methods: A multidisciplinary study team was assembled, including ophthalmologists, endocrinologists, anesthesiologists, management, and nurses to review the current diabetic protocol. From August 2016 to July 2017, 13 patient safety concerns or incident reports were reviewed that identified two serious cases of hypoglycemia. Using the concerns data, frontline perspectives, and reviewing best practice guidelines, a new diabetic algorithm was developed and trialed for 24 months. The new algorithm limited the use of an existing preoperative insulin protocol and reduced the number of nurses required. The number of adverse events, nursing setup process steps, setup time, and preoperative insulin infusion protocols used were collected. An evaluation of the supply costs was performed. Results: After implementing the new diabetic algorithm, zero safety incidents were reported, and a 97.5% reduction in the use of preoperative insulin protocol resulted. Nursing staff perceived that the new diabetic algorithm was easier to configure, 23 minutes faster to set up, and required one nursing staff member. Supply cost was reduced by $30.63 (Canadian Dollars, CAD) per patient. Conclusion: Perioperative glucose irregularities may threaten patient safety and surgical outcomes. Healthcare professionals must improve patient safety, decrease healthcare expenditure, and prevent unnecessary delays. Multidisciplinary frontline staff experiential knowledge aided in the recognition of potential problems and comprehensive solutions to optimize patient care.
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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 | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".