Comparing glycaemic outcomes of digital and paper‐based hospitals (<scp>GOOD</scp> study)
Bibliographic record
Abstract
BACKGROUND: Digital technologies in healthcare are seen as mechanisms to improve and optimise management of health conditions. AIM: To assess the impact of digitisation on clinical outcomes and medication errors for patients with diabetes. METHODS: This repeated cross-sectional study used data collected from the Queensland Inpatient Diabetes Survey (QuIDS), which was conducted in 2019 and 2021 at digital and paper-based hospitals in Queensland. Relevant data were collected from patients with diabetes admitted to participating hospitals during a single day of the study week. Outcomes and error rates of patients who were admitted to digital versus paper-based hospitals were compared. Regression determined the factors that contributed to 'good diabetes days' and 'no hypoglycaemic days'. Prescribing and management errors were compared. RESULTS: Data on 1942 patient admissions (6977 patient bed days) were collected. Of these, 1076 patient admissions (55%) were at a digital hospital, while 866 patient admissions (45%) were at a paper-based hospital. Using regression, it was found that being admitted to a digital hospital increased the odds of a 'good diabetes day' by 45% (P < 0.001), but there was no change in 'no hypoglycaemic days' (P = 0.183). There was a reduction in the proportion of patients with at least one error type across all error categories amongst those admitted to a digital hospital (P < 0.001). CONCLUSION: Admission to a digital hospital improves the odds of experiencing a good diabetes day but does not change the occurrence of hypoglycaemia. There are potential quality and safety considerations for those hospitals still delivering paper-based diabetes 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".