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 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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".