Effectiveness of a Diabetes-Focused Electronic Discharge Order Set and Postdischarge Nursing Support Among Poorly Controlled Hospitalized Patients: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND While electronic order sets have become standard practice for inpatient diabetes management, there is limited decision support at discharge. OBJECTIVE This study assessed whether an electronic discharge order set (DOS) plus nurse follow up calls improves discharge orders and post-discharge outcomes among hospitalized patients with type 2 diabetes (T2D). METHODS This is a randomized open label single center study comparing an electronic DOS and nurse phone calls to enhanced standard care (ESC) in hospitalized insulin-requiring patients with T2D. The primary outcome was change in HbA1c at 24 weeks post-discharge. Secondary outcomes included completeness and accuracy of discharge prescriptions related to diabetes. RESULTS The study was stopped early due to feasibility concerns related to long-term follow-up. However a total of 158 subjects were enrolled (DOS=82, ESC=76), 155 of whom had discharge data. The DOS group had a greater frequency of prescriptions for bolus insulin (81% vs 44%; P=0.01), needles/syringes (95% vs 63%; P=0.03), and glucometers (86% vs 36%; P=0.0002). Clarity of orders was similar. HbA1c was available in 27 subjects in each arm at 12 weeks, and 20/21 subjects in the DOS/ESC arms at 24 weeks. The adjusted difference in change in HbA1c (DOS-ESC) was -0.5 ± 0.4% at 12 weeks (P = 0.20) and -0.7 ± 0.4% at 24 weeks (P= 0.09). Achievement of individualized HbA1c target was greater in the DOS group at 12 weeks but not 24 weeks. CONCLUSIONS A DOS resulted in more complete discharge prescriptions. Assessment of post-discharge outcomes was limited due to loss of long-term follow-up but suggests possible benefit in glucose control. CLINICALTRIAL NCT03455985 Effectiveness of a Diabetes Focused Discharge Order Set Among Poorly Controlled Hospitalized Patients Transitioning to Glargine U300 Insulin
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".