Use of Clinical Decision Support to Improve the Quality of Care Provided to Older Hospitalized Patients
Bibliographic record
Abstract
BACKGROUND: Frail older inpatients are at risk of unintended adverse events while in hospital, particularly falls, functional decline, delirium and incontinence. OBJECTIVE: The aim of this pragmatic trial was to pilot and evaluate a multi-component knowledge translation intervention that incorporated a nurse-initiated computerized clinical decision support tool to reduce harms in the care of older medical inpatients. METHODS: A stepped wedge trial design was conducted on six medical units at two hospitals in Calgary, Alberta, Canada. The primary quantitative outcome was the rate of order set use. Secondary outcomes included the number of falls, the average number of days in hospital, and the total number of consults ordered for each of orthopedics, geriatrics, psychiatry and physiotherapy. Qualitative analysis included interviews with nurses to explore barriers and facilitators around the implementation of the electronic decision support tool. RESULTS: The estimated mean rate of order set use over a 2 week period was 3.1 (95% CI 1.9-5.3) sets higher after the intervention than before. The estimated odds of a fall happening on a unit over a 2-week period was 9.3 (p = 0.065) times higher before than after the intervention. There was no significant effect of the intervention on length of hospital stay (p = 0.67) or consults to related clinical services (all p <0.2). Interviews with front-line nurses and nurse managers/educators revealed that the order set is not being regularly ordered because its content is perceived as part of good nursing care and due to the high workload on these busy medical units. CONCLUSIONS: Although not statistically significant, a reduction in the number of falls as a result of the intervention was noted. Frontline users' engagement is crucial for the successful implementation of any decision support tool. New strategies of implementation will be evaluated before broad dissemination of this knowledge translation intervention.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".