Implementing Clinical Practice Guidelines for Screening and Detection of Delirium in a 21-Hospital System in Northern California
Bibliographic record
Abstract
PURPOSE: The purpose of this article was to describe a quality improvement process on a diverse adult intensive care unit (ICU) population for a large healthcare organization for early detection of delirium. BACKGROUND: Delirium is often considered a common unpreventable problem in the ICU. A process for early detection of delirium allows the critical care team to evaluate the patient and intervene to improve or reverse the delirium. DESCRIPTION: A business case was first developed, and then using performance improvement methodology combined with quality improvement methods and oversight from a Delirium/Sedation Workgroup, an implementation plan was developed. Intensive care clinical nurse specialists were educated; patients in the ICU were screened for delirium twice daily by bedside nurses using the Confusion Assessment Method. The clinical nurse specialist in each ICU was instrumental for driving the process of change and supporting the bedside nurse and physicians to discuss preventing, screening, and treating delirium. OUTCOME: System-wide process implementation was completed in 1 year, 2011. In 2012, all medical centers had a program in place to decrease the use of benzodiazepines and improve communication in the multidisciplinary teams during daily rounds about the treatment and prevention of delirium. The process of performance improvement is ongoing with continual reassessment and feedback required to ensure sustainability. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: Performance improvement involving 21 medical centers is a large-scale undertaking by an organization. It requires a systematic approach with key stakeholders and advanced practice nurses as subject matter experts involved throughout all phases of the implementation. Bedside clinicians assessing the patient must feel supported and valued members of the process. Challenges of all care providers need to be acknowledged and addressed.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 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".