Assessing the Benefit of a Digital Pain Alert System in a Community Hospital
Bibliographic record
Abstract
Introduction: In the last decade, clinical alert systems were developed for clinical use, including patient deterioration and other urgent clinical situations. However, investigations focusing on digital pain alert systems to assess and manage pain on time in in-hospital patients are scarce. The objective of the study was to assess the implementation of digital pain alerts in the various departments of a community hospital. Methods: Administrative data from the year 2020 to 2023 were collected. Only data from cases when pain alert was activated were included. Data included pain alert activation frequency, pain alert response duration (time from pain alert activation to alert stop when medication was administered), the department from which the pain alert was activated. Results: There was a steady significant decrease in the mean pain alert response duration over time from 2020 to 2023. The department with the shortest pain alert response duration was the Department of Surgery, and Cardiology (252.76 ± 4.712). The longest time delay was in the ICU (463.27±2.73 min) and at the Mental Health Department (440.59± 5.46 min) (p < 0.01). The pain alert response duration gradually decreased from 2020 to 2023, with decreases from the first year in all of the units/departments, except for the ICU. NPS scores at the start of the alert to 30 minutes after alert stop decreased significantly. Conclusion: Management of pain improved over time across the hospital, and in most of the departments, suggesting that with experience, digital pain alert systems have the potential to improve pain management by providing timely pain intervention.
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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 | 0.042 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".