New Approaches to Improving Patient Safety: Strategy, Technology and Funding
Bibliographic record
Abstract
I n keeping with its commitment to provide practical medication safety solutions to hospitals throughout Canada, Cardinal Health brought together nationally recognized healthcare financial thought leaders to focus on the use of innovative technology to improve patient safety in Canadian acute care hospitals.The consensus of the financial healthcare thought leaders was that improving patient safety is most importantly "the right thing to do"; a good business case is an added bonus.Avoiding adverse events (AEs), including adverse drug events (ADEs), may improve efficiencies by avoiding unnecessary costs and extended lengths of stay.This can be especially important while a "window of opportunity" exists to increase new funding for Canadian hospitals by improving efficiency ratings using the Integrated Population-Based Allocation (IPBA) methodology.Participants also explored various possibilities for funding safety technology.This Executive Summary details the Key Points that emerged from conference presentations and discussion. Need for Improved Medication SafetyLength of stay -The Canadian Adverse Events Study showed that adverse events, including preventable adverse drug events (PADEs), are associated with increased length of stay.As described below, advanced technology is now available to avert highrisk medication errors -those most likely to lead to PADEs -and help avoid increased length of stay."Given the IPBA formula, your best bet to increase funding is to reduce your cost per case while increasing or at least maintaining your number of weighted cases.Reducing length of stay would be a major driver to actually achieve that."Dennis Biesaida, BBA, CMA Corporate Director of Finance and CFO, Grey Bruce Health Services High-risk medication errors -Several factors have increased the likelihood of error in Canadian hospitals.Along with higher patient acuity and lower staffing levels, recent years have seen dramatic increases in the numbers of medications used in treatment and the complexity of medication management.While some errors cannot be avoided, (e.g., an This project profile supported by an educational grant from Cardinal Health -Alaris Products *A detailed report with references, tables and appendices can be found at www.longwoods.com/jobsite/HQ83PatientSafety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".