Implementation of an Electronic Data Collection Tool to Monitor Nursing-Sensitive Indicators in a Large Academic Health Sciences Centre.
Bibliographic record
Abstract
BACKGROUND: Monitoring the quality of nursing care is essential to identify patients at risk, measure adherence to hospital policies and evaluate the effectiveness of best practice interventions. However, monitoring nursing-sensitive indicators (NSI) is a challenge. Prevalence surveys are one method used by some organizations to monitor NSI, which are patient outcomes that are directly affected by the quantity or quality of nursing care that the patient receives. OBJECTIVE: The aim of this paper is to describe the development of an innovative electronic data collection tool to monitor NSI. METHODS: In the preliminary development work, we designed a mobile computing application with pre-populated patient census information to collect the nursing quality data. In subsequent phases, we refined this process by designing an electronic trigger using The Ottawa Hospital's Patient Safety Learning System, which automatically generated a case report form for each inpatient based on the hospital's daily patient census on the day of the prevalence survey. OBSERVATIONS: Both of these electronic data collection tools were accessible on tablet computers, which substantially reduced data collection, analysis and reporting time compared to previous paper-based methods. The electronic trigger provided improved completeness of the data. CONCLUSION: This work leveraged the use of tablet computers combined with a web-based application for patient data collection at point of care. Overall, the electronic methods improved data completeness and timeliness compared to traditional paper-based methods. This initiative has resulted in the ability to collect and report on NSI organization-wide to advance decision-making support and identify quality improvement opportunities within the organization.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".