Optimizing the transfer of patient care information among nurses and members of the multidisciplinary team in a regional hospital in Northwestern Ontario / by Karina Gagalo.
Bibliographic record
Abstract
This study was designed to examine the current methods and content of shift report at a hospital in Northwestern Ontario. It also examined the potential of a computer-generated method of shift report to improve communication and transfer of patient information in shift report. The objectives were to enhance standardized communication between nurses \nand other members of the multidisciplinary team (MT), provide efficient and effective coordination of communication between nurses and other members of the MT, and examine factors that impact on patient safety through audits and personal observation. \nAction-oriented research was the framework o f the study and the methodology. Questionnaires were developed using evidence based-practice literature to obtain data about patient documentation and information transfer, and comments from the participants regarding shift report. Other data collection processes included focus group sessions as well as nonparticipatory observation, documentation audits and audiotaped \nhandover audits to assess the content transferred in shift report, along with content located in patient charts. The target population was 105 participants, an affiliation of registered nurse (RNs), registered practical nurses (RPNs), and members of the MT from three units at the hospital. Of the 105 potential participants, 62 nurses (RNs & RPNs) and 11 MT members participated in the study, providing a sample of 73 individuals. The \nfindings indicated that with the new computer-generated shift summaries, the transfer of patient care information has improved among nursing staff and the MT members. The findings showed that with the new system, there is a decrease in communication among nursing staff but an increase in communication between MT members and nursing staff. The findings indicated areas within the new system that can be enhanced to improve communication and patient information transfer. Recommendations to improve communication between nursing staff and members of the MT include use of wireless systems to replace current hard-standing computers which would increase the time spent with patients and decrease the number of errors with documentation through point-of-care documentation. As well, use of the phone system as a method of shift report would allow nurses to record their patient data when convenient for them, without having to be \nat a stationary computer. It also would allow other members of the MT to access the patient reports through the phone system and levels of passwords.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".