Use of Standardized Assessment Tools to Improve the Effectiveness of Palliative Care Rounds
Bibliographic record
Abstract
BACKGROUND: Optimal care for patients in the palliative care setting requires effective clinical teamwork. Communication may be challenging for health-care workers from different disciplines. Daily rounds are one way for clinical teams to share information and develop care plans for patients. OBJECTIVE: The objective of this initiative was to improve the structure and process of daily palliative care rounds by incorporating the use of standardized tools and improved documentation into the meeting. We chose a quality improvement (QI) approach to address this initiative. Our aims were to increase the use of assessment tools when discussing patient care in rounds and to improve the documentation and accessibility of important information in the health record, including goals of care. METHODS: This QI initiative used a preintervention and postintervention comparison of the outcome measures of interest. The initiative was tested in a palliative care unit (PCU) over a 22-month period from April 2014 to January 2016. Participants were clinical staff in the PCU. RESULTS: Data collected after the completion of several plan-do-study-act cycles showed increased use and incorporation of the Edmonton Symptom Assessment System and Palliative Performance Scale into patient care discussions as well as improvement in inclusion of goals of care into the patient plan of care. CONCLUSION: Our findings demonstrate that the effectiveness of daily palliative care rounds can be improved by incorporating the use of standard assessment tools and changes into the meeting structure to better focus and direct patient care discussions.
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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 | 0.057 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".