Can a "good death" be made better?: A preliminary evaluation of a patient-centred quality improvement strategy for severely ill in-patients
Bibliographic record
Abstract
BACKGROUND: Prior studies attempting to improve end-of-life care have focused on specific outcomes deemed important to healthcare providers, with disappointing results. Improvement may be best achieved by identifying concerns important to individual patients, communicating the patients' concerns to the treating medical team, and repeating the process frequently until all concerns are addressed. Our objective was to conduct a preliminary evaluation of this innovative patient-centred quality improvement strategy. METHODS: Initial interviews elicited participants' ideas for improvement, which were then fed back to health care providers by the study investigator. A rapid-cycle change model ensured frequent reassessment and continued feedback. The study involved 36 seriously ill, hospitalized patients on teaching general medical inpatient units of a tertiary care hospital. The main outcome measure was participants' ratings of satisfaction within different domains of care on follow-up interviews. RESULTS: The proportion of participants who rated various aspects of their care as "excellent" or "very good" on initial interview was 72% for overall care, 64% for symptom control, 66% for level of support, and 75% for discussions about life sustaining treatments. Patients and families identified many actionable steps for improvement such as; better control of pain and shortness of breath, better access to physicians and medical information, more help with activities of daily living, improving the patient's environment, and shorter waits for nursing care, diagnosis, and treatment. Following feedback to the clinical team, participants reported improvement in overall care (32%), symptom control (44%), and support (40%). Only a minority had further discussions about life sustaining treatments. CONCLUSION: A patient-centred approach using rapid-cycle change was feasible and shows promise for improving the quality of end-of-life care. It should be evaluated on a larger sample in a controlled trial.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".