Evaluation of a program using a physician assistant and an electronic patient–provider communication tool to facilitate discussions about goals of care in older adults in hospital: a pilot study
Bibliographic record
Abstract
BACKGROUND: Many patients receive unwanted, low-value, high-intensity care at the end of life because of poor communication with health care providers. Our aim was to evaluate the feasibility of using a physician assistant and an electronic tool to facilitate discussions about goals of care. METHOD: We conducted a pilot study for the intervention (physician assistant-led discussion using an electronic tool) from Apr. 1 to Aug. 31, 2019. Patients aged 79 years or older admitted to the Royal Victoria Hospital (Barrie, Ontario) with either (i) no documented resuscitation preferences or (ii) a request for life-sustaining treatments in the event of a life-threatening illness were eligible for the intervention. The goal of this study was to complete more than 30 interventions. The primary outcomes included the proportion of consenting eligible patients, the time required and the proportion of patients changing their resuscitation preferences. RESULTS: A total of 763 patients met the inclusion criteria, with 337 eligible for the intervention. Of these, 49 cases were contacted for consent, and 37 interventions were completed (75.5%, 95% confidence interval [CI] 61.1%-86.6%). On average, the intervention required 50 minutes (standard deviation 21) to complete. Overall, 31 interventions resulted in a change in resuscitation preferences (83.7%, 95% CI 68.0%-93.8%), with 22 patients choosing to forgo any access to life-sustaining treatments in the event of a life-threatening illness (59.4%, 95% CI 42.1%-75.2%). INTERPRETATION: In this pilot study, the intervention was completed in a minority of eligible patients and required substantial time; however, it led to many changes in resuscitation preferences. Before designing a study to evaluate its impact, the intervention needs to be revised to make it more efficient to administer.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".