Acceptability and Reliability of a Novel Palliative Care Screening Tool Among Emergency Department Providers
Bibliographic record
Abstract
BACKGROUND: The Palliative Care and Rapid Emergency Screening (P-CaRES) Project is an initiative intended to improve access to palliative care (PC) among emergency department (ED) patients with life-limiting illness by facilitating early referral for inpatient PC consultations. In the previous two phases of this project, we derived and validated a novel PC screening tool. This paper reports on the third and final preimplementation phase. OBJECTIVES: Examine the acceptability of the P-CaRES tool among PC and ED providers as well as test its reliability on case vignettes. Compare variations in reliability and acceptability of the tool based on ED providers' roles (attendings, residents, and nurses) and lengths of experience. METHODS: A two-part electronic survey was distributed to ED providers at multiple sites across the United States. We tested the reliability of the tool in the first part of the survey, through a series of case vignettes. A criterion standard of correct responses was first defined by consensus input from expert PC physicians' interpretations of the vignettes. The experts' input was validated using the Gwet's AC1 coefficient for inter-rater reliability. ED providers were then presented with the case vignettes and asked to use the P-CaRES tool to correctly identify which patients had unmet PC needs. ED provider responses were compared both against the criterion standard and against different subsets of respondents (divided both by role and by level of experience). The second part of the survey assessed acceptability of the P-CaRES tool among ED providers using responses to questions from a modified Ottawa Acceptability of Decision Rules Instrument, based on a 1-5 Likert rating scale. Descriptive statistics were used to report all outcomes. RESULTS: In total, 213 ED providers employed in three different regions across the country responded to the survey (39.4%) and 185 (86.9%) of those completed it. The majority of providers felt that the tool would be useful in their practice (80.5%), agreed that the tool was clear and unambiguous (87.1%), thought that use of the tool would likely benefit patients (87.5%), and thought that it would result in improved use of resources to help severely ill patients (83.6%). Over three-quarters of ED providers (78.5%) also self-reported that they refer patients with unmet PC needs less than 10% of the time, and only 10.8% of respondents believed that they are already utilizing an effective strategy to screen or refer patients to PC. Applying our P-CaRES tool to case vignettes, ED providers generated PC referrals in concordance with PC experts over 88.7% of the time (95% confidence interval = 86.4% to 90.6%), with an overall sensitivity of more than 90%. These results varied minimally regardless of the respondent's role in the ED or their level of experience. CONCLUSION: Screening by emergency medicine providers for unmet PC needs using a brief, novel, content-validated screening tool is acceptable and is also reliable when applied to case vignettes-regardless of provider role or experience. Clinical trial and further study are warranted and are currently under way.
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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 | 0.046 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".