Exploring Perspectives of Health Care Professionals on AI in Palliative Care: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: The use of artificial intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routinely collected data from electronic health records; however, there are few data on the views of palliative care health care professionals on the role of AI in practice. Determining the opinions of palliative care health care professionals on the potential uses of AI in palliative care will be useful for policymakers and practitioners to determine and inform the meaningful use of AI in palliative care practice. OBJECTIVE: This study aimed to explore the views of palliative care health care professionals on the use of AI for the analysis of patient data in palliative care. METHODS: This was a phenomenological study using qualitative semistructured interviews with palliative care health care professionals with a minimum of 1 year of clinical experience in a hospice in the North West of England. Data were analyzed using inductive thematic analysis. RESULTS: We interviewed 6 palliative care professionals, including physicians, nurses, and occupational therapists. AI was viewed positively, although most participants had not used it in practice. None of the participants had received training in AI and stated that education in AI would be beneficial. Participants described the potential benefits of AI in palliative care, including the identification of people requiring palliative care interventions and the evaluation of patient experiences. Participants highlighted security and ethical concerns regarding AI related to data governance, efficacy, patient confidentiality, and consent issues. CONCLUSIONS: This study highlights the importance of staff perceptions of AI in palliative care. Our findings support the role of AI in enhancing care, addressing educational needs, and tackling trust, ethics, and governance issues. This study lays the groundwork for guidelines on AI implementation, urging further research on the methodological, ethical, and practical aspects of AI in palliative care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".