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Predicting hospice eligibility among dementia patients using language models
Thomas H. McCoy, Roy H. Perlis. Alzheimer s & Dementia, 2025-11-01
Topic: Risk, prevention and care in population studies
Of 9872 individuals, 3563 (36%) died within 6 months. GPT-4o predictions stratified risk of death within 6 months (log-rank p < 0.001, area under the curve [AUC] = 0.79); predictions were strongly associated with mortality in Cox regression models (adjusted hazard ratio [aHR] = 31.02 95% confidence interval [CI] 27.44-35.08, p < 0.001) with similar results between sites. GPT-4o can stratify mortality risk using routinely generated documentation, potentially facilitating hospice referral decisions, but more prospective work is needed. HIGHLIGHTS: Large language models (LLMs) can estimate 6-month mortality in patients with dementia. GPT-4o estimates of mortality risk from discharge summaries were highly discriminative (area under the curve [AUC] = 0.79). Predictions may support hospice referral decisions.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
McCoy, T. H., & Perlis, R. H. (2025). Predicting hospice eligibility among dementia patients using language models. Alzheimer s & Dementia. https://doi.org/10.1002/alz.70878
Vancouver
McCoy TH, Perlis RH. Predicting hospice eligibility among dementia patients using language models. Alzheimer s & Dementia. 2025. doi:10.1002/alz.70878. PMID: 41216867.
BibTeX
@article{mccoy2025-41216867,
author = {McCoy, Thomas H. and Perlis, Roy H.},
title = {{Predicting hospice eligibility among dementia patients using language models}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz.70878},
pmid = {41216867},
url = {https://doi.org/10.1002/alz.70878}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- Of 9872 individuals, 3563 (36%) died within 6 months.
- GPT-4o predictions stratified risk of death within 6 months (log-rank p < 0.001, area under the curve [AUC] = 0.79); predictions were strongly associated with mortality in Cox regression models (adjusted hazard ratio [aHR] = 31.02 95% confidence interval [CI] 27.44-35.08, p < 0.001) with similar results between sites.
- GPT-4o can stratify mortality risk using routinely generated documentation, potentially facilitating hospice referral decisions, but more prospective work is needed.
- HIGHLIGHTS: Large language models (LLMs) can estimate 6-month mortality in patients with dementia.
- GPT-4o estimates of mortality risk from discharge summaries were highly discriminative (area under the curve [AUC] = 0.79).
Why it matters
This study found that GPT-4o could stratify 6-month mortality risk among patients with dementia using routinely generated documentation, with an area under the curve of 0.79. It suggests this could potentially facilitate hospice referral decisions, but the authors state that more prospective work is needed.
Study details
- Participants9,872
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 3 / 4 | 9,872 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Roy H. Perlis: h-index 107 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41216867) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
What each of these 20 components measures, and what this score is not →
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