ArticleNPJ digital medicine2026
Automated algorithms for identifying patients requiring palliative care: a systematic review and meta‑analysis.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electronic health record (EHR)-based algorithms have been developed to identify patients with high palliative care needs and facilitate timely palliative care interventions. We systematically searched PubMed, Embase, and the Cochrane Library for randomized trials published up to December 2025. Pooled risk ratios (RRs) and weighted mean differences with 95% confidence intervals (CIs) were estimated using random-effects models. Risk of bias was assessed using Cochrane RoB 2.0, and certainty of evidence was rated with GRADE. Seven trials enrolling 125,666 patients were included. Automated EHR algorithms significantly increased palliative care consultations (noncancer: RR, 2.19; 95% CI, 1.12-4.28; cancer: RR, 5.31; 95% CI, 3.49-8.09) and do-not-resuscitate documentation (RR, 1.22; 95% CI, 1.17-1.28). Effects on hospice use and in-hospital mortality were marginal. No significant effects were observed for ICU admission, length of stay, or family-reported psychological outcomes. Implementation of automated EHR algorithms is associated with higher rates of palliative care consultations and advance care planning documentation, suggesting potential to enhance the timeliness and quality of end-of-life care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.