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Detecting spiritual pain in electronic medical records using large language models
- Truvace record
- TRV-2026-2499raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
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PURPOSE: Spiritual pain is one dimension of total pain in terminally ill patients with cancer. Reducing the burden of assessing spiritual pain in electronic medical records is essential for improving the quality of spiritual care. Large language models may offer a solution. This study explored whether large language models can be used to develop an automatic system for accurately identifying spiritual pain in patients with terminal cancer. METHODS: We used unstructured nursing records from patients with cancer w…
53Gain side
How the two sides scoreClose call
51Problem side
The gain
In a study of 500 nursing records from 109 dying cancer patients in Japan, Gemini and Copilot each identified all 56 records with spiritual pain (sensitivity 1.00), with negative predictive value of 1.00.
The problem
In the same study, Gemini's positive predictive value was only 0.34 and Copilot's 0.25, and Gemini produced 111 false positives, often mistaking declining physical function or delirium for spiritual pain.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
The study used records from one Japanese university hospital, only two models, and 500 pre-filtered records from 109 patients, which the authors say may overestimate real-world performance.
Sources
1 on file- Supportive Care in Cancerdoi:10.1007/s00520-026-11416-0
Cite this record
v1Truvace Impact Record TRV-2026-2499, v1: “Detecting spiritual pain in electronic medical records using large language models.” Truvace, 2026-11-12. /record/TRV-2026-2499 (accessed on the day you copy it). sha256 ff04ebcdc98ad80d…
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