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Clinician Perspectives on Automated Facial Analysis for Pain Assessment: A Cross-Sectional Survey of Physicians, Advanced Practice Providers, and Nurses
Mullins, C. S.; Espelien, C.; Gurka, M.; Chernyavskiy, P.. medRxiv (preprint), 2026-09-23
Topic: Risk, prevention and care in population studies
Pain assessment remains challenging in clinical practice. Automated pain assessment using artificial intelligence (AI) and facial expression analysis has emerged as a potential approach to support more objective and continuous pain monitoring, particularly for patients who are unable to communicate effectively. However, successful implementation depends on clinician acceptance. We conducted a cross-sectional survey of physicians, advanced practice providers (APPs), and nurses at a tertiary academic medical center to evaluate current pain assessment practices and perceptions of a facial-analysis-based pain assessment system. Eighty-seven eligible clinicians participated. For current assessment practices, clinicians commonly relied on observational indicators of pain, including vital signs (88.5%), physical examination findings (85.1%), and facial expressions (79.3%). Attitudes toward automated pain assessment were generally neutral to positive. Half of respondents agreed that such a system would be useful for monitoring patients over time (50.6%), and many perceived value for nonverbal patients (86.5%), patients with dementia or cognitive impairment (78.4%), and patients with language barriers (62.2%). Most clinicians reported requiring high levels of accuracy before routine use, with nearly 60% requiring at least 90% accuracy. Key concerns included alert fatigue and unequal performance across patient populations. These findings suggest clinicians view automated facial-analysis systems as potentially valuable adjuncts for pain monitoring, particularly in communication-impaired populations, while emphasizing the importance of accuracy, fairness, explainability, and workflow integration during system development and implementation.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Mullins, C. S., Espelien, C., Gurka, M., & Chernyavskiy, P. (2026). Clinician Perspectives on Automated Facial Analysis for Pain Assessment: A Cross-Sectional Survey of Physicians, Advanced Practice Providers, and Nurses. medRxiv (preprint). https://doi.org/10.64898/2026.09.21.26363597
Vancouver
Mullins CS, Espelien C, Gurka M, Chernyavskiy P. Clinician Perspectives on Automated Facial Analysis for Pain Assessment: A Cross-Sectional Survey of Physicians, Advanced Practice Providers, and Nurses. medRxiv (preprint). 2026. doi:10.64898/2026.09.21.26363597.
BibTeX
@article{mullins2026-4d14b03b,
author = {Mullins, C. S. and Espelien, C. and Gurka, M. and Chernyavskiy, P.},
title = {{Clinician Perspectives on Automated Facial Analysis for Pain Assessment: A Cross-Sectional Survey of Physicians, Advanced Practice Providers, and Nurses}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.09.21.26363597},
url = {https://doi.org/10.64898/2026.09.21.26363597}
}
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
- These findings suggest clinicians view automated facial-analysis systems as potentially valuable adjuncts for pain monitoring, particularly in communication-impaired populations, while emphasizing the importance of accuracy, fairness, explainability, and workflow integration during system development and implementation.
Why it matters
Clinicians surveyed said automated facial-analysis systems could be useful for monitoring pain in patients who cannot communicate well, including those with dementia. However, most wanted high accuracy before routine use, and concerns about alert fatigue and fairness were raised, so this is about developing a tool, not treating Alzheimer's.
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Study details
- Study designcross-sectional
- Effect size88.5%; 85.1%; 79.3%
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 26 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for sample size, studied in people and randomized / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 24 of the 94 points we could judge it on, which is 26 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Pavel Chernyavskiy: h-index 20 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Virginia: institution h-index 1,118 (OpenAlex) |
| Multicenter collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 24 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 24 of the 94 points we could judge it on is 26 out of 100. |
| Total | 26 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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