Passive AI Detection of Stress and Burnout Among Frontline Workers.
Background: Burnout is a widespread concern across frontline professions, with healthcare, education, and emergency services workers experiencing particularly high rates of stress and emotional exhaustion. Passive artificial intelligence (AI) technologies may provide novel means to monitor and predi...
| Publicado en: | Nursing Reports Vol. 15; no. 11; pp. 373 - 395 |
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| Autores principales: | , , , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
MDPI
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189677031&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189677031 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2039439X EGNT jtl: Nursing Reports issn: 2039439X maglogo: N pubinfo: dt: Nov2025 vid: 15 iid: 11 pid: 97109 pub: MDPI artinfo: ui: 189677031 189677031 189677031 10.3390/nursrep15110373 189677031 ppf: 373 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Passive AI Detection of Stress and Burnout Among Frontline Workers. aug: au: Rana, Rajib Higgins, Niall Stedman, Terry March, Sonja Gucciardi, Daniel F. Barua, Prabal D. Joshi, Rohina affil: School of Mathematics, Physics and Computing, University of Southern Queensland, Ipswich, QLD 4300, Australia sug: subj: Frontline Personnel Psychosocial Factors Stress, Occupational Diagnosis Burnout, Professional Diagnosis Artificial Intelligence Biological Markers Workflow Electronic Health Records Heart Rate Heart Rate Variability Artificial Intelligence, Generative Natural Language Processing Occupational Health ab: Background: Burnout is a widespread concern across frontline professions, with healthcare, education, and emergency services workers experiencing particularly high rates of stress and emotional exhaustion. Passive artificial intelligence (AI) technologies may provide novel means to monitor and predict burnout risk using data collected continuously and non-invasively. Objective: This review aims to synthesize recent evidence on passive AI approaches for detecting stress and burnout among frontline workers, identify key physiological and behavioral biomarkers, and highlight current limitations in implementation, validation, and generalizability. Methods: A narrative review of peer-reviewed literature was conducted across multiple databases and digital libraries, including PubMed, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science. Eligible studies applied passive AI methods to infer stress or burnout in individuals in frontline roles. Only studies using passive data (e.g., wearables, Electronic Health Record (EHR) logs) and involving healthcare, education, emergency response, or retail workers were included. Studies focusing exclusively on self-reported or active measures were excluded. Results: Recent evidence indicates that biometric data (e.g., heart rate variability, skin conductance, sleep) from wearables are most frequently used and moderately predictive of stress, with reported accuracies often ranging from 75 to 95%. Workflow interaction logs (e.g., EHR usage patterns) and communication metrics (e.g., email timing and sentiment) show promise but remain underexplored. Organizational network analysis and ambient computing remain largely conceptual in nature. Few studies have examined cross-sector or long-term data, and limited work addresses the generalizability of demographic or cultural findings. Challenges persist in data standardization, privacy, ethical oversight, and integration with clinical or operational workflows. Conclusions: Passive AI systems offer significant promise for proactive burnout detection among frontline workers. However, current studies are limited by small sample sizes, short durations, and sector-specific focus. Future work should prioritize longitudinal, multi-sector validation, address inclusivity and bias, and establish ethical frameworks to support deployment in real-world settings. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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