ArticleJournal of the American Medical Informatics Association : JAMIA2024
Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Healthcare Professionals' Perceptions of Artificial Intelligence in Healthcare-A Systematic Review of Qualitative Studies.Journal of advanced nursing · 2026Pooled it
- Bridging language gaps in healthcare: a systematic review of the practical implementation of neural machine translation technologies in clinical settings.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Communication Barriers in Patient-Provider Interactions in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Struggling to integrate artificial intelligence in prehospital emergency care in a developing country: exploration of the Iranian experts' views based on qualitative content analysis.International journal of emergency medicine · 2026Article
- Artificial intelligence and person-centred practice: a critical reflection.NPJ digital medicine · 2026Review
- Beyond translation: a patient-centered research agenda for artificial intelligence interpreter services in healthcare.NPJ digital medicine · 2026Article
- Development of a language access index: a mixed methods analysis.Population health metrics · 2026Article
- Evaluating professional interpreting services for patients with limited English proficiency in secondary care settings: a scoping review.BMJ open quality · 2025Article
- Language matters: assessing the role of language barriers in patient-reported experiences of breast cancer patients in Germany.International journal for equity in health · 2025Article
- Hospital Food Service Experiences Between Older Patients From English- and Non-English Speaking Backgrounds in a Large Public Hospital in Australia: A Qualitative Analysis.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Bridging Barriers: Engaging Ethnic Minorities in Cardiovascular Research.Healthcare (Basel, Switzerland) · 2025Article
- Comparing remote and in-person interpretation experiences for clinicians and Spanish-speaking patients with limited English proficiency: a mixed methods study.BMJ open quality · 2025Article
- Using ChatGPT for writing hospital inpatient discharge summaries - perspectives from an inpatient infectious diseases service.BMC health services research · 2025Article
- Artificial intelligence in prehospital emergency care systems in low- and middle-income countries: cure or curiosity? Insights from a qualitative study.Frontiers in public health · 2025Article
- Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections.Applied clinical informatics · 2025Article
- Multilingual voice-enabled informatics tools: Catalyst for equitable AI in HIV and HIV-comorbidity healthcare management.PloS one · 2025Article
- Leveraging AI and data science to mitigate the respiratory health impacts of climate change in Africa: Organisation, costs, and challenges.Journal of global health · 2024Article
- Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students.Nursing reports (Pavia, Italy) · 2024Article
- Review
- Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesInpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. MATERIALS AND
methodsThis qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software.
resultsWe completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply-demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. DISCUSSION: This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers.
conclusionArtificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.
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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.