Designing an Artificial Intelligence Transcription Tool to Support Multimodal Serious Illness Communication: Institutional Experience and Best Practices.
Background: Communication during serious illness is often complex, emotionally charged, and cognitively demanding, particularly for patients and families with limited health literacy, sensory impairments, or language barriers. Existing communication supports inadequately address diverse information-...
| Publicado en: | Journal of Palliative Medicine Vol. 29; no. 8; pp. 1137 - 1145 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
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Mary Ann Liebert, Inc.
Aug2026
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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=195627586&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195627586 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10966218 C1U jtl: Journal of Palliative Medicine issn: 10966218 maglogo: N pubinfo: dt: Aug2026 vid: 29 iid: 8 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 195627586 195627586 195627586 10.1177/10966218261452780 195627586 ppf: 1137 ppct: 8 formats: tig: atl: Designing an Artificial Intelligence Transcription Tool to Support Multimodal Serious Illness Communication: Institutional Experience and Best Practices. aug: au: Murdaugh, Kimberly Kwok, Ian B. affil: Division of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, New York, USA. sug: subj: Artificial Intelligence Communication Ethics Palliative Care Practice Guidelines Technology Health Insurance Portability and Accountability Act Software Design Human Male Female Academic Medical Centers Health Services Accessibility Descriptive Statistics Leadership Privacy and Confidentiality Male Female ab: Background: Communication during serious illness is often complex, emotionally charged, and cognitively demanding, particularly for patients and families with limited health literacy, sensory impairments, or language barriers. Existing communication supports inadequately address diverse information-processing needs, underscoring the need for thoughtfully designed, technology-enabled interventions. Local Problem: Within inpatient palliative care settings, family meetings frequently convey high-stakes information without a durable, accessible record of the conversation for patients and caregivers. This limitation may exacerbate disparities in understanding, recall, and decision making, especially for individuals who benefit from visual or multimodal communication supports. Methods: We employed a multiphase, iterative development and rapid refinement process to design an artificial intelligence (AI)-enabled communication support tool that generates written transcripts of palliative care family meetings. Development was guided by ethical principles, user-centered design, and early engagement with institutional data security, privacy, and regulatory leadership. The interdisciplinary development team included palliative care physicians, gerontologists, and a physician with training in public health who led the computational development. Interventions: The intervention involved the development of a multimodal communication tool that produces AI-generated written transcripts of clinician-led family meetings, designed to supplement—not replace—verbal communication. Development occurred in collaboration with institutional IT, privacy, and security leadership to enable future integration into existing clinical workflows and governance structures, while supporting transparency, clinician oversight, and institutional compliance. Results: The development process demonstrated that generation of high-quality, AI-generated written transcripts of family meetings is feasible within inpatient palliative care settings and institutional data security architectures. Early co-development with clinical leadership and regulatory stakeholders streamlined implementation while maintaining ethical and institutional safeguards. Conclusions: In the context of the rapid evolution of AI technologies, clinically led, ethically grounded development of AI-generated written transcripts for serious illness communication, involving institutional IT, privacy and security leadership, clinicians, patients, and caregivers, is feasible and may enhance accessible, patient-centered communication. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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